Datasets:
Download docs/index.html from cy0307/ropedia-xperience-10m-task-suite-artifacts: direct link, hf CLI and curl.
- Browser
- Download file 481 kB
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https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/resolve/ee1bb718aae327bd84247384a03979cb8483bc82/docs/index.html
- Command line
-
hf download hf://datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts@ee1bb718aae327bd84247384a03979cb8483bc82/docs/index.html
-
curl -L -o index.html https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/resolve/ee1bb718aae327bd84247384a03979cb8483bc82/docs/index.html
481 kB
| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1"> | |
| <title>Ropedia Xperience-10M Task Suite</title> | |
| <meta name="description" content="This project builds public embodied-AI task and evaluation materials on the Xperience-10M dataset released by Ropedia, with one inspectable sample episode and a selected-128 comparison line."> | |
| <meta name="theme-color" content="#020502"> | |
| <meta name="robots" content="index, follow"> | |
| <link rel="canonical" href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/"> | |
| <link rel="icon" href="favicon.png" type="image/png" sizes="64x64"> | |
| <link rel="apple-touch-icon" href="apple-touch-icon.png"> | |
| <link rel="manifest" href="site.webmanifest"> | |
| <meta property="og:title" content="Ropedia Xperience-10M Task Suite"> | |
| <meta property="og:description" content="This project builds public embodied-AI task and evaluation materials on the Xperience-10M dataset released by Ropedia: one inspectable sample episode plus a selected-128 comparison line for baselines and model diagnostics."> | |
| <meta property="og:type" content="website"> | |
| <meta property="og:url" content="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/"> | |
| <meta property="og:image" content="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/brand/xperience10m-logo-social-card.png?v=xperience10m-logo-v4"> | |
| <meta property="og:image:width" content="1200"> | |
| <meta property="og:image:height" content="630"> | |
| <meta name="twitter:card" content="summary_large_image"> | |
| <meta name="twitter:title" content="Ropedia Xperience-10M Task Suite"> | |
| <meta name="twitter:description" content="This project builds public embodied-AI task materials for Xperience-10M: one sample episode, one selected-128 comparison line, and 180 scored method-task records."> | |
| <meta name="twitter:image" content="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/brand/xperience10m-logo-social-card.png?v=xperience10m-logo-v4"> | |
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| "description": "This project builds public embodied-AI task and evaluation materials on the Xperience-10M dataset released by Ropedia, including sample-episode inspection and selected-episode comparison.", | |
| "codeRepository": "https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite", | |
| "url": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/", | |
| "image": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/brand/xperience10m-logo-social-card.png", | |
| "programmingLanguage": ["Python", "HTML", "CSS", "JavaScript"], | |
| "isBasedOn": "https://ropedia.com/dataset", | |
| "license": "https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/LICENSE" | |
| } | |
| </script> | |
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| --line: rgba(204, 255, 160, 0.24); | |
| --soft-line: rgba(204, 255, 160, 0.14); | |
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| flex: 0 0 auto; | |
| display: block; | |
| border-radius: 10px; | |
| border: 1px solid rgba(204, 255, 160, 0.42); | |
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| .hero-actions { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 12px; | |
| margin-top: 34px; | |
| } | |
| .button { | |
| height: 46px; | |
| padding: 0 18px; | |
| border-radius: 999px; | |
| border: 1px solid rgba(255, 255, 255, 0.30); | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 10px; | |
| text-decoration: none; | |
| font-family: var(--font-btn); | |
| font-weight: 700; | |
| background: var(--ropedia-pill); | |
| transition: transform 240ms cubic-bezier(0.16, 1, 0.3, 1), border-color 240ms cubic-bezier(0.16, 1, 0.3, 1), background 240ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .button:hover { transform: translateY(-2px); border-color: var(--green); background: rgba(204, 255, 160, 0.10); } | |
| .button:active { transform: translateY(0) scale(0.98); } | |
| .button.primary { | |
| background: var(--ink); | |
| color: #020502; | |
| border-color: var(--green); | |
| background: var(--green); | |
| } | |
| .hero-paths { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 12px; | |
| margin-top: 24px; | |
| max-width: 940px; | |
| } | |
| .hero-path { | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.08), rgba(7, 18, 7, 0.78)), | |
| rgba(2, 5, 2, 0.52); | |
| padding: 15px; | |
| color: inherit; | |
| text-decoration: none; | |
| min-height: 136px; | |
| display: grid; | |
| align-content: start; | |
| gap: 10px; | |
| transition: transform 220ms cubic-bezier(0.16, 1, 0.3, 1), border-color 220ms cubic-bezier(0.16, 1, 0.3, 1), background 220ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .hero-path:hover { | |
| transform: translateY(-2px); | |
| border-color: rgba(204, 255, 160, 0.58); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.13), rgba(7, 18, 7, 0.82)), | |
| rgba(2, 5, 2, 0.56); | |
| } | |
| .hero-path small, | |
| .resource-mode small { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 760; | |
| letter-spacing: 0.07em; | |
| text-transform: uppercase; | |
| } | |
| .hero-path strong { | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 17px; | |
| line-height: 1.08; | |
| } | |
| .hero-path span { | |
| color: var(--muted); | |
| font-size: 12.5px; | |
| line-height: 1.42; | |
| } | |
| .hero-stats { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 10px; | |
| margin-top: 28px; | |
| max-width: 760px; | |
| } | |
| .stat { | |
| border: 1px solid var(--line); | |
| background: rgba(7, 18, 7, 0.82); | |
| padding: 14px 14px 13px; | |
| border-radius: var(--radius); | |
| } | |
| .stat strong { display: block; font-family: var(--font-mono); font-size: 21px; line-height: 1; font-variant-numeric: tabular-nums; } | |
| .stat span { display: block; margin-top: 7px; font-size: 12px; color: var(--muted); } | |
| .hero-panel { | |
| background: rgba(7, 18, 7, 0.88); | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| box-shadow: var(--shadow); | |
| padding: 18px; | |
| } | |
| .panel-top { | |
| display: flex; | |
| justify-content: space-between; | |
| align-items: center; | |
| border-bottom: 1px solid var(--soft-line); | |
| padding: 6px 6px 14px; | |
| color: var(--muted); | |
| font-size: 13px; | |
| font-family: var(--font-mono); | |
| } | |
| .signal { | |
| display: grid; | |
| grid-template-columns: 88px 1fr 74px; | |
| gap: 14px; | |
| align-items: center; | |
| padding: 15px 6px; | |
| border-bottom: 1px solid var(--soft-line); | |
| } | |
| .signal:last-child { border-bottom: 0; } | |
| .signal code { | |
| color: #e7f3df; | |
| background: rgba(204, 255, 160, 0.08); | |
| border: 1px solid var(--soft-line); | |
| padding: 5px 7px; | |
| border-radius: 5px; | |
| font-size: 12px; | |
| font-family: var(--font-mono); | |
| } | |
| .track { | |
| position: relative; | |
| height: 12px; | |
| background: rgba(204, 255, 160, 0.16); | |
| overflow: hidden; | |
| border-radius: 999px; | |
| } | |
| .track > span { | |
| position: absolute; | |
| inset: 0 auto 0 0; | |
| width: var(--w); | |
| background: var(--c); | |
| border-radius: inherit; | |
| } | |
| .signal strong { text-align: right; font-family: var(--font-mono); font-size: 13px; font-variant-numeric: tabular-nums; } | |
| main { display: flex; flex-direction: column; } | |
| main > section { | |
| padding: 88px 0 96px; | |
| border-bottom: 1px solid var(--soft-line); | |
| background: rgba(2, 5, 2, 0.72); | |
| scroll-margin-top: var(--tab-stack-offset); | |
| } | |
| main > section:focus { outline: none; } | |
| main.tabbed > section[hidden] { display: none; } | |
| .project-tabs-shell { | |
| display: none; | |
| order: 0; | |
| position: sticky; | |
| top: var(--nav-height); | |
| z-index: 14; | |
| border-bottom: 1px solid var(--soft-line); | |
| background: rgba(2, 5, 2, 0.94); | |
| backdrop-filter: blur(18px); | |
| padding: 10px 0 12px; | |
| } | |
| .project-tabs { | |
| display: grid; | |
| grid-template-columns: repeat(6, minmax(0, 1fr)); | |
| gap: 16px; | |
| } | |
| .project-tab { | |
| appearance: none; | |
| min-width: 0; | |
| min-height: 120px; | |
| border: 1px solid var(--soft-line); | |
| border-radius: 58px; | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.06), rgba(7, 18, 7, 0.82)), | |
| var(--surface); | |
| color: #dce8d6; | |
| cursor: pointer; | |
| padding: 24px 22px; | |
| text-align: left; | |
| font: inherit; | |
| transition: transform 220ms cubic-bezier(0.16, 1, 0.3, 1), border-color 220ms cubic-bezier(0.16, 1, 0.3, 1), background 220ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .project-tab:hover { transform: translateY(-1px); border-color: rgba(204, 255, 160, 0.42); } | |
| .project-tab strong { | |
| display: block; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: clamp(18px, 1.2vw, 25px); | |
| line-height: 1.06; | |
| } | |
| .project-tab span { | |
| display: block; | |
| margin-top: 8px; | |
| color: var(--muted); | |
| font-size: clamp(13px, 0.95vw, 19px); | |
| line-height: 1.18; | |
| max-width: 18ch; | |
| } | |
| .project-tab.active { | |
| border-color: var(--green); | |
| background: var(--green); | |
| color: #020502; | |
| box-shadow: 0 12px 34px rgba(204, 255, 160, 0.10); | |
| } | |
| .project-tab.active strong, | |
| .project-tab.active span { color: #020502; } | |
| .section-tabs { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 8px; | |
| overflow-x: visible; | |
| padding-top: 0; | |
| padding-bottom: 2px; | |
| scrollbar-width: thin; | |
| scrollbar-color: rgba(204, 255, 160, 0.42) rgba(7, 18, 7, 0.72); | |
| } | |
| .section-tab { | |
| appearance: none; | |
| display: inline-flex; | |
| align-items: center; | |
| justify-content: center; | |
| flex: 0 0 auto; | |
| min-height: 40px; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: 999px; | |
| background: rgba(7, 18, 7, 0.78); | |
| color: #c8d5c2; | |
| cursor: pointer; | |
| padding: 0 15px; | |
| font-family: var(--font-ui); | |
| font-size: clamp(12px, 0.82vw, 15px); | |
| font-weight: 660; | |
| line-height: 1.15; | |
| text-align: center; | |
| white-space: nowrap; | |
| transition: transform 220ms cubic-bezier(0.16, 1, 0.3, 1), border-color 220ms cubic-bezier(0.16, 1, 0.3, 1), background 220ms cubic-bezier(0.16, 1, 0.3, 1), color 220ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .section-tab:hover { | |
| transform: translateY(-1px); | |
| border-color: rgba(204, 255, 160, 0.42); | |
| color: var(--ink); | |
| } | |
| .section-tab.active { | |
| border-color: rgba(204, 255, 160, 0.82); | |
| background: rgba(204, 255, 160, 0.14); | |
| color: var(--green); | |
| } | |
| .section-orientation { | |
| display: flex; | |
| flex-wrap: wrap; | |
| align-items: center; | |
| justify-content: space-between; | |
| gap: 10px; | |
| margin-top: 7px; | |
| padding: 8px 0 0; | |
| border-top: 1px solid rgba(204, 255, 160, 0.12); | |
| color: #cbd8c5; | |
| font-size: 12px; | |
| line-height: 1.35; | |
| } | |
| .section-orientation strong { | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 13px; | |
| } | |
| .section-orientation span { | |
| color: var(--muted); | |
| } | |
| .section-orientation a { | |
| color: var(--cyan); | |
| text-decoration: none; | |
| font-weight: 760; | |
| } | |
| .section-orientation a:hover { | |
| color: var(--green); | |
| } | |
| .content-tabs { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 12px; | |
| overflow-x: visible; | |
| padding: 2px 0 6px; | |
| margin-bottom: 22px; | |
| scrollbar-width: thin; | |
| scrollbar-color: rgba(204, 255, 160, 0.42) rgba(7, 18, 7, 0.72); | |
| } | |
| .content-tab { | |
| appearance: none; | |
| flex: 1 1 220px; | |
| min-width: 210px; | |
| min-height: 66px; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: 999px; | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.05), rgba(7, 18, 7, 0.72)), | |
| rgba(2, 5, 2, 0.54); | |
| color: #dce8d6; | |
| cursor: pointer; | |
| padding: 12px 20px; | |
| text-align: left; | |
| font: inherit; | |
| transition: transform 220ms cubic-bezier(0.16, 1, 0.3, 1), border-color 220ms cubic-bezier(0.16, 1, 0.3, 1), background 220ms cubic-bezier(0.16, 1, 0.3, 1), color 220ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .content-tab:hover { | |
| transform: translateY(-1px); | |
| border-color: rgba(204, 255, 160, 0.42); | |
| color: var(--ink); | |
| } | |
| .content-tab strong { | |
| display: block; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 16px; | |
| line-height: 1.15; | |
| } | |
| .content-tab span { | |
| display: block; | |
| margin-top: 4px; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.25; | |
| } | |
| .content-tab.active { | |
| border-color: rgba(204, 255, 160, 0.82); | |
| background: rgba(204, 255, 160, 0.14); | |
| color: var(--green); | |
| } | |
| .content-tab.active strong, | |
| .content-tab.active span { color: var(--green); } | |
| .tabbed-panel[hidden] { display: none; } | |
| #overview { order: 1; } | |
| #roadmap { order: 2; } | |
| #reading-path { order: 3; } | |
| #dataset-card { order: 4; } | |
| #data-analysis { order: 5; } | |
| #raw-sample { order: 6; } | |
| #suite { order: 7; } | |
| #pipeline { order: 8; } | |
| #protocol { order: 9; } | |
| #takeaways { order: 10; } | |
| #directions { order: 12; } | |
| #architectures { order: 14; } | |
| #walkthroughs { order: 15; } | |
| #features { order: 17; } | |
| #evidence { order: 19; } | |
| #glossary { order: 20; } | |
| #artifacts { order: 21; } | |
| #omni-scale-up { order: 22; } | |
| #suite { padding: 62px 0 76px; } | |
| #suite .wrap { width: min(1680px, calc(100% - 48px)); } | |
| #suite .section-head { max-width: var(--max); margin-inline: auto; } | |
| .suite-jump-row, | |
| .result-jump-row, | |
| .direction-jump-row { | |
| max-width: var(--max); | |
| margin: -8px auto 24px; | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 10px; | |
| } | |
| .suite-jump-row a, | |
| .result-jump-row a, | |
| .direction-jump-row a { | |
| display: inline-flex; | |
| align-items: center; | |
| min-height: 38px; | |
| border: 1px solid rgba(204, 255, 160, 0.22); | |
| border-radius: 999px; | |
| padding: 0 14px; | |
| color: var(--accent-2); | |
| background: rgba(6, 14, 7, 0.74); | |
| font-size: 12px; | |
| font-weight: 800; | |
| letter-spacing: 0.04em; | |
| text-decoration: none; | |
| text-transform: uppercase; | |
| } | |
| .suite-jump-row a:hover, | |
| .result-jump-row a:hover, | |
| .direction-jump-row a:hover { | |
| border-color: rgba(204, 255, 160, 0.72); | |
| color: var(--ink); | |
| background: rgba(204, 255, 160, 0.10); | |
| } | |
| .result-subsection, | |
| .direction-subsection { | |
| scroll-margin-top: var(--tab-stack-offset); | |
| margin-top: clamp(28px, 4vw, 48px); | |
| border: 1px solid rgba(204, 255, 160, 0.20); | |
| border-radius: var(--radius); | |
| background: | |
| radial-gradient(circle at top right, rgba(103, 232, 209, 0.08), transparent 30%), | |
| linear-gradient(180deg, rgba(6, 14, 7, 0.72), rgba(2, 5, 2, 0.82)); | |
| padding: clamp(20px, 3vw, 32px); | |
| } | |
| .result-subsection-head, | |
| .direction-subsection-head { | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.9fr) minmax(260px, 0.58fr); | |
| gap: 22px; | |
| align-items: end; | |
| margin-bottom: 20px; | |
| } | |
| .result-subsection-head h3, | |
| .direction-subsection-head h3 { | |
| margin: 0; | |
| font-family: var(--font-ui); | |
| font-size: clamp(24px, 2.4vw, 34px); | |
| line-height: 1.05; | |
| letter-spacing: 0; | |
| text-wrap: balance; | |
| } | |
| .result-subsection-head p, | |
| .direction-subsection-head p { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 15px; | |
| line-height: 1.6; | |
| text-wrap: pretty; | |
| } | |
| .suite-radar-block { | |
| scroll-margin-top: var(--tab-stack-offset); | |
| } | |
| .suite-task-cards-block { | |
| scroll-margin-top: var(--tab-stack-offset); | |
| margin-top: clamp(28px, 4vw, 48px); | |
| border: 1px solid rgba(204, 255, 160, 0.22); | |
| border-radius: var(--radius); | |
| background: | |
| radial-gradient(circle at top left, rgba(204, 255, 160, 0.10), transparent 34%), | |
| linear-gradient(180deg, rgba(6, 14, 7, 0.86), rgba(2, 5, 2, 0.88)); | |
| padding: clamp(20px, 3vw, 32px); | |
| } | |
| .task-suite-subhead { | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.9fr) minmax(280px, 0.62fr); | |
| gap: 22px; | |
| align-items: end; | |
| margin-bottom: 20px; | |
| } | |
| .task-suite-subhead h3 { | |
| margin: 0; | |
| font-family: var(--font-ui); | |
| font-size: clamp(26px, 3vw, 40px); | |
| line-height: 1.04; | |
| letter-spacing: 0; | |
| text-wrap: balance; | |
| } | |
| .task-suite-subhead p { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 15px; | |
| line-height: 1.6; | |
| text-wrap: pretty; | |
| } | |
| .suite-task-index { | |
| scroll-margin-top: var(--tab-stack-offset); | |
| border: 1px solid rgba(204, 255, 160, 0.22); | |
| border-radius: var(--radius); | |
| background: | |
| radial-gradient(circle at 0% 0%, rgba(122, 229, 195, 0.10), transparent 34%), | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.07), rgba(2, 5, 2, 0.82)), | |
| rgba(7, 18, 7, 0.78); | |
| padding: clamp(18px, 2.6vw, 28px); | |
| margin: 20px 0 24px; | |
| } | |
| .suite-task-index-head { | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.72fr) minmax(280px, 0.62fr); | |
| gap: 20px; | |
| align-items: end; | |
| margin-bottom: 16px; | |
| padding-bottom: 15px; | |
| border-bottom: 1px solid rgba(204, 255, 160, 0.16); | |
| } | |
| .suite-task-index-head span { | |
| display: block; | |
| margin-bottom: 8px; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 820; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .suite-task-index-head h3 { | |
| margin: 0; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: clamp(24px, 3vw, 38px); | |
| line-height: 1.04; | |
| letter-spacing: 0; | |
| } | |
| .suite-task-index-head p { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 14px; | |
| line-height: 1.55; | |
| text-wrap: pretty; | |
| } | |
| .task-index-grid { | |
| display: grid; | |
| grid-template-columns: repeat(5, minmax(0, 1fr)); | |
| gap: 10px; | |
| } | |
| .task-index-grid a { | |
| min-width: 0; | |
| display: grid; | |
| grid-template-columns: 34px minmax(0, 1fr); | |
| grid-template-rows: auto auto; | |
| column-gap: 9px; | |
| row-gap: 2px; | |
| align-items: center; | |
| border: 1px solid rgba(204, 255, 160, 0.15); | |
| border-radius: 8px; | |
| background: rgba(2, 5, 2, 0.44); | |
| color: var(--ink); | |
| padding: 9px; | |
| text-decoration: none; | |
| transition: transform 200ms var(--motion-ease), border-color 200ms var(--motion-ease), background 200ms var(--motion-ease); | |
| } | |
| .task-index-grid a:hover { | |
| transform: translateY(-1px); | |
| border-color: rgba(204, 255, 160, 0.48); | |
| background: rgba(204, 255, 160, 0.08); | |
| } | |
| .task-index-grid img { | |
| grid-row: 1 / span 2; | |
| width: 34px; | |
| height: 34px; | |
| border: 1px solid rgba(204, 255, 160, 0.20); | |
| border-radius: 8px; | |
| background: rgba(2, 5, 2, 0.78); | |
| object-fit: contain; | |
| } | |
| .task-index-grid strong { | |
| min-width: 0; | |
| color: #f6ffef; | |
| font-family: var(--font-ui); | |
| font-size: 12.5px; | |
| line-height: 1.14; | |
| } | |
| .task-index-grid span { | |
| min-width: 0; | |
| color: var(--muted); | |
| font-size: 11px; | |
| line-height: 1.2; | |
| } | |
| .task-map-details { | |
| scroll-margin-top: var(--tab-stack-offset); | |
| margin: 22px 0 24px; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: var(--radius); | |
| background: rgba(2, 5, 2, 0.52); | |
| overflow: hidden; | |
| } | |
| .task-map-details summary { | |
| list-style: none; | |
| cursor: pointer; | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.54fr) minmax(280px, 1fr); | |
| gap: 16px; | |
| align-items: center; | |
| padding: 16px 18px; | |
| border-bottom: 1px solid rgba(204, 255, 160, 0.12); | |
| } | |
| .task-map-details summary::-webkit-details-marker { | |
| display: none; | |
| } | |
| .task-map-details summary span { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 820; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .task-map-details summary strong { | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.4; | |
| } | |
| .task-map-details[open] summary { | |
| background: rgba(204, 255, 160, 0.06); | |
| } | |
| .figure-pan#task-suite-map { | |
| margin: 22px 0 24px; | |
| overflow: visible; | |
| padding-bottom: 0; | |
| } | |
| .figure-pan#task-suite-map .task-map-details { | |
| margin: 0; | |
| } | |
| .task-map-figure-frame { | |
| overflow-x: auto; | |
| padding: 0 16px 16px; | |
| } | |
| .task-map-figure-frame .task-suite-image { | |
| margin-top: 16px; | |
| } | |
| .section-head { | |
| display: flex; | |
| justify-content: space-between; | |
| align-items: end; | |
| gap: 32px; | |
| margin-bottom: 28px; | |
| } | |
| h2 { | |
| margin: 0; | |
| font-family: var(--font-ui); | |
| font-size: clamp(30px, 4vw, 48px); | |
| line-height: 1.05; | |
| letter-spacing: 0; | |
| text-wrap: balance; | |
| } | |
| .section-head p { | |
| max-width: 560px; | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 16px; | |
| line-height: 1.65; | |
| text-wrap: pretty; | |
| } | |
| .pipeline-image, | |
| .architecture-image, | |
| .task-suite-image, | |
| .chart { | |
| display: block; | |
| width: 100%; | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| background: var(--surface); | |
| box-shadow: 0 18px 46px rgba(0, 0, 0, 0.36); | |
| } | |
| .radar-chart { | |
| background: #020502; | |
| object-fit: contain; | |
| } | |
| .unified-radar-chart { | |
| height: auto; | |
| min-height: 0; | |
| padding: 10px; | |
| } | |
| #suite .split-radar-card img { | |
| min-height: 0; | |
| } | |
| .lora-pipeline-image { | |
| display: block; | |
| margin-top: 22px; | |
| } | |
| .figure-brief { | |
| margin: 24px 0 0; | |
| display: grid; | |
| grid-template-columns: minmax(0, 1.1fr) minmax(260px, 0.9fr); | |
| gap: 20px; | |
| align-items: stretch; | |
| } | |
| .figure-brief-card { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| background: linear-gradient(180deg, rgba(204, 255, 160, 0.08), rgba(6, 14, 7, 0.76)); | |
| padding: 22px; | |
| box-shadow: 0 14px 36px rgba(0, 0, 0, 0.24); | |
| } | |
| .figure-brief-card h3 { | |
| margin: 0 0 10px; | |
| font-family: var(--font-ui); | |
| font-size: 21px; | |
| line-height: 1.18; | |
| letter-spacing: 0; | |
| } | |
| .figure-brief-card p { | |
| margin: 0; | |
| color: var(--muted); | |
| line-height: 1.6; | |
| font-size: 14px; | |
| } | |
| .method-flow-panel { | |
| margin: 24px 0 26px; | |
| padding: 20px; | |
| } | |
| .method-flow-steps { | |
| display: grid; | |
| grid-template-columns: repeat(5, minmax(0, 1fr)); | |
| gap: 10px; | |
| counter-reset: methodFlow; | |
| } | |
| .method-flow-steps article { | |
| position: relative; | |
| min-width: 0; | |
| border: 1px solid rgba(204, 255, 160, 0.14); | |
| border-radius: var(--radius); | |
| background: rgba(2, 5, 2, 0.46); | |
| padding: 15px; | |
| } | |
| .method-flow-steps article::before { | |
| counter-increment: methodFlow; | |
| content: "0" counter(methodFlow); | |
| display: inline-flex; | |
| margin-bottom: 10px; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 850; | |
| letter-spacing: 0.08em; | |
| } | |
| .method-flow-steps strong { | |
| display: block; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 16px; | |
| line-height: 1.14; | |
| } | |
| .method-flow-steps p { | |
| margin: 8px 0 0; | |
| color: var(--muted); | |
| font-size: 12.5px; | |
| line-height: 1.45; | |
| } | |
| .split-radar-grid { | |
| display: grid; | |
| grid-template-columns: minmax(0, 1fr); | |
| gap: 24px; | |
| margin: 26px 0 34px; | |
| } | |
| .split-radar-details { | |
| margin: 18px 0 28px; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: var(--radius); | |
| background: rgba(2, 5, 2, 0.46); | |
| overflow: hidden; | |
| } | |
| .split-radar-details summary { | |
| list-style: none; | |
| cursor: pointer; | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.38fr) minmax(280px, 1fr); | |
| gap: 16px; | |
| align-items: center; | |
| padding: 16px 18px; | |
| } | |
| .split-radar-details summary::-webkit-details-marker { | |
| display: none; | |
| } | |
| .split-radar-details summary span { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 820; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .split-radar-details summary strong { | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.4; | |
| } | |
| .split-radar-details[open] summary { | |
| border-bottom: 1px solid rgba(204, 255, 160, 0.12); | |
| background: rgba(204, 255, 160, 0.06); | |
| } | |
| .split-radar-details .split-radar-grid { | |
| margin: 0; | |
| padding: 18px; | |
| } | |
| .full-radar-frame { | |
| display: grid; | |
| gap: 12px; | |
| padding: 18px; | |
| } | |
| .split-radar-card { | |
| min-width: 0; | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.06), rgba(6, 14, 7, 0.82)), | |
| var(--surface); | |
| padding: 20px; | |
| } | |
| .split-radar-card h3 { | |
| margin: 0; | |
| font-family: var(--font-ui); | |
| font-size: 18px; | |
| line-height: 1.18; | |
| letter-spacing: 0; | |
| } | |
| .split-radar-card p { | |
| margin: 8px 0 14px; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.5; | |
| } | |
| .split-radar-card img { | |
| display: block; | |
| width: 100%; | |
| height: auto; | |
| min-height: 0; | |
| object-fit: contain; | |
| border: 1px solid rgba(204, 255, 160, 0.14); | |
| border-radius: 6px; | |
| background: #020502; | |
| } | |
| .foundation-pipeline-grid { | |
| display: grid; | |
| grid-template-columns: 1fr; | |
| gap: 18px; | |
| margin: 0 0 28px; | |
| } | |
| .foundation-pipeline-card { | |
| min-width: 0; | |
| display: grid; | |
| grid-template-columns: minmax(0, 1.45fr) minmax(320px, 0.55fr); | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| overflow: hidden; | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.06), rgba(6, 14, 7, 0.86)), | |
| var(--surface); | |
| box-shadow: 0 18px 46px rgba(0, 0, 0, 0.3); | |
| } | |
| .foundation-pipeline-card img { | |
| display: block; | |
| width: 100%; | |
| height: 100%; | |
| min-height: 320px; | |
| aspect-ratio: 16 / 9; | |
| object-fit: contain; | |
| border-right: 1px solid var(--line); | |
| background: #020502; | |
| } | |
| .foundation-pipeline-body { | |
| padding: 18px; | |
| } | |
| .foundation-pipeline-body span { | |
| display: inline-flex; | |
| margin-bottom: 10px; | |
| color: var(--accent); | |
| font-family: var(--font-ui); | |
| font-size: 11px; | |
| font-weight: 800; | |
| letter-spacing: 0.12em; | |
| text-transform: uppercase; | |
| } | |
| .foundation-pipeline-body h3 { | |
| margin: 0 0 9px; | |
| font-family: var(--font-ui); | |
| font-size: 20px; | |
| line-height: 1.16; | |
| letter-spacing: 0; | |
| } | |
| .foundation-pipeline-body p { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.55; | |
| } | |
| .foundation-io-panel { | |
| display: grid; | |
| gap: 10px; | |
| margin-top: 14px; | |
| padding-top: 14px; | |
| border-top: 1px solid var(--soft-line); | |
| } | |
| .foundation-io-row { | |
| border: 1px solid rgba(204, 255, 160, 0.13); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.34); | |
| padding: 10px 11px; | |
| } | |
| .foundation-io-row strong { | |
| display: block; | |
| margin-bottom: 5px; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 12px; | |
| font-weight: 850; | |
| line-height: 1.2; | |
| } | |
| .foundation-io-row p { | |
| color: var(--muted); | |
| font-size: 12.5px; | |
| line-height: 1.45; | |
| } | |
| .foundation-io-row code { | |
| color: var(--accent-2); | |
| font-size: 0.95em; | |
| } | |
| .foundation-io-tasks { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 6px; | |
| } | |
| .foundation-io-tasks a { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| color: var(--accent-2); | |
| font-size: 11px; | |
| font-weight: 800; | |
| line-height: 1; | |
| padding: 6px 7px; | |
| text-decoration: none; | |
| background: rgba(2, 5, 2, 0.52); | |
| } | |
| .foundation-io-tasks a:hover { | |
| border-color: var(--green); | |
| color: var(--ink); | |
| } | |
| .foundation-pipeline-links { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 10px; | |
| margin-top: 14px; | |
| } | |
| .foundation-pipeline-links a { | |
| color: var(--accent-2); | |
| font-size: 12px; | |
| font-weight: 800; | |
| text-decoration: none; | |
| text-transform: uppercase; | |
| letter-spacing: 0.06em; | |
| } | |
| .split-radar-links { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 8px; | |
| margin-top: 12px; | |
| } | |
| .split-radar-links a { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| color: var(--cyan); | |
| font-size: 12px; | |
| font-weight: 700; | |
| padding: 7px 8px; | |
| text-decoration: none; | |
| background: rgba(2, 5, 2, 0.42); | |
| } | |
| .split-radar-links a:hover { border-color: var(--green); color: var(--ink); } | |
| .suite-lines { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 14px; | |
| margin: 24px 0; | |
| } | |
| .suite-line-card { | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.08), rgba(6, 14, 7, 0.76)), | |
| var(--surface); | |
| padding: 18px; | |
| min-width: 0; | |
| } | |
| .suite-line-card small { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 760; | |
| letter-spacing: 0.07em; | |
| text-transform: uppercase; | |
| } | |
| .suite-line-card h3 { | |
| margin: 8px 0; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 21px; | |
| line-height: 1.12; | |
| letter-spacing: 0; | |
| } | |
| .suite-line-card p { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.52; | |
| } | |
| .line-readout { | |
| display: grid; | |
| gap: 8px; | |
| margin: 14px 0 0; | |
| padding: 13px; | |
| border: 1px solid rgba(204, 255, 160, 0.14); | |
| border-radius: var(--radius); | |
| background: rgba(2, 5, 2, 0.38); | |
| } | |
| .line-readout span { | |
| display: block; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 800; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .line-readout p { | |
| margin: 2px 0 0; | |
| color: #d7e2d0; | |
| font-size: 12.5px; | |
| line-height: 1.42; | |
| } | |
| .suite-line-facts { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 8px; | |
| margin-top: 14px; | |
| } | |
| .suite-line-facts span { | |
| border: 1px solid rgba(204, 255, 160, 0.13); | |
| border-radius: 8px; | |
| background: rgba(2, 5, 2, 0.46); | |
| padding: 9px; | |
| color: rgba(245, 247, 240, 0.74); | |
| font-size: 11.5px; | |
| line-height: 1.28; | |
| } | |
| .suite-line-facts strong { | |
| display: block; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 14px; | |
| line-height: 1; | |
| margin-bottom: 5px; | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .suite-line-card a { | |
| display: inline-flex; | |
| margin-top: 14px; | |
| color: var(--cyan); | |
| font-weight: 760; | |
| text-decoration: none; | |
| } | |
| .suite-line-card a:hover { color: var(--green); } | |
| .line-map-figure { | |
| margin: 28px 0; | |
| border: 1px solid rgba(204, 255, 160, 0.20); | |
| border-radius: var(--radius); | |
| background: rgba(2, 5, 2, 0.78); | |
| overflow: hidden; | |
| box-shadow: 0 28px 80px rgba(0, 0, 0, 0.28); | |
| } | |
| .line-map-figure img { | |
| display: block; | |
| width: 100%; | |
| height: auto; | |
| background: #020502; | |
| } | |
| .about-identity { | |
| display: grid; | |
| grid-template-columns: 92px minmax(0, 1fr); | |
| gap: 20px; | |
| align-items: center; | |
| margin: 24px 0 30px; | |
| padding: 20px; | |
| border: 1px solid rgba(204, 255, 160, 0.24); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(135deg, rgba(204, 255, 160, 0.11), rgba(7, 18, 7, 0.68) 42%), | |
| rgba(2, 5, 2, 0.84); | |
| box-shadow: 0 18px 46px rgba(0, 0, 0, 0.28); | |
| } | |
| .about-identity img { | |
| width: 92px; | |
| height: 92px; | |
| display: block; | |
| border: 1px solid rgba(204, 255, 160, 0.42); | |
| border-radius: var(--radius); | |
| background: #061006; | |
| object-fit: contain; | |
| box-shadow: 0 0 22px rgba(122, 229, 195, 0.16); | |
| } | |
| .about-identity span { | |
| display: block; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 800; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .about-identity strong { | |
| display: block; | |
| margin-top: 6px; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: clamp(22px, 2.2vw, 34px); | |
| line-height: 1.08; | |
| } | |
| .about-identity p { | |
| max-width: 980px; | |
| margin: 10px 0 0; | |
| color: rgba(245, 247, 240, 0.76); | |
| font-size: 15px; | |
| line-height: 1.58; | |
| } | |
| .about-identity-links { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 8px; | |
| margin-top: 14px; | |
| } | |
| .about-identity-links a { | |
| min-height: 34px; | |
| display: inline-flex; | |
| align-items: center; | |
| justify-content: center; | |
| padding: 0 12px; | |
| border: 1px solid rgba(204, 255, 160, 0.20); | |
| border-radius: 999px; | |
| background: rgba(204, 255, 160, 0.07); | |
| color: #dfeadc; | |
| font-family: var(--font-btn); | |
| font-size: 12px; | |
| font-weight: 760; | |
| text-decoration: none; | |
| } | |
| .about-identity-links a:hover { | |
| color: var(--green); | |
| border-color: rgba(204, 255, 160, 0.44); | |
| background: rgba(204, 255, 160, 0.12); | |
| } | |
| .reader-route-panel, | |
| .task-axis-panel, | |
| .method-flow-panel { | |
| scroll-margin-top: calc(var(--nav-height) + 20px); | |
| border: 1px solid rgba(204, 255, 160, 0.20); | |
| border-radius: var(--radius); | |
| background: | |
| radial-gradient(circle at 16% 0%, rgba(204, 255, 160, 0.12), transparent 32%), | |
| linear-gradient(180deg, rgba(7, 18, 7, 0.78), rgba(2, 5, 2, 0.72)); | |
| box-shadow: 0 18px 50px rgba(0, 0, 0, 0.26); | |
| } | |
| .reader-route-panel { | |
| display: grid; | |
| grid-template-columns: minmax(260px, 0.62fr) minmax(0, 1fr); | |
| gap: 18px; | |
| align-items: stretch; | |
| margin: 12px 0 26px; | |
| padding: 18px; | |
| } | |
| .reader-route-copy { | |
| padding: 6px 4px; | |
| } | |
| .reader-route-copy small, | |
| .task-axis-head small, | |
| .method-flow-head small, | |
| .citation-ledger caption { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 820; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .reader-route-copy strong, | |
| .task-axis-head strong, | |
| .method-flow-head strong { | |
| display: block; | |
| margin-top: 8px; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: clamp(22px, 2.4vw, 34px); | |
| line-height: 1.05; | |
| letter-spacing: 0; | |
| text-wrap: balance; | |
| } | |
| .reader-route-copy p, | |
| .task-axis-head p, | |
| .method-flow-head p { | |
| margin: 10px 0 0; | |
| color: var(--muted); | |
| font-size: 14px; | |
| line-height: 1.58; | |
| } | |
| .reader-route-list { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 10px; | |
| } | |
| .reader-route-list a { | |
| min-width: 0; | |
| display: grid; | |
| gap: 8px; | |
| align-content: start; | |
| border: 1px solid rgba(204, 255, 160, 0.15); | |
| border-radius: var(--radius); | |
| background: rgba(2, 5, 2, 0.50); | |
| color: var(--muted); | |
| padding: 14px; | |
| text-decoration: none; | |
| } | |
| .reader-route-list a:hover { | |
| border-color: rgba(204, 255, 160, 0.45); | |
| background: rgba(204, 255, 160, 0.08); | |
| color: var(--ink); | |
| } | |
| .reader-route-list span { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 820; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .reader-route-list strong { | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 16px; | |
| line-height: 1.14; | |
| } | |
| .reader-route-list em { | |
| color: var(--muted); | |
| font-style: normal; | |
| font-size: 12px; | |
| line-height: 1.45; | |
| } | |
| .task-axis-panel { | |
| margin: 24px 0 28px; | |
| padding: 20px; | |
| } | |
| .task-axis-head, | |
| .method-flow-head { | |
| display: grid; | |
| grid-template-columns: minmax(260px, 0.58fr) minmax(0, 1fr); | |
| gap: 22px; | |
| align-items: end; | |
| margin-bottom: 18px; | |
| } | |
| .task-axis-grid { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 12px; | |
| } | |
| .task-axis-visual { | |
| position: relative; | |
| margin: 22px 0 18px; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: var(--radius); | |
| overflow: hidden; | |
| background: #020502; | |
| box-shadow: inset 0 0 0 1px rgba(255, 255, 255, 0.02); | |
| } | |
| .task-axis-visual img { | |
| display: block; | |
| width: 100%; | |
| height: auto; | |
| min-height: 320px; | |
| object-fit: cover; | |
| background: #020502; | |
| } | |
| .task-axis-visual figcaption { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 8px; | |
| padding: 12px; | |
| border-top: 1px solid rgba(204, 255, 160, 0.16); | |
| background: rgba(2, 5, 2, 0.92); | |
| } | |
| .task-axis-visual figcaption span { | |
| border: 1px solid rgba(204, 255, 160, 0.22); | |
| border-radius: 8px; | |
| background: rgba(204, 255, 160, 0.055); | |
| backdrop-filter: blur(10px); | |
| color: rgba(245, 247, 240, 0.84); | |
| font-family: var(--font-ui); | |
| font-size: 12px; | |
| font-weight: 760; | |
| line-height: 1.2; | |
| padding: 10px 11px; | |
| } | |
| .task-axis-visual figcaption strong { | |
| display: block; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 850; | |
| letter-spacing: 0.08em; | |
| margin-bottom: 4px; | |
| text-transform: uppercase; | |
| } | |
| .task-axis-summary { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 12px; | |
| margin: 0 0 16px; | |
| } | |
| .task-axis-summary-card { | |
| min-width: 0; | |
| display: grid; | |
| grid-template-columns: auto 1fr; | |
| gap: 13px; | |
| align-items: start; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: var(--radius); | |
| background: | |
| radial-gradient(circle at 12% 8%, rgba(204, 255, 160, 0.12), transparent 34%), | |
| rgba(2, 5, 2, 0.58); | |
| padding: 14px; | |
| } | |
| .task-axis-count { | |
| width: 54px; | |
| height: 54px; | |
| display: inline-flex; | |
| align-items: center; | |
| justify-content: center; | |
| border: 1px solid rgba(204, 255, 160, 0.28); | |
| border-radius: 12px; | |
| background: rgba(204, 255, 160, 0.10); | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 22px; | |
| font-weight: 900; | |
| line-height: 1; | |
| box-shadow: inset 0 0 0 1px rgba(255, 255, 255, 0.025); | |
| } | |
| .task-axis-summary-card small { | |
| display: block; | |
| color: var(--cyan); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 850; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .task-axis-summary-card strong { | |
| display: block; | |
| margin-top: 5px; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 18px; | |
| line-height: 1.1; | |
| } | |
| .task-axis-summary-card p { | |
| margin: 7px 0 0; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.48; | |
| } | |
| .task-axis-reader-rule { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 12px; | |
| margin: 0 0 16px; | |
| } | |
| .task-axis-reader-rule article { | |
| min-width: 0; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: 10px; | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.075), rgba(2, 5, 2, 0.62)), | |
| rgba(2, 5, 2, 0.56); | |
| padding: 14px; | |
| } | |
| .task-axis-reader-rule span { | |
| color: var(--cyan); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 850; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .task-axis-reader-rule strong { | |
| display: block; | |
| margin-top: 6px; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 17px; | |
| line-height: 1.12; | |
| } | |
| .task-axis-reader-rule p { | |
| margin: 7px 0 0; | |
| color: var(--muted); | |
| font-size: 12.5px; | |
| line-height: 1.45; | |
| } | |
| .task-axis-key { | |
| display: grid; | |
| grid-template-columns: minmax(0, 1.35fr) repeat(3, minmax(0, 0.82fr)); | |
| gap: 12px; | |
| margin: 0 0 16px; | |
| } | |
| .task-axis-key-card { | |
| min-width: 0; | |
| border: 1px solid rgba(204, 255, 160, 0.16); | |
| border-radius: var(--radius); | |
| background: rgba(2, 5, 2, 0.52); | |
| padding: 14px; | |
| } | |
| .task-axis-key-card > span { | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 8px; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 850; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .task-axis-key-card > span::before { | |
| content: ""; | |
| width: 8px; | |
| height: 8px; | |
| border-radius: 999px; | |
| background: var(--green); | |
| box-shadow: 0 0 18px rgba(188, 255, 132, 0.42); | |
| } | |
| .task-axis-key-card strong { | |
| display: block; | |
| margin-top: 8px; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 18px; | |
| line-height: 1.08; | |
| letter-spacing: 0; | |
| } | |
| .task-axis-key-card p { | |
| margin: 7px 0 0; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.45; | |
| } | |
| .task-axis-mini-list { | |
| display: grid; | |
| gap: 7px; | |
| margin: 12px 0 0; | |
| padding: 0; | |
| list-style: none; | |
| } | |
| .task-axis-mini-list li { | |
| display: grid; | |
| grid-template-columns: auto 1fr; | |
| gap: 8px; | |
| align-items: start; | |
| min-width: 0; | |
| color: rgba(245, 247, 240, 0.84); | |
| font-family: var(--font-ui); | |
| font-size: 12px; | |
| font-weight: 720; | |
| line-height: 1.25; | |
| } | |
| .task-axis-mini-list b { | |
| min-width: 22px; | |
| color: var(--cyan); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 860; | |
| letter-spacing: 0.05em; | |
| } | |
| .task-axis-task-list { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 6px; | |
| margin: 12px 0 0; | |
| padding: 0; | |
| list-style: none; | |
| } | |
| .task-axis-task-list li { | |
| display: grid; | |
| grid-template-columns: auto 1fr; | |
| gap: 7px; | |
| min-width: 0; | |
| align-items: center; | |
| border: 1px solid rgba(204, 255, 160, 0.11); | |
| border-radius: 7px; | |
| background: rgba(204, 255, 160, 0.045); | |
| color: rgba(245, 247, 240, 0.82); | |
| font-family: var(--font-ui); | |
| font-size: 11px; | |
| font-weight: 700; | |
| line-height: 1.15; | |
| padding: 6px 7px; | |
| } | |
| .task-axis-task-list b { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 9px; | |
| font-weight: 850; | |
| letter-spacing: 0.04em; | |
| } | |
| .task-axis-card { | |
| border: 1px solid rgba(204, 255, 160, 0.15); | |
| border-radius: var(--radius); | |
| background: rgba(2, 5, 2, 0.46); | |
| padding: 16px; | |
| min-width: 0; | |
| } | |
| .task-axis-card span { | |
| color: var(--cyan); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 820; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .task-axis-card strong { | |
| display: block; | |
| margin-top: 8px; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 19px; | |
| line-height: 1.12; | |
| } | |
| .task-axis-card p { | |
| margin: 9px 0 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.5; | |
| } | |
| .task-axis-flow { | |
| display: grid; | |
| grid-template-columns: 1fr auto 1fr auto 1fr auto 1fr; | |
| align-items: center; | |
| gap: 10px; | |
| margin-top: 14px; | |
| color: rgba(245, 247, 240, 0.82); | |
| font-size: 12px; | |
| line-height: 1.45; | |
| } | |
| .task-axis-flow strong { | |
| border: 1px solid rgba(204, 255, 160, 0.14); | |
| border-radius: 8px; | |
| background: rgba(204, 255, 160, 0.06); | |
| padding: 10px 12px; | |
| font-family: var(--font-ui); | |
| } | |
| .task-axis-flow span { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-weight: 850; | |
| } | |
| .result-reading-order { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 12px; | |
| margin: 24px 0 28px; | |
| } | |
| .result-reading-step { | |
| border: 1px solid rgba(204, 255, 160, 0.16); | |
| border-radius: var(--radius); | |
| background: rgba(7, 18, 7, 0.72); | |
| padding: 16px; | |
| } | |
| .result-reading-step span { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 800; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .result-reading-step strong { | |
| display: block; | |
| margin-top: 8px; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 17px; | |
| line-height: 1.1; | |
| } | |
| .result-reading-step p { | |
| margin: 9px 0 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.45; | |
| } | |
| .result-rules-panel { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 12px; | |
| margin: 22px 0 22px; | |
| } | |
| .result-rules-panel article { | |
| border: 1px solid rgba(204, 255, 160, 0.20); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.08), rgba(7, 18, 7, 0.72)), | |
| rgba(2, 5, 2, 0.70); | |
| padding: 16px; | |
| min-width: 0; | |
| } | |
| .result-rules-panel small { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 820; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .result-rules-panel strong { | |
| display: block; | |
| margin-top: 7px; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 18px; | |
| line-height: 1.12; | |
| } | |
| .result-rules-panel p { | |
| margin: 8px 0 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.48; | |
| } | |
| .result-citation-banner { | |
| display: grid; | |
| grid-template-columns: auto minmax(0, 1fr); | |
| gap: 14px; | |
| align-items: start; | |
| margin: 20px 0 18px; | |
| border: 1px solid rgba(155, 184, 255, 0.28); | |
| border-radius: var(--radius); | |
| background: | |
| radial-gradient(circle at 8% 0%, rgba(155, 184, 255, 0.16), transparent 36%), | |
| linear-gradient(180deg, rgba(155, 184, 255, 0.08), rgba(2, 5, 2, 0.74)); | |
| padding: 16px; | |
| } | |
| .result-citation-banner span { | |
| width: 42px; | |
| height: 42px; | |
| display: inline-flex; | |
| align-items: center; | |
| justify-content: center; | |
| border: 1px solid rgba(155, 184, 255, 0.34); | |
| border-radius: 10px; | |
| background: rgba(155, 184, 255, 0.10); | |
| color: #c9d7ff; | |
| font-family: var(--font-mono); | |
| font-size: 16px; | |
| font-weight: 900; | |
| } | |
| .result-citation-banner strong { | |
| display: block; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 18px; | |
| line-height: 1.15; | |
| } | |
| .result-citation-banner p { | |
| margin: 6px 0 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.5; | |
| } | |
| .citation-ledger { | |
| margin-top: 8px; | |
| } | |
| .citation-ledger caption { | |
| caption-side: top; | |
| text-align: left; | |
| padding: 0 0 10px; | |
| } | |
| .line-table { | |
| width: 100%; | |
| border-collapse: collapse; | |
| margin: 18px 0 28px; | |
| border: 1px solid var(--soft-line); | |
| border-radius: var(--radius); | |
| overflow: hidden; | |
| background: rgba(7, 18, 7, 0.54); | |
| } | |
| .line-table th, | |
| .line-table td { | |
| border-bottom: 1px solid var(--soft-line); | |
| padding: 13px 14px; | |
| text-align: left; | |
| vertical-align: top; | |
| font-size: 13px; | |
| line-height: 1.5; | |
| } | |
| .line-table th { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 760; | |
| letter-spacing: 0.07em; | |
| text-transform: uppercase; | |
| background: rgba(204, 255, 160, 0.07); | |
| } | |
| .line-table tr:last-child td { border-bottom: 0; } | |
| .line-table td:first-child { | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-weight: 760; | |
| width: 18%; | |
| } | |
| .qwen-lineage-table td:first-child { | |
| width: 8%; | |
| min-width: 58px; | |
| } | |
| .qwen-lineage-table th:nth-child(2), | |
| .qwen-lineage-table td:nth-child(2), | |
| .qwen-lineage-table th:nth-child(3), | |
| .qwen-lineage-table td:nth-child(3) { | |
| width: 28%; | |
| } | |
| .line-table a { | |
| color: var(--cyan); | |
| font-weight: 760; | |
| text-decoration: none; | |
| } | |
| .line-table a:hover { color: var(--green); } | |
| .line-table td[data-label]::before { | |
| display: none; | |
| } | |
| .table-note { | |
| margin: -14px 0 28px; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.45; | |
| max-width: 900px; | |
| } | |
| .result-matrix-panel { | |
| margin: 30px 0 34px; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.07), rgba(204, 255, 160, 0.025)), | |
| rgba(4, 10, 5, 0.88); | |
| overflow: hidden; | |
| box-shadow: 0 24px 70px rgba(0, 0, 0, 0.28); | |
| } | |
| .result-matrix-head { | |
| display: grid; | |
| grid-template-columns: minmax(0, 1fr) auto; | |
| gap: 24px; | |
| align-items: end; | |
| padding: 24px; | |
| border-bottom: 1px solid rgba(204, 255, 160, 0.14); | |
| } | |
| .result-matrix-head span { | |
| display: block; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 820; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .result-matrix-head h3 { | |
| margin: 7px 0 0; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: clamp(24px, 2.6vw, 40px); | |
| line-height: 1.05; | |
| } | |
| .result-matrix-head p { | |
| max-width: 920px; | |
| margin: 10px 0 0; | |
| color: rgba(245, 247, 240, 0.74); | |
| font-size: 14px; | |
| line-height: 1.55; | |
| } | |
| .result-matrix-actions { | |
| display: flex; | |
| flex-wrap: wrap; | |
| justify-content: flex-end; | |
| gap: 8px; | |
| } | |
| .result-matrix-actions a { | |
| min-height: 34px; | |
| display: inline-flex; | |
| align-items: center; | |
| justify-content: center; | |
| padding: 0 12px; | |
| border: 1px solid rgba(204, 255, 160, 0.24); | |
| border-radius: 999px; | |
| color: #dfeadc; | |
| font-family: var(--font-btn); | |
| font-size: 12px; | |
| font-weight: 780; | |
| text-decoration: none; | |
| background: rgba(204, 255, 160, 0.06); | |
| } | |
| .result-matrix-actions a:hover { | |
| color: #020502; | |
| border-color: var(--green); | |
| background: var(--green); | |
| } | |
| .result-matrix-stats { | |
| display: grid; | |
| grid-template-columns: repeat(5, minmax(0, 1fr)); | |
| gap: 1px; | |
| border-bottom: 1px solid rgba(204, 255, 160, 0.14); | |
| background: rgba(204, 255, 160, 0.10); | |
| } | |
| .result-matrix-stat { | |
| min-height: 88px; | |
| padding: 16px 18px; | |
| background: rgba(4, 10, 5, 0.92); | |
| } | |
| .result-matrix-stat strong { | |
| display: block; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 28px; | |
| line-height: 1; | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .result-matrix-stat span { | |
| display: block; | |
| margin-top: 8px; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.3; | |
| } | |
| .result-matrix-details { | |
| border-top: 1px solid rgba(204, 255, 160, 0.14); | |
| background: rgba(2, 5, 2, 0.26); | |
| } | |
| .result-matrix-details summary { | |
| list-style: none; | |
| cursor: pointer; | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.34fr) minmax(260px, 1fr); | |
| gap: 16px; | |
| align-items: center; | |
| padding: 18px 24px; | |
| color: var(--muted); | |
| } | |
| .result-matrix-details summary::-webkit-details-marker { | |
| display: none; | |
| } | |
| .result-matrix-details summary span { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 820; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .result-matrix-details summary strong { | |
| color: rgba(245, 247, 240, 0.76); | |
| font-size: 13px; | |
| line-height: 1.45; | |
| } | |
| .result-matrix-details[open] summary { | |
| border-bottom: 1px solid rgba(204, 255, 160, 0.12); | |
| background: rgba(204, 255, 160, 0.06); | |
| } | |
| .result-matrix-body { | |
| padding: 22px 24px 24px; | |
| } | |
| .result-matrix-legend { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 8px; | |
| margin: 14px 0; | |
| } | |
| .matrix-legend-pill { | |
| min-height: 30px; | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 8px; | |
| padding: 0 10px; | |
| border: 1px solid rgba(204, 255, 160, 0.17); | |
| border-radius: 999px; | |
| color: rgba(245, 247, 240, 0.78); | |
| font-size: 12px; | |
| font-weight: 680; | |
| background: rgba(204, 255, 160, 0.045); | |
| } | |
| .matrix-legend-pill::before { | |
| content: ""; | |
| width: 8px; | |
| height: 8px; | |
| border-radius: 999px; | |
| background: var(--pill-color, var(--green)); | |
| box-shadow: 0 0 0 3px rgba(204, 255, 160, 0.08); | |
| } | |
| .term-with-help { | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 5px; | |
| min-width: 0; | |
| } | |
| .term-help { | |
| position: relative; | |
| display: inline-flex; | |
| align-items: center; | |
| justify-content: center; | |
| width: 17px; | |
| height: 17px; | |
| flex: 0 0 auto; | |
| border: 1px solid rgba(204, 255, 160, 0.40); | |
| border-radius: 999px; | |
| color: #ccffa0; | |
| background: rgba(204, 255, 160, 0.075); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 840; | |
| line-height: 1; | |
| text-transform: none; | |
| letter-spacing: 0; | |
| cursor: help; | |
| } | |
| .term-help:focus-visible { | |
| outline: 2px solid var(--green); | |
| outline-offset: 2px; | |
| } | |
| .term-tooltip { | |
| position: absolute; | |
| left: 50%; | |
| bottom: calc(100% + 9px); | |
| z-index: 30; | |
| width: min(280px, 78vw); | |
| transform: translate(-50%, 4px); | |
| opacity: 0; | |
| pointer-events: none; | |
| border: 1px solid rgba(204, 255, 160, 0.28); | |
| border-radius: 8px; | |
| background: rgba(2, 5, 2, 0.98); | |
| box-shadow: 0 18px 46px rgba(0, 0, 0, 0.42); | |
| color: #edf6e8; | |
| padding: 10px 11px; | |
| font-family: var(--font-copy); | |
| font-size: 12px; | |
| font-weight: 620; | |
| letter-spacing: 0; | |
| line-height: 1.4; | |
| text-align: left; | |
| text-transform: none; | |
| transition: opacity 140ms ease, transform 140ms ease; | |
| white-space: normal; | |
| } | |
| .term-help:hover .term-tooltip, | |
| .term-help:focus .term-tooltip, | |
| .term-help:focus-within .term-tooltip { | |
| opacity: 1; | |
| transform: translate(-50%, 0); | |
| } | |
| .term-tooltip::after { | |
| content: ""; | |
| position: absolute; | |
| left: 50%; | |
| top: 100%; | |
| width: 8px; | |
| height: 8px; | |
| transform: translate(-50%, -4px) rotate(45deg); | |
| border-right: 1px solid rgba(204, 255, 160, 0.28); | |
| border-bottom: 1px solid rgba(204, 255, 160, 0.28); | |
| background: rgba(2, 5, 2, 0.98); | |
| } | |
| .tooltip-portal-ready .term-tooltip { | |
| display: none ; | |
| } | |
| .term-floating-tooltip { | |
| position: fixed; | |
| left: 0; | |
| top: 0; | |
| z-index: 10000; | |
| width: min(360px, calc(100vw - 24px)); | |
| max-width: calc(100vw - 24px); | |
| transform: translate3d(var(--tooltip-x, 12px), var(--tooltip-y, 12px), 0); | |
| opacity: 0; | |
| pointer-events: none; | |
| border: 1px solid rgba(204, 255, 160, 0.32); | |
| border-radius: 10px; | |
| background: | |
| linear-gradient(180deg, rgba(12, 22, 11, 0.99), rgba(2, 5, 2, 0.99)); | |
| box-shadow: 0 20px 54px rgba(0, 0, 0, 0.58), 0 0 0 1px rgba(204, 255, 160, 0.07) inset; | |
| color: var(--text); | |
| padding: 11px 12px; | |
| font-family: var(--font-copy); | |
| font-size: var(--type-sm); | |
| font-weight: 620; | |
| letter-spacing: 0; | |
| line-height: 1.42; | |
| text-align: left; | |
| text-transform: none; | |
| white-space: normal; | |
| visibility: hidden; | |
| } | |
| .term-floating-tooltip.show { | |
| opacity: 1; | |
| visibility: visible; | |
| } | |
| .result-matrix-stat .term-with-help, | |
| .matrix-legend-pill .term-with-help, | |
| .method-cell .term-with-help, | |
| .task-header .term-with-help { | |
| display: inline-flex; | |
| } | |
| .result-matrix-stat .term-with-help { | |
| margin-top: 8px; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.3; | |
| } | |
| .result-matrix-stat .term-with-help span:first-child, | |
| .matrix-legend-pill .term-with-help span:first-child, | |
| .method-cell .term-with-help span:first-child, | |
| .task-header .term-with-help span:first-child { | |
| display: inline; | |
| margin-top: 0; | |
| } | |
| .method-cell .term-help, | |
| .task-header .term-help { | |
| width: 15px; | |
| height: 15px; | |
| font-size: 9px; | |
| } | |
| .method-summary-scroll, | |
| .result-matrix-scroll { | |
| overflow-x: auto; | |
| border: 1px solid rgba(204, 255, 160, 0.14); | |
| border-radius: var(--radius); | |
| background: rgba(0, 0, 0, 0.20); | |
| } | |
| .method-summary-scroll { | |
| margin: 12px 0 18px; | |
| } | |
| .method-summary-table, | |
| .result-score-table { | |
| width: 100%; | |
| border-collapse: separate; | |
| border-spacing: 0; | |
| color: var(--ink); | |
| } | |
| .method-summary-table th, | |
| .method-summary-table td, | |
| .result-score-table th, | |
| .result-score-table td { | |
| border-right: 1px solid rgba(204, 255, 160, 0.10); | |
| border-bottom: 1px solid rgba(204, 255, 160, 0.10); | |
| padding: 10px 12px; | |
| text-align: left; | |
| vertical-align: top; | |
| font-size: 12px; | |
| line-height: 1.35; | |
| } | |
| .method-summary-table th, | |
| .result-score-table th { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 820; | |
| letter-spacing: 0.07em; | |
| text-transform: uppercase; | |
| background: rgba(204, 255, 160, 0.08); | |
| } | |
| .result-score-table { | |
| min-width: 2380px; | |
| } | |
| .result-score-table thead th { | |
| position: sticky; | |
| top: 0; | |
| z-index: 2; | |
| } | |
| .result-score-table th:first-child, | |
| .result-score-table td:first-child { | |
| position: sticky; | |
| left: 0; | |
| z-index: 3; | |
| min-width: 220px; | |
| max-width: 220px; | |
| background: rgba(5, 13, 6, 0.98); | |
| box-shadow: 10px 0 18px rgba(0, 0, 0, 0.24); | |
| } | |
| .result-score-table thead th:first-child { | |
| z-index: 4; | |
| background: rgba(15, 28, 14, 0.98); | |
| } | |
| .method-cell strong, | |
| .task-header strong { | |
| display: block; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 13px; | |
| line-height: 1.14; | |
| } | |
| .method-cell span, | |
| .task-header span { | |
| display: block; | |
| margin-top: 4px; | |
| color: rgba(245, 247, 240, 0.56); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| line-height: 1.25; | |
| } | |
| .method-cell .term-with-help, | |
| .task-header .term-with-help, | |
| .method-summary-table th .term-with-help, | |
| .result-score-table th .term-with-help { | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 5px; | |
| margin-top: 0; | |
| color: inherit; | |
| font: inherit; | |
| line-height: inherit; | |
| } | |
| .method-cell .term-with-help > span:first-child, | |
| .task-header .term-with-help > span:first-child, | |
| .method-summary-table th .term-with-help > span:first-child, | |
| .result-score-table th .term-with-help > span:first-child { | |
| display: inline; | |
| margin-top: 0; | |
| color: inherit; | |
| font: inherit; | |
| line-height: inherit; | |
| } | |
| .method-cell .term-help, | |
| .task-header .term-help, | |
| .method-summary-table th .term-help, | |
| .result-score-table th .term-help { | |
| display: inline-flex; | |
| margin-top: 0; | |
| font-family: var(--font-mono); | |
| font-size: 9px; | |
| line-height: 1; | |
| } | |
| .method-cell .term-tooltip, | |
| .task-header .term-tooltip, | |
| .method-summary-table th .term-tooltip, | |
| .result-score-table th .term-tooltip { | |
| display: block; | |
| margin-top: 0; | |
| color: #edf6e8; | |
| font-family: var(--font-copy); | |
| font-size: 12px; | |
| font-weight: 620; | |
| line-height: 1.4; | |
| } | |
| .score-chip { | |
| display: block; | |
| min-width: 94px; | |
| padding-left: 9px; | |
| border-left: 3px solid var(--score-color, var(--green)); | |
| } | |
| .score-chip strong { | |
| display: block; | |
| color: var(--ink); | |
| font-family: var(--font-mono); | |
| font-size: 14px; | |
| line-height: 1.1; | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .score-chip span { | |
| display: block; | |
| margin-top: 4px; | |
| color: rgba(245, 247, 240, 0.60); | |
| font-size: 10.5px; | |
| line-height: 1.25; | |
| word-break: break-word; | |
| } | |
| .score-chip em { | |
| display: inline-flex; | |
| align-items: center; | |
| margin-top: 6px; | |
| padding: 2px 6px; | |
| border-radius: 999px; | |
| color: #020502; | |
| background: var(--score-color, var(--green)); | |
| font-family: var(--font-mono); | |
| font-size: 9px; | |
| font-style: normal; | |
| font-weight: 820; | |
| text-transform: uppercase; | |
| } | |
| .score-chip.proxy em { | |
| color: #1b0612; | |
| background: #f472b6; | |
| } | |
| .score-source-link { | |
| color: inherit; | |
| text-decoration: none; | |
| } | |
| .score-source-link:hover .score-chip strong { | |
| color: var(--green); | |
| } | |
| .result-matrix-foot { | |
| margin: 13px 0 0; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.45; | |
| } | |
| .task-suite-image { | |
| display: block; | |
| margin-top: 30px; | |
| } | |
| .figure-pan { | |
| overflow-x: auto; | |
| overflow-y: hidden; | |
| border-radius: var(--radius); | |
| padding-bottom: 4px; | |
| } | |
| .figure-pan .task-suite-image { | |
| margin-bottom: 0; | |
| min-width: 0; | |
| max-width: 100%; | |
| height: auto; | |
| } | |
| .atlas-note { | |
| margin: 16px 0 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.55; | |
| } | |
| .raw-sample-layout { | |
| display: grid; | |
| grid-template-columns: minmax(0, 1fr) minmax(330px, 0.46fr); | |
| gap: 18px; | |
| align-items: stretch; | |
| } | |
| .raw-player-panel, | |
| .raw-file-panel, | |
| .raw-detail-card { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.07), rgba(7, 18, 7, 0.88)), | |
| var(--surface); | |
| box-shadow: 0 18px 46px rgba(0, 0, 0, 0.28); | |
| } | |
| .raw-player-panel { | |
| padding: clamp(18px, 2.2vw, 28px); | |
| display: grid; | |
| gap: 16px; | |
| align-content: start; | |
| min-width: 0; | |
| } | |
| .raw-player-top { | |
| display: flex; | |
| align-items: start; | |
| justify-content: space-between; | |
| gap: 18px; | |
| padding-bottom: 14px; | |
| border-bottom: 1px solid var(--soft-line); | |
| } | |
| .raw-player-top h3 { | |
| margin: 0; | |
| font-family: var(--font-ui); | |
| font-size: clamp(24px, 3vw, 38px); | |
| line-height: 1.04; | |
| overflow-wrap: anywhere; | |
| } | |
| .raw-player-top p { | |
| margin: 8px 0 0; | |
| max-width: 720px; | |
| color: var(--muted); | |
| font-size: 14px; | |
| line-height: 1.55; | |
| } | |
| .raw-kind-pill { | |
| flex: none; | |
| border: 1px solid rgba(204, 255, 160, 0.28); | |
| border-radius: 999px; | |
| background: rgba(204, 255, 160, 0.08); | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 12px; | |
| font-weight: 700; | |
| padding: 7px 9px; | |
| text-transform: uppercase; | |
| } | |
| .raw-video-frame { | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: var(--radius); | |
| background: #020502; | |
| overflow: hidden; | |
| } | |
| .raw-video-frame[hidden], | |
| .raw-audio-frame[hidden] { display: none; } | |
| .raw-video-frame video { | |
| display: block; | |
| width: 100%; | |
| height: clamp(240px, 42vh, 420px); | |
| object-fit: contain; | |
| background: #020502; | |
| } | |
| .raw-preview-note { | |
| margin: -4px 0 0; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.45; | |
| } | |
| .raw-preview-note[hidden] { display: none; } | |
| .raw-audio-frame { | |
| border: 1px solid rgba(216, 244, 165, 0.26); | |
| border-radius: var(--radius); | |
| background: rgba(216, 244, 165, 0.06); | |
| padding: 14px; | |
| } | |
| .raw-audio-frame strong { | |
| display: block; | |
| margin-bottom: 10px; | |
| font-family: var(--font-ui); | |
| font-size: 14px; | |
| } | |
| .raw-audio-frame audio { width: 100%; } | |
| .raw-link-row { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 10px; | |
| } | |
| .raw-link-row a { | |
| min-height: 38px; | |
| display: inline-flex; | |
| align-items: center; | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| color: var(--cyan); | |
| font-size: 13px; | |
| font-weight: 700; | |
| padding: 8px 10px; | |
| text-decoration: none; | |
| background: rgba(2, 5, 2, 0.42); | |
| } | |
| .raw-link-row a:hover { border-color: var(--green); color: var(--ink); } | |
| .raw-file-panel { | |
| padding: 14px; | |
| display: grid; | |
| gap: 8px; | |
| align-content: start; | |
| } | |
| .raw-file-button { | |
| appearance: none; | |
| width: 100%; | |
| min-height: 86px; | |
| border: 1px solid rgba(204, 255, 160, 0.14); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.48); | |
| color: var(--ink); | |
| cursor: pointer; | |
| display: grid; | |
| grid-template-columns: 78px minmax(0, 1fr) auto; | |
| gap: 5px 12px; | |
| align-items: center; | |
| padding: 12px; | |
| text-align: left; | |
| font: inherit; | |
| } | |
| .raw-file-button:hover, | |
| .raw-file-button.active { | |
| border-color: rgba(204, 255, 160, 0.74); | |
| background: rgba(204, 255, 160, 0.10); | |
| } | |
| .raw-file-button strong { | |
| grid-column: 2; | |
| min-width: 0; | |
| font-family: var(--font-ui); | |
| font-size: 15px; | |
| line-height: 1.16; | |
| overflow-wrap: anywhere; | |
| } | |
| .raw-file-button span { | |
| grid-column: 2 / -1; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.35; | |
| } | |
| .raw-file-button em { | |
| grid-column: 3; | |
| color: var(--green); | |
| font-style: normal; | |
| font-family: var(--font-mono); | |
| font-size: 12px; | |
| line-height: 1.2; | |
| text-align: right; | |
| white-space: nowrap; | |
| } | |
| .raw-file-button .raw-file-thumb { | |
| grid-column: 1; | |
| grid-row: 1 / span 2; | |
| width: 78px; | |
| height: 58px; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: 6px; | |
| object-fit: cover; | |
| background: rgba(0, 0, 0, 0.36); | |
| box-shadow: inset 0 0 0 1px rgba(245, 247, 240, 0.04); | |
| } | |
| .raw-file-button .raw-file-kind { | |
| display: grid; | |
| place-items: center; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 12px; | |
| font-weight: 800; | |
| letter-spacing: 0; | |
| text-transform: uppercase; | |
| } | |
| .raw-detail-grid { | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.75fr) minmax(0, 1.25fr); | |
| gap: 18px; | |
| margin-top: 18px; | |
| } | |
| .raw-detail-card { | |
| padding: 22px; | |
| min-width: 0; | |
| } | |
| .raw-detail-card h3 { | |
| margin: 0 0 12px; | |
| font-family: var(--font-ui); | |
| font-size: 20px; | |
| line-height: 1.18; | |
| } | |
| .raw-detail-card p { | |
| margin: 0 0 12px; | |
| color: var(--muted); | |
| line-height: 1.58; | |
| } | |
| .raw-tree { | |
| margin: 0; | |
| overflow-x: auto; | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.62); | |
| padding: 15px; | |
| color: #eaf5e5; | |
| font-family: var(--font-mono); | |
| font-size: 13px; | |
| line-height: 1.52; | |
| } | |
| .hdf5-map { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 10px; | |
| } | |
| .hdf5-map article { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.48); | |
| padding: 12px; | |
| min-width: 0; | |
| } | |
| .hdf5-map strong { | |
| display: block; | |
| margin-bottom: 6px; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 12px; | |
| overflow-wrap: anywhere; | |
| } | |
| .hdf5-map span { | |
| color: #c7d1c3; | |
| font-size: 12px; | |
| line-height: 1.45; | |
| } | |
| .raw-file-detail-map { | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| } | |
| .raw-file-detail-map[hidden] { | |
| display: none; | |
| } | |
| .architecture-image { | |
| display: block; | |
| } | |
| .callout-row { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 18px; | |
| margin-top: 18px; | |
| } | |
| .two-col { | |
| display: grid; | |
| grid-template-columns: minmax(0, 1fr) minmax(340px, 0.52fr); | |
| gap: 30px; | |
| align-items: start; | |
| } | |
| .callout { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 24px; | |
| background: rgba(204, 255, 160, 0.06); | |
| color: #eaf5e5; | |
| } | |
| .callout h3, .artifact h3 { margin: 0 0 8px; font-size: 17px; } | |
| .callout p, .artifact p { margin: 0; color: #aab5a5; line-height: 1.6; } | |
| .snapshot-grid { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 16px; | |
| } | |
| .snapshot-card { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 20px; | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.08), rgba(7, 18, 7, 0.9)), | |
| var(--surface); | |
| min-height: 210px; | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| } | |
| .snapshot-card.gated { | |
| border-color: rgba(216, 244, 165, 0.32); | |
| background: | |
| linear-gradient(180deg, rgba(216, 244, 165, 0.075), rgba(7, 18, 7, 0.9)), | |
| var(--surface); | |
| } | |
| .snapshot-card h3 { margin: 0; font-size: 18px; line-height: 1.2; } | |
| .snapshot-card p { margin: 0; color: var(--muted); line-height: 1.58; } | |
| .snapshot-meta { | |
| display: grid; | |
| gap: 6px; | |
| margin-top: 2px; | |
| padding-top: 12px; | |
| border-top: 1px solid var(--soft-line); | |
| color: #dce8d6; | |
| font-size: 13px; | |
| } | |
| .snapshot-meta span { | |
| display: flex; | |
| justify-content: space-between; | |
| gap: 14px; | |
| color: var(--muted); | |
| } | |
| .snapshot-meta strong { | |
| color: var(--ink); | |
| font-family: var(--font-mono); | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .snapshot-actions { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 10px; | |
| margin-top: 20px; | |
| } | |
| .snapshot-actions a { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| color: var(--cyan); | |
| font-size: 13px; | |
| font-weight: 700; | |
| padding: 9px 10px; | |
| text-decoration: none; | |
| background: rgba(2, 5, 2, 0.42); | |
| } | |
| .snapshot-actions a:hover { border-color: var(--green); color: var(--ink); } | |
| .roadmap-grid { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 16px; | |
| align-items: stretch; | |
| } | |
| .roadmap-card { | |
| position: relative; | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 18px; | |
| min-height: 0; | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.08), rgba(7, 18, 7, 0.92)), | |
| var(--surface); | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| } | |
| .roadmap-card::before { | |
| content: ""; | |
| position: absolute; | |
| left: 18px; | |
| right: 18px; | |
| top: 0; | |
| height: 2px; | |
| background: linear-gradient(90deg, var(--green), rgba(122, 229, 195, 0.2)); | |
| } | |
| .roadmap-card[data-status="active"] { | |
| border-color: rgba(122, 229, 195, 0.42); | |
| background: | |
| linear-gradient(180deg, rgba(122, 229, 195, 0.11), rgba(7, 18, 7, 0.92)), | |
| var(--surface); | |
| } | |
| .roadmap-card[data-status="next"] { | |
| border-color: rgba(216, 244, 165, 0.36); | |
| background: | |
| linear-gradient(180deg, rgba(216, 244, 165, 0.09), rgba(7, 18, 7, 0.92)), | |
| var(--surface); | |
| } | |
| .roadmap-status { | |
| width: fit-content; | |
| border: 1px solid var(--soft-line); | |
| border-radius: 999px; | |
| padding: 5px 8px; | |
| color: var(--green); | |
| background: rgba(2, 5, 2, 0.5); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 700; | |
| text-transform: uppercase; | |
| letter-spacing: 0.06em; | |
| } | |
| .roadmap-card h3 { | |
| margin: 0; | |
| color: var(--ink); | |
| font-size: 18px; | |
| line-height: 1.18; | |
| } | |
| .roadmap-card p { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.55; | |
| } | |
| .roadmap-meta { | |
| display: grid; | |
| gap: 8px; | |
| padding-top: 10px; | |
| border-top: 1px solid var(--soft-line); | |
| } | |
| .roadmap-meta strong { | |
| display: block; | |
| color: #dce8d7; | |
| font-size: 12px; | |
| line-height: 1.2; | |
| } | |
| .roadmap-links { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 8px; | |
| margin-top: 18px; | |
| } | |
| .roadmap-links a { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| color: var(--cyan); | |
| font-size: 13px; | |
| font-weight: 700; | |
| padding: 9px 10px; | |
| text-decoration: none; | |
| background: rgba(2, 5, 2, 0.42); | |
| } | |
| .roadmap-links a:hover { border-color: var(--green); color: var(--ink); } | |
| .roadmap-entry-panel, | |
| .roadmap-resource-panel { | |
| border: 1px solid rgba(204, 255, 160, 0.20); | |
| border-radius: var(--radius); | |
| background: | |
| radial-gradient(circle at 14% 10%, rgba(204, 255, 160, 0.10), transparent 32%), | |
| linear-gradient(135deg, rgba(204, 255, 160, 0.08), rgba(122, 229, 195, 0.035)), | |
| rgba(7, 18, 7, 0.72); | |
| padding: clamp(18px, 2.2vw, 24px); | |
| margin: 22px 0; | |
| } | |
| .roadmap-entry-panel { | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.92fr) minmax(360px, 1.08fr); | |
| gap: 22px; | |
| align-items: stretch; | |
| } | |
| .roadmap-entry-copy { | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| } | |
| .roadmap-entry-copy span, | |
| .roadmap-resource-head span, | |
| .roadmap-resource-card small { | |
| color: var(--green); | |
| font-family: var(--font-btn); | |
| font-size: 12px; | |
| font-weight: 700; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .roadmap-entry-copy h3, | |
| .roadmap-resource-head h3 { | |
| margin: 0; | |
| color: var(--ink); | |
| font-size: clamp(24px, 3vw, 34px); | |
| line-height: 1.05; | |
| text-wrap: balance; | |
| } | |
| .roadmap-entry-copy p, | |
| .roadmap-resource-head p, | |
| .roadmap-resource-card p, | |
| .structured-mirror-note { | |
| margin: 0; | |
| color: var(--muted); | |
| line-height: 1.58; | |
| } | |
| .roadmap-entry-steps { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 10px; | |
| margin: 0; | |
| padding: 0; | |
| list-style: none; | |
| } | |
| .roadmap-entry-steps li { | |
| min-height: 132px; | |
| border: 1px solid var(--soft-line); | |
| border-radius: 8px; | |
| background: rgba(2, 5, 2, 0.46); | |
| padding: 14px; | |
| display: grid; | |
| gap: 8px; | |
| align-content: start; | |
| } | |
| .roadmap-entry-steps strong { | |
| color: var(--ink); | |
| font-size: 16px; | |
| line-height: 1.18; | |
| } | |
| .roadmap-entry-steps span { | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.42; | |
| } | |
| .roadmap-quick-actions, | |
| .roadmap-resource-actions, | |
| .structured-mirror-grid { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 10px; | |
| } | |
| .roadmap-quick-actions a, | |
| .roadmap-resource-actions a, | |
| .structured-mirror-grid a { | |
| min-height: 38px; | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 7px; | |
| border: 1px solid var(--soft-line); | |
| border-radius: 999px; | |
| background: rgba(2, 5, 2, 0.48); | |
| color: var(--cyan); | |
| font-family: var(--font-btn); | |
| font-size: 13px; | |
| font-weight: 700; | |
| padding: 0 13px; | |
| text-decoration: none; | |
| } | |
| .roadmap-quick-actions a:first-child, | |
| .roadmap-resource-card.primary .roadmap-resource-actions a:first-child { | |
| color: #020502; | |
| border-color: var(--green); | |
| background: var(--green); | |
| } | |
| .roadmap-quick-actions a:hover, | |
| .roadmap-resource-actions a:hover, | |
| .structured-mirror-grid a:hover { | |
| color: var(--ink); | |
| border-color: var(--green); | |
| } | |
| .roadmap-resource-head { | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.78fr) minmax(320px, 0.72fr); | |
| gap: 22px; | |
| align-items: end; | |
| margin-bottom: 16px; | |
| } | |
| .roadmap-resource-grid { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 12px; | |
| } | |
| .roadmap-resource-card { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 8px; | |
| background: rgba(2, 5, 2, 0.50); | |
| padding: 16px; | |
| display: grid; | |
| gap: 10px; | |
| align-content: start; | |
| min-height: 168px; | |
| } | |
| .roadmap-resource-card.primary { | |
| grid-column: span 2; | |
| background: | |
| linear-gradient(135deg, rgba(204, 255, 160, 0.12), rgba(122, 229, 195, 0.05)), | |
| rgba(2, 5, 2, 0.54); | |
| } | |
| .roadmap-resource-card h3 { | |
| margin: 0; | |
| color: var(--ink); | |
| font-size: 18px; | |
| line-height: 1.18; | |
| } | |
| .structured-mirrors { | |
| margin-top: 14px; | |
| border: 1px solid var(--soft-line); | |
| border-radius: 8px; | |
| background: rgba(2, 5, 2, 0.46); | |
| padding: 14px; | |
| } | |
| .structured-mirrors summary { | |
| cursor: pointer; | |
| color: #dce8d6; | |
| font-family: var(--font-btn); | |
| font-weight: 700; | |
| } | |
| .structured-mirror-note { | |
| margin: 10px 0 12px; | |
| font-size: 13px; | |
| } | |
| .brief-panel { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: clamp(20px, 3vw, 32px); | |
| margin-bottom: 24px; | |
| background: | |
| linear-gradient(135deg, rgba(204, 255, 160, 0.13), rgba(122, 229, 195, 0.045) 58%, rgba(7, 18, 7, 0.9)), | |
| var(--surface); | |
| box-shadow: 0 22px 70px rgba(0, 0, 0, 0.28); | |
| } | |
| .brief-panel-head { | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.9fr) minmax(320px, 0.85fr); | |
| gap: 28px; | |
| align-items: end; | |
| margin-bottom: 22px; | |
| } | |
| .brief-panel-head span { | |
| display: block; | |
| margin-bottom: 10px; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 12px; | |
| font-weight: 700; | |
| text-transform: uppercase; | |
| letter-spacing: 0.08em; | |
| } | |
| .brief-panel-head h3 { | |
| margin: 0; | |
| font-family: var(--font-ui); | |
| font-size: clamp(30px, 4.8vw, 58px); | |
| line-height: 0.98; | |
| text-wrap: balance; | |
| } | |
| .brief-panel-head p { | |
| margin: 0; | |
| color: #c8d4c3; | |
| line-height: 1.65; | |
| font-size: 16px; | |
| text-wrap: pretty; | |
| } | |
| .brief-grid { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 14px; | |
| } | |
| .resource-mode strong { | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 19px; | |
| line-height: 1.1; | |
| } | |
| .resource-mode p { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.52; | |
| } | |
| .resource-mode a { | |
| justify-self: start; | |
| margin-top: auto; | |
| color: var(--cyan); | |
| font-weight: 760; | |
| text-decoration: none; | |
| } | |
| .resource-mode a:hover { | |
| color: var(--green); | |
| } | |
| .brief-card { | |
| border: 1px solid var(--soft-line); | |
| border-radius: var(--radius); | |
| padding: 18px; | |
| background: rgba(2, 5, 2, 0.42); | |
| min-height: 216px; | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| } | |
| .brief-card strong { | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 18px; | |
| line-height: 1.14; | |
| } | |
| .brief-card p, | |
| .brief-card li { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.55; | |
| } | |
| .brief-card ul { | |
| display: grid; | |
| gap: 8px; | |
| margin: 0; | |
| padding: 0; | |
| list-style: none; | |
| } | |
| .brief-card li::before { | |
| content: ""; | |
| display: inline-block; | |
| width: 6px; | |
| height: 6px; | |
| margin: 0 8px 1px 0; | |
| border-radius: 999px; | |
| background: var(--green); | |
| } | |
| .reader-map-panel { | |
| margin-bottom: 28px; | |
| } | |
| .reader-map-panel .brief-panel-head h3 { | |
| font-size: clamp(26px, 3.8vw, 48px); | |
| } | |
| .reader-map-panel .brief-card { | |
| min-height: 164px; | |
| } | |
| .reader-map-panel .brief-card small { | |
| color: var(--cyan); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 700; | |
| letter-spacing: 0.03em; | |
| text-transform: uppercase; | |
| } | |
| .surface-map { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 12px; | |
| margin-top: 14px; | |
| } | |
| .surface-map a { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 8px; | |
| padding: 12px; | |
| color: var(--ink); | |
| text-decoration: none; | |
| background: rgba(2, 5, 2, 0.46); | |
| } | |
| .surface-map a strong { | |
| display: block; | |
| margin-bottom: 5px; | |
| font-size: 14px; | |
| } | |
| .surface-map a span { | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.45; | |
| } | |
| .surface-map a:hover { border-color: var(--green); } | |
| .glossary-panel { | |
| display: grid; | |
| grid-template-columns: 1fr; | |
| gap: 18px; | |
| align-items: start; | |
| } | |
| .glossary-summary { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 12px; | |
| } | |
| .glossary-summary article { | |
| border: 1px solid var(--line); | |
| border-radius: var(--card-radius); | |
| padding: 18px; | |
| background: linear-gradient(180deg, rgba(204, 255, 160, 0.1), rgba(122, 229, 195, 0.025)); | |
| } | |
| .glossary-summary small { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 800; | |
| letter-spacing: 0.06em; | |
| text-transform: uppercase; | |
| } | |
| .glossary-summary strong { | |
| display: block; | |
| margin-top: 8px; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 20px; | |
| line-height: 1.15; | |
| } | |
| .glossary-summary p { | |
| margin: 8px 0 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.5; | |
| } | |
| .glossary-controls { | |
| display: grid; | |
| gap: 12px; | |
| margin-bottom: 14px; | |
| border: 1px solid rgba(204, 255, 160, 0.16); | |
| border-radius: var(--card-radius); | |
| background: rgba(2, 5, 2, 0.48); | |
| padding: 14px; | |
| } | |
| .glossary-search { | |
| display: grid; | |
| gap: 7px; | |
| } | |
| .glossary-search span, | |
| .glossary-count { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 820; | |
| letter-spacing: 0.07em; | |
| text-transform: uppercase; | |
| } | |
| .glossary-search input { | |
| width: 100%; | |
| border: 1px solid rgba(204, 255, 160, 0.22); | |
| border-radius: 8px; | |
| background: rgba(0, 0, 0, 0.28); | |
| color: var(--ink); | |
| font: inherit; | |
| font-size: 14px; | |
| line-height: 1.2; | |
| padding: 11px 12px; | |
| outline: none; | |
| } | |
| .glossary-search input:focus { | |
| border-color: rgba(204, 255, 160, 0.62); | |
| box-shadow: 0 0 0 3px rgba(204, 255, 160, 0.10); | |
| } | |
| .glossary-filter-bar { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 8px; | |
| } | |
| .glossary-filter { | |
| min-height: 32px; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: 999px; | |
| background: rgba(204, 255, 160, 0.055); | |
| color: #dce8d6; | |
| cursor: pointer; | |
| font-family: var(--font-btn); | |
| font-size: 11.5px; | |
| font-weight: 760; | |
| padding: 0 11px; | |
| transition: background 180ms var(--motion-ease), border-color 180ms var(--motion-ease), color 180ms var(--motion-ease); | |
| } | |
| .glossary-filter:hover { | |
| border-color: rgba(204, 255, 160, 0.48); | |
| color: var(--ink); | |
| } | |
| .glossary-filter.active { | |
| border-color: var(--green); | |
| background: var(--green); | |
| color: #020502; | |
| } | |
| .glossary-count { | |
| margin: 0; | |
| color: rgba(204, 255, 160, 0.74); | |
| text-transform: none; | |
| letter-spacing: 0.02em; | |
| } | |
| .glossary-table-shell { | |
| border: 1px solid var(--line); | |
| border-radius: var(--card-radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.055), rgba(2, 5, 2, 0.62)), | |
| rgba(2, 5, 2, 0.58); | |
| overflow: hidden; | |
| box-shadow: inset 0 0 0 1px rgba(255, 255, 255, 0.018); | |
| } | |
| .glossary-scroll-note { | |
| display: flex; | |
| align-items: center; | |
| justify-content: space-between; | |
| gap: 16px; | |
| padding: 12px 14px; | |
| border-bottom: 1px solid rgba(204, 255, 160, 0.14); | |
| background: rgba(2, 5, 2, 0.78); | |
| } | |
| .glossary-scroll-note span { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 850; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| white-space: nowrap; | |
| } | |
| .glossary-scroll-note p { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.4; | |
| text-align: right; | |
| } | |
| .glossary-table-wrap { | |
| max-height: clamp(430px, 58vh, 760px); | |
| overflow: auto; | |
| overscroll-behavior: contain; | |
| background: rgba(2, 5, 2, 0.36); | |
| scrollbar-color: rgba(204, 255, 160, 0.45) rgba(204, 255, 160, 0.08); | |
| scrollbar-width: thin; | |
| } | |
| .glossary-table-wrap:focus-visible { | |
| outline: 3px solid rgba(204, 255, 160, 0.26); | |
| outline-offset: -3px; | |
| } | |
| .glossary-table-wrap::-webkit-scrollbar { | |
| width: 10px; | |
| height: 10px; | |
| } | |
| .glossary-table-wrap::-webkit-scrollbar-track { | |
| background: rgba(204, 255, 160, 0.06); | |
| } | |
| .glossary-table-wrap::-webkit-scrollbar-thumb { | |
| border: 2px solid rgba(2, 5, 2, 0.72); | |
| border-radius: 999px; | |
| background: rgba(204, 255, 160, 0.50); | |
| } | |
| .glossary-table { | |
| width: 100%; | |
| min-width: 920px; | |
| border-collapse: collapse; | |
| font-size: 13px; | |
| line-height: 1.46; | |
| } | |
| .glossary-table th, | |
| .glossary-table td { | |
| padding: 14px 16px; | |
| border-bottom: 1px solid rgba(204, 255, 160, 0.14); | |
| vertical-align: top; | |
| text-align: left; | |
| } | |
| .glossary-table th { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| letter-spacing: 0.05em; | |
| text-transform: uppercase; | |
| position: sticky; | |
| top: 0; | |
| z-index: 2; | |
| background: rgba(15, 25, 11, 0.96); | |
| backdrop-filter: blur(12px); | |
| } | |
| .glossary-table td:first-child { | |
| width: 18%; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 15px; | |
| font-weight: 750; | |
| } | |
| .glossary-table td:first-child small { | |
| display: block; | |
| margin-top: 6px; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 800; | |
| letter-spacing: 0.05em; | |
| text-transform: uppercase; | |
| opacity: 0.82; | |
| } | |
| .glossary-table td:nth-child(2) { | |
| width: 32%; | |
| color: #d8e2d4; | |
| } | |
| .glossary-table td:nth-child(3), | |
| .glossary-table td:nth-child(4) { | |
| color: var(--muted); | |
| } | |
| .glossary-table tr:last-child td { | |
| border-bottom: 0; | |
| } | |
| .glossary-table tr[hidden] { | |
| display: none; | |
| } | |
| .glossary-links { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 10px; | |
| margin-top: 16px; | |
| } | |
| .glossary-links a { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| color: var(--blue); | |
| font-size: 13px; | |
| font-weight: 800; | |
| padding: 9px 11px; | |
| text-decoration: none; | |
| background: rgba(255, 255, 255, 0.035); | |
| } | |
| .glossary-links a:hover { | |
| border-color: var(--green); | |
| color: var(--green); | |
| } | |
| .brief-actions { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 10px; | |
| margin-top: 18px; | |
| } | |
| .brief-actions a { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| color: var(--blue); | |
| font-size: 13px; | |
| font-weight: 700; | |
| padding: 8px 10px; | |
| text-decoration: none; | |
| background: rgba(2, 5, 2, 0.48); | |
| } | |
| .brief-actions a:first-child { | |
| color: #020502; | |
| background: var(--green); | |
| border-color: var(--green); | |
| } | |
| .brief-actions a:hover { border-color: var(--green); color: var(--ink); } | |
| .brief-actions a:first-child:hover { color: #020502; } | |
| .reading-grid { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 16px; | |
| margin-bottom: 18px; | |
| } | |
| .reading-card { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 18px; | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.075), rgba(7, 18, 7, 0.9)), | |
| var(--surface); | |
| min-height: 270px; | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| } | |
| .reading-card .step-index { | |
| width: 38px; | |
| height: 38px; | |
| display: grid; | |
| place-items: center; | |
| border: 1px solid rgba(204, 255, 160, 0.36); | |
| border-radius: 8px; | |
| color: #020502; | |
| background: var(--green); | |
| font-family: var(--font-mono); | |
| font-weight: 700; | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .reading-card h3 { margin: 0; font-size: 17px; line-height: 1.22; } | |
| .reading-card p { margin: 0; color: var(--muted); font-size: 13px; line-height: 1.55; } | |
| .reading-links { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 8px; | |
| margin-top: 4px; | |
| } | |
| .reading-links a { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| color: var(--cyan); | |
| font-size: 12px; | |
| font-weight: 700; | |
| padding: 7px 8px; | |
| text-decoration: none; | |
| background: rgba(2, 5, 2, 0.42); | |
| } | |
| .reading-links a:hover { border-color: var(--green); color: var(--ink); } | |
| .boundary-strip { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 16px; | |
| margin-top: 18px; | |
| } | |
| .boundary-item { | |
| border: 1px solid var(--soft-line); | |
| border-radius: var(--radius); | |
| padding: 18px; | |
| background: rgba(204, 255, 160, 0.055); | |
| } | |
| .boundary-item strong { | |
| display: block; | |
| margin-bottom: 8px; | |
| color: var(--ink); | |
| font-size: 15px; | |
| } | |
| .boundary-item span { | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.55; | |
| } | |
| .evidence-grid { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 18px; | |
| } | |
| .evidence-card { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 22px; | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.06), rgba(7, 18, 7, 0.88)), | |
| var(--surface); | |
| min-height: 230px; | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| } | |
| .evidence-card h3 { margin: 0; font-size: 18px; line-height: 1.2; } | |
| .evidence-card p { margin: 0; color: var(--muted); line-height: 1.6; } | |
| .evidence-card code { color: var(--ink); font-family: var(--font-mono); font-size: 12px; } | |
| .evidence-card:last-child { grid-column: 1 / -1; } | |
| .evidence-links { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 10px; | |
| margin-top: 4px; | |
| } | |
| .evidence-links a { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| color: var(--blue); | |
| font-size: 13px; | |
| font-weight: 700; | |
| padding: 7px 9px; | |
| text-decoration: none; | |
| background: rgba(2, 5, 2, 0.42); | |
| } | |
| .evidence-links a:hover { border-color: var(--green); color: var(--ink); } | |
| .models { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 16px; | |
| margin-bottom: 24px; | |
| } | |
| .model { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 18px; | |
| background: var(--surface); | |
| transition: transform 240ms cubic-bezier(0.16, 1, 0.3, 1), box-shadow 240ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .model:hover { transform: translateY(-3px); box-shadow: 0 18px 38px rgba(204, 255, 160, 0.08); } | |
| .model h3 { margin: 0; font-size: 15px; } | |
| .model .score { display: block; margin-top: 18px; font-family: var(--font-mono); font-size: 33px; font-weight: 700; line-height: 1; font-variant-numeric: tabular-nums; } | |
| .model .meta { display: block; margin-top: 8px; color: var(--muted); font-size: 13px; } | |
| .task-toolbar { | |
| display: flex; | |
| gap: 10px; | |
| flex-wrap: wrap; | |
| margin-bottom: 18px; | |
| } | |
| .filter { | |
| border: 1px solid var(--line); | |
| background: var(--surface); | |
| border-radius: 999px; | |
| height: 36px; | |
| padding: 0 14px; | |
| font-weight: 650; | |
| color: #d7e3d0; | |
| cursor: pointer; | |
| transition: transform 220ms cubic-bezier(0.16, 1, 0.3, 1), background 220ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .filter:hover { transform: translateY(-1px); } | |
| .filter.active { color: #020502; background: var(--green); border-color: var(--green); } | |
| .task-grid { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 18px; | |
| } | |
| .task-card { | |
| appearance: none; | |
| width: 100%; | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 20px; | |
| background: var(--surface); | |
| color: inherit; | |
| font: inherit; | |
| text-align: left; | |
| cursor: pointer; | |
| display: grid; | |
| gap: 15px; | |
| min-height: 520px; | |
| align-content: start; | |
| transition: transform 240ms cubic-bezier(0.16, 1, 0.3, 1), border-color 240ms cubic-bezier(0.16, 1, 0.3, 1), box-shadow 240ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .task-card:hover { transform: translateY(-3px); border-color: var(--green); } | |
| .task-card.active { | |
| border-color: rgba(204, 255, 160, 0.72); | |
| box-shadow: 0 20px 48px rgba(204, 255, 160, 0.08); | |
| } | |
| .task-card.hide { display: none; } | |
| .task-title-wrap { | |
| display: grid; | |
| grid-template-columns: 46px minmax(0, 1fr); | |
| gap: 11px; | |
| align-items: start; | |
| min-width: 0; | |
| } | |
| .task-card-icon, | |
| .task-axis-icon, | |
| .selector-task-icon, | |
| .player-task-icon { | |
| display: block; | |
| object-fit: contain; | |
| flex: 0 0 auto; | |
| filter: drop-shadow(0 0 10px rgba(167, 255, 79, 0.20)); | |
| } | |
| .task-card-icon { | |
| width: 46px; | |
| height: 46px; | |
| border: 1px solid rgba(204, 255, 160, 0.26); | |
| border-radius: 10px; | |
| background: rgba(2, 5, 2, 0.72); | |
| } | |
| .task-top { display: flex; justify-content: space-between; gap: 14px; align-items: start; } | |
| .task-name { | |
| display: block; | |
| font-family: var(--font-ui); | |
| font-size: 21px; | |
| font-weight: 700; | |
| line-height: 1.08; | |
| letter-spacing: 0; | |
| text-wrap: balance; | |
| } | |
| .task-research-name { | |
| display: block; | |
| margin-top: 6px; | |
| color: #d7e5d1; | |
| font-size: 13px; | |
| line-height: 1.35; | |
| } | |
| .tag { | |
| font-size: 11px; | |
| border-radius: 999px; | |
| padding: 4px 8px; | |
| color: #d8ead2; | |
| background: rgba(204, 255, 160, 0.08); | |
| white-space: nowrap; | |
| } | |
| .tag.supervised { background: rgba(155, 223, 255, 0.12); color: #9bdfff; } | |
| .tag.forecast { background: rgba(204, 255, 160, 0.12); color: #ccffa0; } | |
| .tag.retrieval { background: rgba(122, 229, 195, 0.12); color: #7ae5c3; } | |
| .tag.diagnostic { background: rgba(216, 244, 165, 0.12); color: #d8f4a5; } | |
| .task-card p { margin: 0; color: var(--muted); font-size: 13px; } | |
| .task-modality-chips { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 6px; | |
| align-items: center; | |
| } | |
| .task-modality-chips span { | |
| display: inline-flex; | |
| align-items: center; | |
| min-height: 26px; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: 999px; | |
| padding: 5px 8px; | |
| color: #d8ead2; | |
| background: rgba(204, 255, 160, 0.07); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 800; | |
| letter-spacing: 0.035em; | |
| text-transform: uppercase; | |
| } | |
| .task-contract { | |
| display: grid; | |
| gap: 8px; | |
| color: #dce8d6; | |
| font-size: 12px; | |
| line-height: 1.4; | |
| } | |
| .task-contract span { | |
| display: grid; | |
| grid-template-columns: 58px minmax(0, 1fr); | |
| gap: 10px; | |
| align-items: baseline; | |
| border-top: 1px solid var(--soft-line); | |
| padding-top: 8px; | |
| } | |
| .task-contract strong { | |
| color: var(--muted); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| text-transform: uppercase; | |
| letter-spacing: 0.04em; | |
| } | |
| .task-results-panel { | |
| display: grid; | |
| gap: 10px; | |
| border-top: 1px solid var(--soft-line); | |
| padding-top: 11px; | |
| } | |
| .task-results-head { | |
| display: flex; | |
| justify-content: space-between; | |
| gap: 12px; | |
| align-items: baseline; | |
| color: #dce8d6; | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 850; | |
| letter-spacing: 0.045em; | |
| text-transform: uppercase; | |
| } | |
| .task-results-head em { | |
| color: var(--muted); | |
| font-style: normal; | |
| text-align: right; | |
| } | |
| .task-method-grid { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 8px; | |
| } | |
| .task-method-score { | |
| --score-color: var(--green); | |
| --score-width: 8%; | |
| position: relative; | |
| display: grid; | |
| gap: 6px; | |
| min-width: 0; | |
| min-height: 84px; | |
| border: 1px solid color-mix(in srgb, var(--score-color), transparent 72%); | |
| border-radius: 8px; | |
| padding: 9px; | |
| background: | |
| linear-gradient(180deg, color-mix(in srgb, var(--score-color), transparent 92%), rgba(2, 5, 2, 0.70)); | |
| overflow: hidden; | |
| } | |
| .task-method-score::after { | |
| content: ""; | |
| position: absolute; | |
| left: 0; | |
| bottom: 0; | |
| width: var(--score-width); | |
| height: 3px; | |
| background: linear-gradient(90deg, var(--score-color), color-mix(in srgb, var(--score-color), white 34%)); | |
| border-radius: 999px; | |
| } | |
| .task-method-score.proxy { | |
| border-style: dashed; | |
| } | |
| .task-method-top { | |
| display: flex; | |
| justify-content: space-between; | |
| gap: 6px; | |
| align-items: center; | |
| min-width: 0; | |
| } | |
| .task-method-top strong { | |
| color: var(--ink); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| line-height: 1; | |
| letter-spacing: 0.035em; | |
| white-space: nowrap; | |
| overflow: hidden; | |
| text-overflow: ellipsis; | |
| } | |
| .task-method-top em { | |
| border: 1px solid color-mix(in srgb, var(--score-color), transparent 58%); | |
| border-radius: 999px; | |
| padding: 2px 5px; | |
| color: color-mix(in srgb, var(--score-color), white 24%); | |
| background: rgba(2, 5, 2, 0.42); | |
| font-family: var(--font-mono); | |
| font-size: 8px; | |
| font-style: normal; | |
| font-weight: 900; | |
| letter-spacing: 0.045em; | |
| line-height: 1; | |
| text-transform: uppercase; | |
| white-space: nowrap; | |
| } | |
| .task-method-value { | |
| color: var(--ink); | |
| font-family: var(--font-mono); | |
| font-size: 17px; | |
| font-weight: 900; | |
| line-height: 1.05; | |
| font-variant-numeric: tabular-nums; | |
| overflow-wrap: anywhere; | |
| } | |
| .task-method-meta { | |
| color: var(--muted); | |
| font-family: var(--font-mono); | |
| font-size: 9px; | |
| font-weight: 750; | |
| line-height: 1.25; | |
| letter-spacing: 0.02em; | |
| overflow-wrap: anywhere; | |
| } | |
| .artifact-grid { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 18px; | |
| } | |
| .artifact-library { | |
| display: grid; | |
| gap: 24px; | |
| } | |
| .resource-mode-grid { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 12px; | |
| margin-bottom: 24px; | |
| } | |
| .resource-mode { | |
| display: grid; | |
| gap: 10px; | |
| min-height: 154px; | |
| align-content: start; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.08), rgba(2, 5, 2, 0.38)), | |
| rgba(7, 18, 7, 0.56); | |
| padding: 17px; | |
| } | |
| .artifact-group { | |
| border: 1px solid var(--soft-line); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.045), rgba(7, 18, 7, 0.62)), | |
| var(--panel); | |
| padding: 20px; | |
| } | |
| .artifact-group-head { | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.75fr) minmax(280px, 0.95fr); | |
| gap: 24px; | |
| align-items: end; | |
| padding-bottom: 18px; | |
| margin-bottom: 18px; | |
| border-bottom: 1px solid var(--soft-line); | |
| } | |
| .artifact-group-head span { | |
| display: block; | |
| margin-bottom: 8px; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 12px; | |
| font-weight: 700; | |
| text-transform: uppercase; | |
| letter-spacing: 0.08em; | |
| } | |
| .artifact-group-head h3 { | |
| margin: 0; | |
| font-size: 24px; | |
| line-height: 1.06; | |
| } | |
| .artifact-group-head p { | |
| margin: 0; | |
| color: var(--muted); | |
| line-height: 1.6; | |
| font-size: 14px; | |
| } | |
| .chart-grid { | |
| display: grid; | |
| grid-template-columns: minmax(0, 1fr); | |
| gap: 22px; | |
| align-items: start; | |
| } | |
| .data-scope-explorer { | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: var(--radius-lg); | |
| background: | |
| linear-gradient(135deg, rgba(204, 255, 160, 0.075), rgba(103, 232, 209, 0.035) 46%, rgba(2, 5, 2, 0.84)), | |
| rgba(2, 5, 2, 0.72); | |
| padding: 18px; | |
| margin: 22px 0 24px; | |
| box-shadow: 0 24px 70px rgba(0, 0, 0, 0.18); | |
| } | |
| .data-analysis-grid { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 16px; | |
| margin: 0; | |
| } | |
| .data-analysis-card { | |
| border: 1px solid rgba(204, 255, 160, 0.20); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.06), rgba(7, 18, 7, 0.66)), | |
| rgba(2, 5, 2, 0.78); | |
| padding: 18px; | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| min-height: 254px; | |
| appearance: none; | |
| width: 100%; | |
| color: inherit; | |
| font: inherit; | |
| text-align: left; | |
| cursor: pointer; | |
| transition: border-color 160ms ease, background 160ms ease, box-shadow 160ms ease, transform 160ms ease; | |
| } | |
| .data-analysis-card:hover, | |
| .data-analysis-card:focus-visible, | |
| .data-analysis-card[aria-pressed="true"] { | |
| border-color: rgba(204, 255, 160, 0.72); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.15), rgba(7, 18, 7, 0.70)), | |
| rgba(2, 5, 2, 0.88); | |
| box-shadow: 0 0 0 1px rgba(204, 255, 160, 0.12), 0 18px 46px rgba(0, 0, 0, 0.22); | |
| transform: translateY(-2px); | |
| } | |
| .data-analysis-card:focus-visible { | |
| outline: 2px solid rgba(204, 255, 160, 0.85); | |
| outline-offset: 3px; | |
| } | |
| .data-analysis-card small, | |
| .data-analysis-table small, | |
| .data-chart-card small { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 800; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .data-analysis-card h3 { | |
| margin: 0; | |
| color: var(--ink); | |
| font-size: clamp(24px, 2.4vw, 34px); | |
| line-height: 1.02; | |
| } | |
| .data-analysis-card p { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 15px; | |
| line-height: 1.55; | |
| } | |
| .data-analysis-metrics { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 9px; | |
| margin-top: 2px; | |
| } | |
| .data-analysis-metrics span { | |
| border: 1px solid rgba(204, 255, 160, 0.15); | |
| border-radius: 12px; | |
| background: rgba(204, 255, 160, 0.055); | |
| padding: 10px; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.25; | |
| } | |
| .data-analysis-metrics strong { | |
| display: block; | |
| margin-bottom: 4px; | |
| color: var(--ink); | |
| font-family: var(--font-mono); | |
| font-size: 17px; | |
| line-height: 1; | |
| letter-spacing: 0.02em; | |
| } | |
| .data-scope-detail { | |
| display: grid; | |
| grid-template-columns: minmax(0, 1.05fr) minmax(280px, 0.95fr); | |
| gap: 18px; | |
| align-items: stretch; | |
| margin-top: 16px; | |
| border: 1px solid rgba(204, 255, 160, 0.22); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(90deg, rgba(204, 255, 160, 0.095), rgba(2, 5, 2, 0.50) 52%, rgba(103, 232, 209, 0.055)), | |
| rgba(2, 5, 2, 0.70); | |
| padding: 18px; | |
| } | |
| .data-scope-detail small { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 800; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .data-scope-detail h3 { | |
| margin: 7px 0 8px; | |
| color: var(--ink); | |
| font-size: clamp(24px, 2.5vw, 38px); | |
| line-height: 1.02; | |
| text-wrap: balance; | |
| } | |
| .data-scope-detail p { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 16px; | |
| line-height: 1.56; | |
| } | |
| .data-scope-facts { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 10px; | |
| } | |
| .data-scope-facts span { | |
| border: 1px solid rgba(204, 255, 160, 0.15); | |
| border-radius: 13px; | |
| background: rgba(2, 5, 2, 0.54); | |
| padding: 12px; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.35; | |
| } | |
| .data-scope-facts strong { | |
| display: block; | |
| margin-bottom: 5px; | |
| color: var(--ink); | |
| font-family: var(--font-mono); | |
| font-size: 17px; | |
| line-height: 1.05; | |
| } | |
| .data-scope-flow { | |
| display: grid; | |
| grid-template-columns: minmax(0, 1fr) auto minmax(0, 1fr) auto minmax(0, 1fr); | |
| gap: 10px; | |
| align-items: stretch; | |
| margin-top: 14px; | |
| } | |
| .data-scope-flow-item { | |
| border: 1px solid rgba(204, 255, 160, 0.16); | |
| border-radius: 14px; | |
| background: rgba(2, 5, 2, 0.52); | |
| padding: 12px; | |
| min-height: 84px; | |
| } | |
| .data-scope-flow-item strong { | |
| display: block; | |
| color: var(--ink); | |
| font-size: 15px; | |
| line-height: 1.15; | |
| } | |
| .data-scope-flow-item span { | |
| display: block; | |
| margin-top: 6px; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.35; | |
| } | |
| .data-scope-flow-arrow { | |
| align-self: center; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 18px; | |
| font-weight: 900; | |
| opacity: 0.82; | |
| } | |
| .data-analysis-table { | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: var(--radius); | |
| overflow: hidden; | |
| background: rgba(2, 5, 2, 0.64); | |
| margin: 18px 0 24px; | |
| } | |
| .data-analysis-table-header { | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.8fr) minmax(280px, 1fr); | |
| gap: 20px; | |
| padding: 18px; | |
| border-bottom: 1px solid rgba(204, 255, 160, 0.16); | |
| align-items: end; | |
| } | |
| .data-analysis-table-header h3 { | |
| margin: 6px 0 0; | |
| font-size: 24px; | |
| line-height: 1.08; | |
| } | |
| .data-analysis-table-header p { | |
| margin: 0; | |
| color: var(--muted); | |
| line-height: 1.55; | |
| } | |
| .data-analysis-table .line-table { | |
| margin: 0; | |
| border: 0; | |
| border-radius: 0; | |
| background: transparent; | |
| } | |
| .data-analysis-chart-grid { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 18px; | |
| margin-top: 22px; | |
| } | |
| .data-chart-card { | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.04), rgba(2, 5, 2, 0.72)), | |
| rgba(7, 18, 7, 0.58); | |
| padding: 16px; | |
| display: grid; | |
| gap: 10px; | |
| position: relative; | |
| transition: border-color 160ms ease, box-shadow 160ms ease, transform 160ms ease, background 160ms ease; | |
| } | |
| .data-chart-card.is-scope-active { | |
| border-color: rgba(204, 255, 160, 0.72); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.08), rgba(2, 5, 2, 0.74)), | |
| rgba(7, 18, 7, 0.70); | |
| box-shadow: 0 18px 50px rgba(0, 0, 0, 0.20); | |
| transform: translateY(-2px); | |
| } | |
| .data-chart-card.wide { | |
| grid-column: 1 / -1; | |
| } | |
| .data-chart-meta { | |
| display: flex; | |
| align-items: center; | |
| justify-content: space-between; | |
| gap: 10px; | |
| } | |
| .chart-scope-chip { | |
| border: 1px solid rgba(204, 255, 160, 0.20); | |
| border-radius: 999px; | |
| background: rgba(204, 255, 160, 0.075); | |
| color: var(--muted); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 800; | |
| letter-spacing: 0.06em; | |
| padding: 5px 8px; | |
| text-transform: uppercase; | |
| white-space: nowrap; | |
| } | |
| .data-chart-card h3 { | |
| margin: 0; | |
| color: var(--ink); | |
| font-size: 20px; | |
| line-height: 1.12; | |
| } | |
| .data-chart-card p { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 14px; | |
| line-height: 1.45; | |
| } | |
| .data-chart-card img { | |
| display: block; | |
| width: 100%; | |
| min-height: 360px; | |
| border-radius: 12px; | |
| border: 1px solid rgba(204, 255, 160, 0.12); | |
| background: #020502; | |
| object-fit: contain; | |
| } | |
| .data-chart-card.is-scope-active img { | |
| border-color: rgba(204, 255, 160, 0.28); | |
| } | |
| .data-chart-card.wide img { | |
| min-height: 430px; | |
| } | |
| .data-analysis-actions { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 10px; | |
| margin-top: 20px; | |
| } | |
| .data-analysis-note { | |
| margin: 16px 0 0; | |
| border-left: 3px solid rgba(204, 255, 160, 0.72); | |
| padding: 12px 14px; | |
| color: var(--muted); | |
| background: rgba(204, 255, 160, 0.055); | |
| line-height: 1.55; | |
| } | |
| .source-map-grid { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 14px; | |
| margin-bottom: 18px; | |
| } | |
| .source-map-card { | |
| border: 1px solid rgba(204, 255, 160, 0.20); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.055), rgba(7, 18, 7, 0.70)), | |
| rgba(2, 5, 2, 0.72); | |
| padding: 16px; | |
| display: grid; | |
| gap: 10px; | |
| min-height: 190px; | |
| align-content: start; | |
| } | |
| .source-map-card small, | |
| .source-map-legend small, | |
| .source-map-caption strong { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 820; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .source-map-card h3 { | |
| margin: 0; | |
| color: var(--ink); | |
| font-size: 19px; | |
| line-height: 1.12; | |
| } | |
| .source-map-card p { | |
| margin: 0; | |
| color: var(--text-soft); | |
| font-size: 13.5px; | |
| line-height: 1.55; | |
| } | |
| .source-map-card a, | |
| .source-map-caption a { | |
| color: var(--cyan); | |
| text-decoration: none; | |
| border-bottom: 1px solid rgba(122, 229, 195, 0.30); | |
| width: fit-content; | |
| } | |
| .source-map-panel { | |
| border: 1px solid rgba(204, 255, 160, 0.22); | |
| border-radius: var(--radius); | |
| background: | |
| radial-gradient(circle at 12% 8%, rgba(122, 229, 195, 0.09), transparent 24%), | |
| rgba(5, 10, 6, 0.82); | |
| padding: 16px; | |
| display: grid; | |
| gap: 14px; | |
| overflow: hidden; | |
| } | |
| .source-map-legend { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 10px; | |
| } | |
| .source-map-legend article { | |
| border: 1px solid rgba(204, 255, 160, 0.14); | |
| border-radius: 7px; | |
| background: rgba(255, 255, 255, 0.035); | |
| padding: 12px; | |
| display: grid; | |
| gap: 6px; | |
| } | |
| .source-map-legend p { | |
| margin: 0; | |
| color: var(--text-soft); | |
| font-size: 12.5px; | |
| line-height: 1.45; | |
| } | |
| .source-map-chart-frame { | |
| margin: 0; | |
| overflow-x: auto; | |
| overflow-y: hidden; | |
| border-radius: var(--radius); | |
| scrollbar-color: rgba(204, 255, 160, 0.45) rgba(204, 255, 160, 0.10); | |
| } | |
| .source-map-chart-frame .chart { | |
| min-width: 920px; | |
| box-shadow: none; | |
| } | |
| .source-map-caption { | |
| display: flex; | |
| justify-content: space-between; | |
| gap: 16px; | |
| align-items: start; | |
| color: var(--muted); | |
| font-size: 12.5px; | |
| line-height: 1.45; | |
| } | |
| .source-map-caption p { | |
| margin: 0; | |
| max-width: 760px; | |
| } | |
| .direction-grid { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 16px; | |
| margin-bottom: 24px; | |
| } | |
| .direction-card { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| background: var(--surface); | |
| padding: 18px; | |
| min-height: 230px; | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| } | |
| .direction-card h3 { margin: 0; font-size: 16px; line-height: 1.25; } | |
| .direction-card p { margin: 0; color: var(--muted); font-size: 13px; line-height: 1.55; } | |
| .status-pill { | |
| width: fit-content; | |
| border-radius: 999px; | |
| padding: 4px 9px; | |
| background: rgba(204, 255, 160, 0.10); | |
| color: var(--green); | |
| font-size: 11px; | |
| font-weight: 700; | |
| } | |
| .direction-counts { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 8px; | |
| font-size: 12px; | |
| color: #bcc8b7; | |
| } | |
| .direction-counts strong { | |
| display: block; | |
| font-family: var(--font-mono); | |
| font-size: 18px; | |
| color: var(--ink); | |
| } | |
| .baseline-strip { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 14px; | |
| margin-top: 18px; | |
| } | |
| .extension-grid { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 16px; | |
| margin: 22px 0 24px; | |
| } | |
| .extension-card { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| background: var(--surface); | |
| padding: 18px; | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| min-height: 282px; | |
| } | |
| .extension-card h3 { margin: 0; font-size: 15px; line-height: 1.3; } | |
| .extension-card p { margin: 0; color: var(--muted); font-size: 13px; line-height: 1.55; } | |
| .extension-metrics { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 8px; | |
| font-size: 12px; | |
| color: var(--muted); | |
| } | |
| .extension-metrics strong { | |
| display: block; | |
| font-family: var(--font-mono); | |
| font-size: 18px; | |
| color: var(--ink); | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .task-player { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| display: grid; | |
| grid-template-columns: minmax(0, 1.05fr) minmax(360px, 0.95fr); | |
| gap: 24px; | |
| padding: clamp(18px, 2.4vw, 28px); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.07), rgba(7, 18, 7, 0.88)), | |
| var(--surface); | |
| box-shadow: 0 20px 58px rgba(0, 0, 0, 0.32); | |
| } | |
| .player-stage, | |
| .player-copy { | |
| min-width: 0; | |
| } | |
| .player-screen { | |
| position: relative; | |
| overflow: hidden; | |
| border: 1px solid rgba(204, 255, 160, 0.22); | |
| border-radius: var(--radius); | |
| background: #020502; | |
| aspect-ratio: 16 / 10; | |
| } | |
| .player-screen img { | |
| width: 100%; | |
| height: 100%; | |
| object-fit: cover; | |
| display: block; | |
| } | |
| .player-badge { | |
| position: absolute; | |
| left: 14px; | |
| bottom: 14px; | |
| max-width: calc(100% - 28px); | |
| border: 1px solid rgba(204, 255, 160, 0.42); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.78); | |
| color: #f4f8ef; | |
| padding: 10px 12px; | |
| backdrop-filter: blur(12px); | |
| display: grid; | |
| grid-template-columns: 44px minmax(0, 1fr); | |
| gap: 9px 10px; | |
| align-items: center; | |
| } | |
| .player-task-icon { | |
| width: 44px; | |
| height: 44px; | |
| grid-row: 1 / span 2; | |
| border: 1px solid rgba(204, 255, 160, 0.30); | |
| border-radius: 10px; | |
| background: rgba(2, 5, 2, 0.72); | |
| } | |
| .player-badge strong { | |
| display: block; | |
| grid-column: 2; | |
| font-family: var(--font-ui); | |
| font-size: clamp(20px, 2.4vw, 34px); | |
| line-height: 1.08; | |
| text-wrap: balance; | |
| word-spacing: 0.06em; | |
| } | |
| .player-badge span { | |
| display: block; | |
| grid-column: 2; | |
| margin-top: 3px; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 12px; | |
| } | |
| .player-frame-chip { | |
| position: absolute; | |
| left: 14px; | |
| top: 14px; | |
| border: 1px solid rgba(204, 255, 160, 0.36); | |
| border-radius: 999px; | |
| background: rgba(2, 5, 2, 0.74); | |
| color: #dce8d6; | |
| padding: 7px 10px; | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 700; | |
| letter-spacing: 0.04em; | |
| text-transform: uppercase; | |
| backdrop-filter: blur(12px); | |
| } | |
| .player-frame-caption { | |
| margin: 12px 0 0; | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.48); | |
| color: #dce8d6; | |
| padding: 11px 12px; | |
| font-size: 13px; | |
| line-height: 1.5; | |
| } | |
| .player-controls { | |
| display: flex; | |
| align-items: center; | |
| gap: 9px; | |
| flex-wrap: wrap; | |
| margin-top: 14px; | |
| } | |
| .player-controls button { | |
| border: 1px solid var(--line); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.62); | |
| color: #eaf5e5; | |
| min-height: 38px; | |
| padding: 0 12px; | |
| font: inherit; | |
| font-size: 13px; | |
| font-weight: 700; | |
| cursor: pointer; | |
| } | |
| .player-controls button.primary-control { | |
| background: var(--green); | |
| color: #020502; | |
| border-color: var(--green); | |
| } | |
| .player-counter { | |
| margin-left: auto; | |
| color: var(--muted); | |
| font-family: var(--font-mono); | |
| font-size: 12px; | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .player-progress { | |
| height: 6px; | |
| margin-top: 12px; | |
| border-radius: 999px; | |
| background: rgba(204, 255, 160, 0.12); | |
| overflow: hidden; | |
| } | |
| .player-progress span { | |
| display: block; | |
| width: 0; | |
| height: 100%; | |
| border-radius: inherit; | |
| background: linear-gradient(90deg, var(--green), var(--cyan)); | |
| transition: width 260ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .task-scrubber { | |
| flex: 1 1 210px; | |
| min-width: 180px; | |
| accent-color: var(--green); | |
| cursor: pointer; | |
| } | |
| .storyboard-steps { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 8px; | |
| margin-top: 12px; | |
| } | |
| .story-button { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.52); | |
| color: #dce8d6; | |
| min-height: 48px; | |
| padding: 9px 8px; | |
| font: inherit; | |
| text-align: left; | |
| cursor: pointer; | |
| } | |
| .story-button strong { | |
| display: block; | |
| color: var(--ink); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| text-transform: uppercase; | |
| letter-spacing: 0.04em; | |
| } | |
| .story-button span { | |
| display: block; | |
| margin-top: 4px; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.25; | |
| } | |
| .story-button.active { | |
| border-color: rgba(204, 255, 160, 0.72); | |
| background: rgba(204, 255, 160, 0.12); | |
| } | |
| .modality-strip { | |
| display: grid; | |
| grid-template-columns: repeat(auto-fit, minmax(96px, 1fr)); | |
| gap: 9px; | |
| margin-top: 12px; | |
| } | |
| .modality-tile { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.48); | |
| overflow: hidden; | |
| min-width: 0; | |
| } | |
| .modality-tile img { | |
| width: 100%; | |
| aspect-ratio: 16 / 9; | |
| object-fit: cover; | |
| display: block; | |
| background: #020502; | |
| } | |
| .modality-tile span { | |
| display: block; | |
| padding: 7px 8px; | |
| color: #dce8d6; | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| line-height: 1.2; | |
| text-transform: uppercase; | |
| } | |
| .player-copy { | |
| display: grid; | |
| gap: 18px; | |
| align-content: start; | |
| } | |
| .player-kicker { | |
| display: flex; | |
| align-items: center; | |
| gap: 10px; | |
| flex-wrap: wrap; | |
| } | |
| .player-copy h3 { | |
| margin: 0; | |
| font-family: var(--font-ui); | |
| font-size: clamp(30px, 3.4vw, 48px); | |
| line-height: 1.02; | |
| text-wrap: balance; | |
| } | |
| .player-copy p { | |
| margin: 0; | |
| color: var(--muted); | |
| line-height: 1.62; | |
| } | |
| .player-case { | |
| color: #eaf5e5; | |
| font-size: 16px; | |
| } | |
| .flow-steps { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 10px; | |
| } | |
| .flow-step, | |
| .module-list li { | |
| border: 1px solid var(--soft-line); | |
| background: rgba(204, 255, 160, 0.06); | |
| border-radius: 6px; | |
| padding: 10px; | |
| min-width: 0; | |
| } | |
| .flow-step { | |
| color: inherit; | |
| font: inherit; | |
| text-align: left; | |
| cursor: pointer; | |
| } | |
| .flow-step.active { | |
| border-color: rgba(204, 255, 160, 0.72); | |
| background: rgba(204, 255, 160, 0.12); | |
| } | |
| .flow-step strong, | |
| .module-list strong { | |
| display: block; | |
| margin-bottom: 5px; | |
| color: var(--ink); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| text-transform: uppercase; | |
| letter-spacing: 0.04em; | |
| } | |
| .flow-step em { | |
| display: block; | |
| color: #dce8d6; | |
| font-style: normal; | |
| line-height: 1.38; | |
| } | |
| .module-list { | |
| display: grid; | |
| gap: 8px; | |
| margin: 0; | |
| padding: 0; | |
| list-style: none; | |
| color: #dce8d6; | |
| font-size: 13px; | |
| line-height: 1.45; | |
| } | |
| .task-selector { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 10px; | |
| margin-top: 18px; | |
| } | |
| .selector-button { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.54); | |
| color: #dce8d6; | |
| font: inherit; | |
| min-height: 64px; | |
| padding: 10px; | |
| text-align: left; | |
| cursor: pointer; | |
| display: grid; | |
| grid-template-columns: 34px minmax(0, 1fr); | |
| gap: 2px 9px; | |
| align-items: center; | |
| } | |
| .selector-task-icon { | |
| width: 34px; | |
| height: 34px; | |
| grid-row: 1 / span 2; | |
| border-radius: 8px; | |
| } | |
| .selector-button strong { | |
| display: block; | |
| grid-column: 2; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 14px; | |
| line-height: 1.15; | |
| } | |
| .selector-button span { | |
| display: block; | |
| grid-column: 2; | |
| margin-top: 4px; | |
| color: var(--muted); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| text-transform: uppercase; | |
| } | |
| .selector-button.active { | |
| border-color: rgba(204, 255, 160, 0.72); | |
| background: rgba(204, 255, 160, 0.12); | |
| } | |
| .task-icon-atlas { | |
| display: grid; | |
| grid-template-columns: minmax(260px, 0.55fr) minmax(0, 1fr); | |
| gap: 18px; | |
| align-items: center; | |
| margin-bottom: 18px; | |
| border: 1px solid rgba(204, 255, 160, 0.20); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(135deg, rgba(204, 255, 160, 0.08), rgba(122, 229, 195, 0.04)), | |
| rgba(7, 18, 7, 0.58); | |
| padding: clamp(16px, 2vw, 22px); | |
| } | |
| .task-icon-atlas h3 { | |
| margin: 0 0 8px; | |
| font-size: 20px; | |
| line-height: 1.18; | |
| } | |
| .task-icon-atlas p { | |
| margin: 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.55; | |
| } | |
| .task-icon-atlas img { | |
| width: 100%; | |
| max-height: 320px; | |
| object-fit: contain; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: 10px; | |
| background: #020502; | |
| } | |
| .walk-flow { | |
| display: grid; | |
| grid-template-columns: 0.72fr 1.15fr 0.72fr; | |
| gap: 8px; | |
| align-items: stretch; | |
| font-size: 12px; | |
| } | |
| .walk-flow span { | |
| border: 1px solid var(--soft-line); | |
| background: rgba(204, 255, 160, 0.06); | |
| border-radius: 6px; | |
| padding: 9px; | |
| min-height: 58px; | |
| } | |
| .walk-flow strong { display: block; color: var(--ink); font-size: 11px; margin-bottom: 4px; text-transform: uppercase; letter-spacing: 0.04em; } | |
| .artifact { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| background: var(--surface); | |
| padding: 18px; | |
| min-height: 164px; | |
| display: grid; | |
| align-content: start; | |
| transition: transform 240ms cubic-bezier(0.16, 1, 0.3, 1), border-color 240ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .artifact.primary-artifact { | |
| grid-column: 1 / -1; | |
| grid-template-columns: minmax(0, 1fr) auto; | |
| gap: 18px; | |
| align-items: end; | |
| min-height: 0; | |
| background: | |
| linear-gradient(120deg, rgba(204, 255, 160, 0.13), rgba(122, 229, 195, 0.05)), | |
| var(--surface); | |
| } | |
| .artifact:hover { transform: translateY(-3px); border-color: var(--green); } | |
| .artifact h3 { line-height: 1.18; } | |
| .artifact a { display: inline-block; margin-top: 14px; font-weight: 700; text-decoration: none; color: var(--blue); } | |
| .artifact a:hover { text-decoration: underline; text-underline-offset: 4px; } | |
| .repro-note { | |
| margin: 0 0 18px; | |
| color: var(--muted); | |
| font-size: 14px; | |
| line-height: 1.65; | |
| } | |
| .code-panel { | |
| background: #000; | |
| color: #dff7d4; | |
| border-radius: var(--radius); | |
| padding: 18px; | |
| overflow: auto; | |
| font-family: var(--font-mono); | |
| font-size: 13px; | |
| line-height: 1.65; | |
| border: 1px solid rgba(204, 255, 160, 0.24); | |
| } | |
| .code-panel button { | |
| float: right; | |
| margin-left: 16px; | |
| height: 30px; | |
| border: 1px solid rgba(204, 255, 160, 0.36); | |
| color: #020502; | |
| background: var(--green); | |
| border-radius: 5px; | |
| cursor: pointer; | |
| font-weight: 700; | |
| } | |
| footer { | |
| padding: 42px 0; | |
| color: var(--muted); | |
| font-size: 14px; | |
| } | |
| .footer-meta { | |
| display: block; | |
| margin-top: 8px; | |
| color: rgba(244, 248, 239, 0.62); | |
| font-size: 13px; | |
| } | |
| .footer-meta a { | |
| color: var(--cyan); | |
| text-decoration: none; | |
| border-bottom: 1px solid rgba(122, 229, 195, 0.28); | |
| } | |
| /* Ropedia component alignment layer */ | |
| main > section { | |
| background: #020502; | |
| } | |
| main > section:nth-of-type(2n + 1) { | |
| background: #05060b; | |
| } | |
| .section-head { | |
| padding-top: 24px; | |
| border-top: 1px solid rgba(255, 255, 255, 0.16); | |
| } | |
| .section-head h2 { | |
| color: #f5f7f0; | |
| } | |
| .section-head p, | |
| .hero-copy, | |
| .article-copy { | |
| color: rgba(245, 247, 240, 0.76); | |
| } | |
| .eyebrow, | |
| .roadmap-status, | |
| .artifact-group-head span, | |
| .brief-panel-head span, | |
| .task-contract strong, | |
| .walk-flow strong, | |
| .flow-step strong, | |
| .module-list strong, | |
| .modality-tile span, | |
| .selector-button span, | |
| .player-frame-chip { | |
| font-family: var(--font-btn); | |
| } | |
| .project-tab, | |
| .content-tab, | |
| .section-tab, | |
| .filter, | |
| .snapshot-actions a, | |
| .reading-links a, | |
| .roadmap-links a, | |
| .brief-actions a, | |
| .evidence-links a, | |
| .player-controls button, | |
| .story-button, | |
| .selector-button, | |
| .code-panel button { | |
| border-radius: 999px; | |
| font-family: var(--font-btn); | |
| } | |
| .project-tab, | |
| .content-tab, | |
| .section-tab, | |
| .filter, | |
| .player-controls button, | |
| .story-button, | |
| .selector-button { | |
| background: var(--ropedia-pill); | |
| border-color: rgba(255, 255, 255, 0.12); | |
| } | |
| .project-tab:hover, | |
| .content-tab:hover, | |
| .section-tab:hover, | |
| .filter:hover, | |
| .player-controls button:hover, | |
| .story-button:hover, | |
| .selector-button:hover { | |
| border-color: var(--green); | |
| color: var(--green); | |
| background: rgba(255, 255, 255, 0.08); | |
| } | |
| .project-tab.active, | |
| .content-tab.active, | |
| .section-tab.active, | |
| .filter.active, | |
| .story-button.active, | |
| .selector-button.active { | |
| border-color: var(--green); | |
| color: #020502; | |
| background: var(--green); | |
| box-shadow: none; | |
| } | |
| .project-tab.active strong, | |
| .project-tab.active span, | |
| .content-tab.active strong, | |
| .content-tab.active span, | |
| .selector-button.active strong, | |
| .selector-button.active span { | |
| color: #020502; | |
| } | |
| .hero-panel, | |
| .snapshot-card, | |
| .roadmap-card, | |
| .brief-panel, | |
| .brief-card, | |
| .reading-card, | |
| .boundary-item, | |
| .evidence-card, | |
| .model, | |
| .task-card, | |
| .artifact-group, | |
| .direction-card, | |
| .extension-card, | |
| .task-player, | |
| .artifact, | |
| .callout { | |
| border-color: rgba(204, 255, 160, 0.18); | |
| background: var(--ropedia-card); | |
| } | |
| .hero-panel, | |
| .brief-panel, | |
| .task-player, | |
| .artifact.primary-artifact { | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.07), rgba(5, 10, 6, 0.88)), | |
| var(--ropedia-card-strong); | |
| } | |
| .section-head, | |
| .brief-panel-head, | |
| .artifact-group-head, | |
| .roadmap-resource-head, | |
| .result-subsection-head, | |
| .direction-subsection-head, | |
| .task-suite-subhead, | |
| .suite-task-index-head { | |
| align-items: start; | |
| } | |
| .brief-panel, | |
| .artifact-group, | |
| .result-subsection, | |
| .direction-subsection, | |
| .suite-task-cards-block, | |
| .suite-task-index { | |
| box-shadow: 0 18px 48px rgba(0, 0, 0, 0.22); | |
| } | |
| .brief-panel + .brief-panel, | |
| .artifact-group + .artifact-group, | |
| .result-subsection + .result-subsection, | |
| .direction-subsection + .direction-subsection { | |
| margin-top: clamp(20px, 2.6vw, 36px); | |
| } | |
| .hero-paths, | |
| .suite-lines, | |
| .hero-stats, | |
| .brief-grid, | |
| .snapshot-grid, | |
| .roadmap-grid, | |
| .artifact-grid, | |
| .evidence-grid, | |
| .reading-grid, | |
| .resource-mode-grid, | |
| .task-grid, | |
| .models, | |
| .direction-grid, | |
| .extension-grid { | |
| align-items: stretch; | |
| } | |
| .hero-path, | |
| .suite-line-card, | |
| .stat, | |
| .brief-card, | |
| .snapshot-card, | |
| .roadmap-card, | |
| .artifact, | |
| .evidence-card, | |
| .reading-card, | |
| .resource-mode, | |
| .task-card, | |
| .model, | |
| .direction-card, | |
| .extension-card { | |
| height: 100%; | |
| } | |
| .section-head p, | |
| .brief-panel-head p, | |
| .artifact-group-head p, | |
| .roadmap-resource-head p, | |
| .result-subsection-head p, | |
| .direction-subsection-head p, | |
| .task-suite-subhead p, | |
| .suite-task-index-head p { | |
| padding-top: 2px; | |
| } | |
| .snapshot-card:hover, | |
| .roadmap-card:hover, | |
| .reading-card:hover, | |
| .evidence-card:hover, | |
| .model:hover, | |
| .task-card:hover, | |
| .direction-card:hover, | |
| .extension-card:hover, | |
| .artifact:hover { | |
| border-color: var(--green); | |
| background: rgba(255, 255, 255, 0.06); | |
| transform: translateY(-2px); | |
| box-shadow: none; | |
| } | |
| .tag, | |
| .status-pill, | |
| .roadmap-status, | |
| .player-frame-chip, | |
| .frame-pill, | |
| .player-badge, | |
| .player-frame-caption, | |
| .flow-step, | |
| .module-list li, | |
| .walk-flow span, | |
| .modality-tile, | |
| .signal code { | |
| border-color: rgba(204, 255, 160, 0.18); | |
| background: rgba(255, 255, 255, 0.05); | |
| } | |
| .tag, | |
| .status-pill, | |
| .roadmap-status { | |
| color: var(--green); | |
| } | |
| .reading-card .step-index, | |
| .button.primary, | |
| .brief-actions a:first-child, | |
| .player-controls button.primary-control, | |
| .code-panel button { | |
| background: var(--green); | |
| border-color: var(--green); | |
| color: #020502; | |
| } | |
| .track, | |
| .player-progress { | |
| background: rgba(204, 255, 160, 0.16); | |
| } | |
| .track > span, | |
| .roadmap-card::before, | |
| .player-progress span, | |
| .bar span { | |
| background: linear-gradient(90deg, var(--c, var(--green)), rgba(204, 255, 160, 0.45)); | |
| } | |
| /* Typography and color consistency layer for repeated long-page components. */ | |
| .button, | |
| .nav-action, | |
| .section-tab, | |
| .project-tab, | |
| .content-tab, | |
| .filter, | |
| .story-button, | |
| .selector-button, | |
| .brief-actions a, | |
| .reading-links a, | |
| .evidence-links a, | |
| .roadmap-links a, | |
| .player-controls button, | |
| .code-panel button, | |
| .surface-map a, | |
| .glossary-links a, | |
| .result-matrix-actions a, | |
| .split-radar-links a, | |
| .raw-link-row a { | |
| font-family: var(--font-btn); | |
| font-weight: 720; | |
| letter-spacing: 0; | |
| } | |
| .snapshot-card h3, | |
| .roadmap-card h3, | |
| .brief-card strong, | |
| .reading-card h3, | |
| .boundary-item strong, | |
| .evidence-card h3, | |
| .model h3, | |
| .task-card h3, | |
| .direction-card h3, | |
| .extension-card h3, | |
| .artifact h3, | |
| .callout h3, | |
| .figure-brief-card h3, | |
| .suite-line-card h3, | |
| .raw-detail-card h3, | |
| .resource-mode strong, | |
| .surface-map a strong { | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-weight: 740; | |
| letter-spacing: 0; | |
| } | |
| .snapshot-card p, | |
| .roadmap-card p, | |
| .brief-card p, | |
| .brief-card li, | |
| .reading-card p, | |
| .evidence-card p, | |
| .task-card p, | |
| .direction-card p, | |
| .extension-card p, | |
| .artifact p, | |
| .callout p, | |
| .figure-brief-card p, | |
| .suite-line-card p, | |
| .raw-detail-card p, | |
| .resource-mode p, | |
| .surface-map a span { | |
| color: var(--muted); | |
| font-size: var(--type-body-sm); | |
| line-height: 1.55; | |
| } | |
| .eyebrow, | |
| .roadmap-status, | |
| .status-pill, | |
| .tag, | |
| .player-frame-chip, | |
| .player-badge, | |
| .line-table td[data-label]::before, | |
| .method-summary-table th, | |
| .result-score-table th, | |
| .glossary-table th { | |
| font-family: var(--font-mono); | |
| font-size: var(--type-xs); | |
| font-weight: 800; | |
| letter-spacing: 0.06em; | |
| text-transform: uppercase; | |
| } | |
| .line-table, | |
| .method-summary-table, | |
| .result-score-table, | |
| .glossary-table { | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .score-chip.proxy em { | |
| color: var(--proxy-ink); | |
| background: var(--proxy); | |
| } | |
| .project-tab strong, | |
| .content-tab strong { | |
| font-weight: 760; | |
| } | |
| .project-tab span, | |
| .content-tab span { | |
| font-family: var(--font-copy); | |
| font-weight: 500; | |
| } | |
| .term-help { | |
| color: var(--green); | |
| font-size: var(--type-micro); | |
| line-height: 1; | |
| } | |
| .result-matrix-stat .term-help, | |
| .matrix-legend-pill .term-help, | |
| .method-cell .term-help, | |
| .task-header .term-help, | |
| .method-summary-table th .term-help, | |
| .result-score-table th .term-help { | |
| color: var(--green); | |
| font-size: var(--type-micro); | |
| line-height: 1; | |
| } | |
| .term-tooltip, | |
| .result-matrix-stat .term-tooltip, | |
| .matrix-legend-pill .term-tooltip, | |
| .method-cell .term-tooltip, | |
| .task-header .term-tooltip, | |
| .method-summary-table th .term-tooltip, | |
| .result-score-table th .term-tooltip { | |
| color: var(--text); | |
| font-family: var(--font-copy); | |
| font-size: var(--type-sm); | |
| font-weight: 620; | |
| line-height: 1.4; | |
| } | |
| /* Product-design polish: align with ropedia.com through real project visuals, | |
| restrained surfaces, and a clear first-viewport research story. */ | |
| body { | |
| background: #020502; | |
| } | |
| .hero { | |
| position: relative; | |
| overflow: hidden; | |
| min-height: min(760px, calc(100vh - 24px)); | |
| background: #020502; | |
| isolation: isolate; | |
| } | |
| .hero::before { | |
| content: ""; | |
| position: absolute; | |
| inset: 0; | |
| z-index: -2; | |
| background: | |
| linear-gradient(90deg, rgba(2, 5, 2, 0.96) 0%, rgba(2, 5, 2, 0.86) 42%, rgba(2, 5, 2, 0.58) 72%, rgba(2, 5, 2, 0.88) 100%), | |
| linear-gradient(180deg, rgba(2, 5, 2, 0.10) 0%, rgba(2, 5, 2, 0.82) 100%), | |
| url("assets/task_suite_infographic.png?v=xperience10m-taskfirst-v14-modality-compact") center right / cover no-repeat; | |
| opacity: 0.92; | |
| } | |
| .hero::after { | |
| content: ""; | |
| position: absolute; | |
| inset: auto 0 0; | |
| z-index: -1; | |
| height: 42%; | |
| background: linear-gradient(180deg, transparent, #020502 88%); | |
| pointer-events: none; | |
| } | |
| .hero-inner { | |
| position: relative; | |
| min-height: min(760px, calc(100vh - 24px)); | |
| grid-template-columns: minmax(0, 1fr); | |
| align-items: end; | |
| padding: 104px 0 72px; | |
| } | |
| .hero-inner::before { | |
| content: ""; | |
| position: absolute; | |
| inset: 88px 0 auto; | |
| height: 1px; | |
| background: linear-gradient(90deg, transparent, rgba(204, 255, 160, 0.58), rgba(122, 229, 195, 0.44), transparent); | |
| opacity: 0.58; | |
| transform-origin: left center; | |
| pointer-events: none; | |
| } | |
| .hero-inner > div:first-child { | |
| max-width: 980px; | |
| } | |
| .hero h1 { | |
| max-width: 940px; | |
| color: #f7fff0; | |
| } | |
| .hero-copy { | |
| max-width: 760px; | |
| color: rgba(245, 247, 240, 0.78); | |
| } | |
| .hero-stats { | |
| max-width: 820px; | |
| } | |
| .hero-panel { | |
| max-width: 1060px; | |
| margin-top: 34px; | |
| display: grid; | |
| grid-template-columns: repeat(7, minmax(96px, 1fr)); | |
| gap: 0; | |
| padding: 0; | |
| overflow: hidden; | |
| border-color: rgba(245, 247, 240, 0.18); | |
| background: rgba(2, 5, 2, 0.74); | |
| box-shadow: none; | |
| backdrop-filter: blur(10px); | |
| } | |
| .panel-top { | |
| grid-column: 1 / -1; | |
| padding: 12px 14px; | |
| border-bottom-color: rgba(245, 247, 240, 0.14); | |
| color: rgba(245, 247, 240, 0.66); | |
| } | |
| .signal { | |
| grid-template-columns: 1fr; | |
| gap: 9px; | |
| align-items: start; | |
| padding: 13px 14px 14px; | |
| border-right: 1px solid rgba(245, 247, 240, 0.10); | |
| border-bottom: 0; | |
| } | |
| .signal:last-child { | |
| border-right: 0; | |
| } | |
| .signal code { | |
| width: fit-content; | |
| color: rgba(245, 247, 240, 0.86); | |
| } | |
| .signal strong { | |
| text-align: left; | |
| color: #f7fff0; | |
| } | |
| .track { | |
| width: 100%; | |
| height: 6px; | |
| } | |
| .hero-radar-panel { | |
| position: relative; | |
| max-width: 1180px; | |
| margin-top: 34px; | |
| border: 1px solid rgba(245, 247, 240, 0.18); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(204, 255, 160, 0.07), rgba(5, 10, 6, 0.88)), | |
| rgba(2, 5, 2, 0.78); | |
| box-shadow: none; | |
| backdrop-filter: blur(12px); | |
| padding: 16px; | |
| overflow: hidden; | |
| } | |
| .hero-radar-panel::before { | |
| content: ""; | |
| position: absolute; | |
| inset: 0; | |
| pointer-events: none; | |
| background: | |
| linear-gradient(115deg, transparent 0 34%, rgba(204, 255, 160, 0.08) 45%, transparent 56%), | |
| linear-gradient(90deg, rgba(204, 255, 160, 0.12), transparent 24%, transparent 76%, rgba(122, 229, 195, 0.10)); | |
| opacity: 0.72; | |
| mix-blend-mode: screen; | |
| transform: translateX(-16%); | |
| } | |
| .hero-radar-top { | |
| display: flex; | |
| align-items: center; | |
| justify-content: space-between; | |
| gap: 14px; | |
| padding: 2px 2px 14px; | |
| border-bottom: 1px solid rgba(245, 247, 240, 0.12); | |
| color: rgba(245, 247, 240, 0.68); | |
| font-family: var(--font-mono); | |
| font-size: 12px; | |
| text-transform: uppercase; | |
| letter-spacing: 0.04em; | |
| } | |
| .hero-radar-top strong { | |
| color: var(--green); | |
| font-weight: 800; | |
| } | |
| .hero-radar-layout { | |
| display: grid; | |
| grid-template-columns: minmax(0, 1.18fr) minmax(330px, 0.82fr); | |
| gap: 18px; | |
| align-items: stretch; | |
| padding-top: 16px; | |
| } | |
| .hero-radar-frame { | |
| position: relative; | |
| display: block; | |
| min-width: 0; | |
| border: 1px solid rgba(204, 255, 160, 0.16); | |
| border-radius: 6px; | |
| background: #020502; | |
| overflow: hidden; | |
| text-decoration: none; | |
| transition: transform 260ms var(--motion-ease), border-color 260ms var(--motion-ease), box-shadow 260ms var(--motion-ease); | |
| } | |
| .hero-radar-frame::after { | |
| content: ""; | |
| position: absolute; | |
| inset: 0; | |
| pointer-events: none; | |
| background: linear-gradient(110deg, transparent 0 38%, rgba(204, 255, 160, 0.12) 48%, transparent 58%); | |
| opacity: 0; | |
| transform: translateX(-80%); | |
| } | |
| .hero-radar-frame:hover { | |
| transform: translateY(-2px); | |
| border-color: rgba(204, 255, 160, 0.44); | |
| box-shadow: 0 22px 52px rgba(0, 0, 0, 0.32); | |
| } | |
| .hero-radar-frame:hover::after { | |
| opacity: 1; | |
| animation: xperienceSheen 980ms var(--motion-ease); | |
| } | |
| .hero-radar-frame img { | |
| display: block; | |
| width: 100%; | |
| height: min(430px, 44vh); | |
| min-height: 300px; | |
| object-fit: contain; | |
| background: #020502; | |
| transition: transform 380ms var(--motion-ease), filter 380ms var(--motion-ease); | |
| } | |
| .hero-radar-frame:hover img { | |
| transform: scale(1.012); | |
| filter: saturate(1.05) contrast(1.04); | |
| } | |
| .hero-radar-copy { | |
| min-width: 0; | |
| display: flex; | |
| flex-direction: column; | |
| gap: 13px; | |
| } | |
| .hero-radar-copy h2 { | |
| margin: 0; | |
| color: #f7fff0; | |
| font-family: var(--font-ui); | |
| font-size: clamp(22px, 2.1vw, 32px); | |
| line-height: 1.04; | |
| letter-spacing: 0; | |
| overflow-wrap: anywhere; | |
| } | |
| .hero-radar-copy p { | |
| margin: 0; | |
| color: rgba(245, 247, 240, 0.72); | |
| font-size: 14px; | |
| line-height: 1.52; | |
| } | |
| .hero-radar-stats, | |
| .hero-method-list { | |
| display: grid; | |
| gap: 8px; | |
| } | |
| .hero-radar-stats { | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| } | |
| .hero-radar-stat, | |
| .hero-method { | |
| position: relative; | |
| border: 1px solid rgba(204, 255, 160, 0.14); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.54); | |
| min-width: 0; | |
| overflow: hidden; | |
| transition: transform 220ms var(--motion-ease), border-color 220ms var(--motion-ease), background 220ms var(--motion-ease); | |
| } | |
| .hero-radar-stat:hover, | |
| .hero-method:hover { | |
| transform: translateY(-1px); | |
| border-color: rgba(204, 255, 160, 0.34); | |
| background: rgba(8, 22, 8, 0.68); | |
| } | |
| .hero-radar-stat { | |
| padding: 10px; | |
| } | |
| .hero-radar-stat strong { | |
| display: block; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 15px; | |
| line-height: 1; | |
| font-variant-numeric: tabular-nums; | |
| text-shadow: 0 0 18px rgba(204, 255, 160, 0.14); | |
| } | |
| .hero-radar-stat span { | |
| display: block; | |
| margin-top: 6px; | |
| color: rgba(245, 247, 240, 0.66); | |
| font-size: 11px; | |
| line-height: 1.2; | |
| } | |
| .hero-method { | |
| display: grid; | |
| grid-template-columns: 10px minmax(0, 1fr); | |
| gap: 10px; | |
| padding: 10px; | |
| align-items: start; | |
| color: inherit; | |
| text-decoration: none; | |
| } | |
| .hero-method::before { | |
| content: ""; | |
| grid-column: 1; | |
| grid-row: 1 / span 2; | |
| width: 10px; | |
| height: 10px; | |
| margin-top: 4px; | |
| border-radius: 999px; | |
| background: var(--method-color); | |
| box-shadow: 0 0 18px color-mix(in srgb, var(--method-color), transparent 48%); | |
| } | |
| .hero-method strong, | |
| .hero-method span { | |
| grid-column: 2; | |
| } | |
| .hero-method strong { | |
| display: block; | |
| color: #f7fff0; | |
| font-family: var(--font-ui); | |
| font-size: 13px; | |
| line-height: 1.18; | |
| } | |
| .hero-method span { | |
| display: block; | |
| margin-top: 3px; | |
| color: rgba(245, 247, 240, 0.66); | |
| font-size: 11px; | |
| line-height: 1.32; | |
| } | |
| .hero-task-strip { | |
| display: grid; | |
| grid-template-columns: repeat(auto-fit, minmax(132px, 1fr)); | |
| gap: 8px; | |
| margin-top: 16px; | |
| padding-top: 14px; | |
| border-top: 1px solid rgba(245, 247, 240, 0.12); | |
| } | |
| .hero-task-strip-head { | |
| grid-column: 1 / -1; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 820; | |
| letter-spacing: 0.06em; | |
| line-height: 1.15; | |
| text-transform: uppercase; | |
| } | |
| .hero-task-strip span { | |
| min-width: 0; | |
| display: flex; | |
| align-items: center; | |
| gap: 7px; | |
| min-height: 44px; | |
| border: 1px solid rgba(245, 247, 240, 0.10); | |
| border-radius: 5px; | |
| background: rgba(255, 255, 255, 0.04); | |
| padding: 7px 8px; | |
| color: rgba(245, 247, 240, 0.80); | |
| font-size: 10.5px; | |
| line-height: 1.18; | |
| overflow-wrap: normal; | |
| word-break: normal; | |
| } | |
| .task-axis-icon { | |
| width: 26px; | |
| height: 26px; | |
| border-radius: 7px; | |
| } | |
| .hero-task-strip .task-axis-label { | |
| min-width: 0; | |
| line-height: 1.22; | |
| } | |
| .hero-radar-links { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 8px; | |
| margin-top: 12px; | |
| } | |
| .hero-radar-links a { | |
| min-height: 34px; | |
| display: inline-flex; | |
| align-items: center; | |
| border: 1px solid rgba(204, 255, 160, 0.18); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.42); | |
| color: var(--cyan); | |
| padding: 7px 10px; | |
| font-family: var(--font-btn); | |
| font-size: 12px; | |
| font-weight: 700; | |
| text-decoration: none; | |
| } | |
| .hero-radar-links a:hover { | |
| border-color: var(--green); | |
| color: var(--ink); | |
| } | |
| .hero-radar-details { | |
| margin: 14px 0 0; | |
| } | |
| .hero-radar-details .hero-radar-frame { | |
| margin: 16px; | |
| } | |
| main > section { | |
| position: relative; | |
| overflow: clip; | |
| } | |
| main > section::before { | |
| content: ""; | |
| position: absolute; | |
| inset: 0 0 auto; | |
| height: 1px; | |
| background: linear-gradient(90deg, transparent, rgba(204, 255, 160, 0.34), transparent); | |
| opacity: 0.58; | |
| pointer-events: none; | |
| } | |
| .stat, | |
| .suite-line-card, | |
| .snapshot-card, | |
| .split-radar-card, | |
| .figure-brief-card, | |
| .result-matrix-stat, | |
| .direction-card, | |
| .artifact, | |
| .artifact-group, | |
| .task-card { | |
| position: relative; | |
| overflow: hidden; | |
| transition: | |
| transform 240ms var(--motion-ease), | |
| border-color 240ms var(--motion-ease), | |
| background 240ms var(--motion-ease), | |
| box-shadow 240ms var(--motion-ease); | |
| } | |
| .stat::after, | |
| .suite-line-card::after, | |
| .snapshot-card::after, | |
| .split-radar-card::after, | |
| .figure-brief-card::after, | |
| .result-matrix-stat::after, | |
| .direction-card::after, | |
| .artifact::after, | |
| .artifact-group::after, | |
| .task-card::after { | |
| content: ""; | |
| position: absolute; | |
| inset: 0; | |
| pointer-events: none; | |
| background: linear-gradient(112deg, transparent 0 40%, rgba(204, 255, 160, 0.10) 49%, transparent 58%); | |
| opacity: 0; | |
| transform: translateX(-86%); | |
| } | |
| .stat:hover, | |
| .suite-line-card:hover, | |
| .snapshot-card:hover, | |
| .split-radar-card:hover, | |
| .figure-brief-card:hover, | |
| .result-matrix-stat:hover, | |
| .direction-card:hover, | |
| .artifact:hover, | |
| .artifact-group:hover { | |
| transform: translateY(-2px); | |
| border-color: rgba(204, 255, 160, 0.42); | |
| box-shadow: 0 18px 46px rgba(0, 0, 0, 0.24); | |
| } | |
| .stat:hover::after, | |
| .suite-line-card:hover::after, | |
| .snapshot-card:hover::after, | |
| .split-radar-card:hover::after, | |
| .figure-brief-card:hover::after, | |
| .result-matrix-stat:hover::after, | |
| .direction-card:hover::after, | |
| .artifact:hover::after, | |
| .artifact-group:hover::after, | |
| .task-card:hover::after { | |
| opacity: 1; | |
| animation: xperienceSheen 920ms var(--motion-ease); | |
| } | |
| .stat strong, | |
| .suite-line-facts strong, | |
| .result-matrix-stat strong { | |
| font-variant-numeric: tabular-nums; | |
| text-shadow: 0 0 16px rgba(204, 255, 160, 0.12); | |
| } | |
| .track > span { | |
| transform-origin: left center; | |
| } | |
| .motion-ready .track > span { | |
| animation: xperienceGrow 860ms var(--motion-ease) both; | |
| } | |
| .motion-ready .count-highlight { | |
| animation: xperienceMetricPulse 620ms var(--motion-ease); | |
| } | |
| @keyframes xperienceSheen { | |
| from { transform: translateX(-86%); } | |
| to { transform: translateX(86%); } | |
| } | |
| @keyframes xperienceGrow { | |
| from { transform: scaleX(0); } | |
| to { transform: scaleX(1); } | |
| } | |
| @keyframes xperienceMetricPulse { | |
| 0% { text-shadow: 0 0 0 rgba(204, 255, 160, 0); } | |
| 52% { text-shadow: 0 0 22px rgba(204, 255, 160, 0.34); } | |
| 100% { text-shadow: 0 0 16px rgba(204, 255, 160, 0.12); } | |
| } | |
| @media (prefers-reduced-motion: no-preference) { | |
| .hero-inner::before { | |
| animation: xperienceLinePulse 6.5s ease-in-out infinite; | |
| } | |
| .hero-radar-panel::before { | |
| animation: xperiencePanelDrift 12s linear infinite; | |
| } | |
| .motion-ready .reveal-ready { | |
| opacity: 0; | |
| transform: translateY(18px); | |
| transition: opacity 720ms var(--motion-ease), transform 720ms var(--motion-ease); | |
| transition-delay: var(--reveal-delay, 0ms); | |
| } | |
| .motion-ready .reveal-ready.is-visible { | |
| opacity: 1; | |
| transform: none; | |
| } | |
| .motion-ready .hero-path:nth-child(2), | |
| .motion-ready .suite-line-card:nth-child(2), | |
| .motion-ready .split-radar-card:nth-child(2) { | |
| --reveal-delay: 80ms; | |
| } | |
| .motion-ready .hero-path:nth-child(3), | |
| .motion-ready .stat:nth-child(3) { | |
| --reveal-delay: 140ms; | |
| } | |
| .motion-ready .hero-path:nth-child(4), | |
| .motion-ready .stat:nth-child(4) { | |
| --reveal-delay: 200ms; | |
| } | |
| } | |
| @keyframes xperienceLinePulse { | |
| 0%, 100% { opacity: 0.28; transform: scaleX(0.72); } | |
| 50% { opacity: 0.72; transform: scaleX(1); } | |
| } | |
| @keyframes xperiencePanelDrift { | |
| from { transform: translateX(-18%); } | |
| to { transform: translateX(18%); } | |
| } | |
| @media (min-width: 1121px) { | |
| .hero-inner { | |
| grid-template-columns: minmax(0, 0.92fr) minmax(470px, 0.78fr); | |
| gap: 34px; | |
| align-items: start; | |
| min-height: 0; | |
| padding: clamp(72px, 6vw, 96px) 0 clamp(56px, 5vw, 76px); | |
| } | |
| .hero-inner > div:first-child { | |
| align-self: start; | |
| max-width: 720px; | |
| } | |
| .hero h1 { | |
| font-size: clamp(46px, 5.4vw, 78px); | |
| } | |
| .hero-copy { | |
| max-width: 660px; | |
| font-size: 17px; | |
| line-height: 1.58; | |
| } | |
| .hero-actions { | |
| margin-top: 28px; | |
| } | |
| .suite-lines { | |
| gap: 10px; | |
| margin: 18px 0 0; | |
| } | |
| .suite-line-card { | |
| padding: 14px; | |
| } | |
| .suite-line-card h3 { | |
| margin: 6px 0; | |
| font-size: 18px; | |
| } | |
| .suite-line-card p { | |
| font-size: 12px; | |
| line-height: 1.42; | |
| } | |
| .line-readout { | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 6px; | |
| margin-top: 10px; | |
| padding: 10px; | |
| } | |
| .line-readout div { | |
| min-width: 0; | |
| } | |
| .line-readout p { | |
| font-size: 11.5px; | |
| line-height: 1.34; | |
| } | |
| .suite-line-facts { | |
| gap: 6px; | |
| margin-top: 10px; | |
| } | |
| .suite-line-facts span { | |
| padding: 7px; | |
| font-size: 10.5px; | |
| } | |
| .suite-line-card a { | |
| margin-top: 10px; | |
| font-size: 12px; | |
| } | |
| .hero-paths { | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 8px; | |
| margin-top: 18px; | |
| max-width: 720px; | |
| } | |
| .hero-path { | |
| min-height: 116px; | |
| padding: 11px; | |
| gap: 6px; | |
| } | |
| .hero-path strong { | |
| font-size: 15px; | |
| } | |
| .hero-path span { | |
| font-size: 11.5px; | |
| line-height: 1.34; | |
| } | |
| .hero-stats { | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| max-width: 520px; | |
| margin-top: 18px; | |
| } | |
| .stat { | |
| padding: 11px 12px; | |
| } | |
| .stat span { | |
| margin-top: 5px; | |
| } | |
| .hero-radar-panel { | |
| align-self: start; | |
| max-width: none; | |
| margin-top: 0; | |
| } | |
| .hero-radar-top { | |
| align-items: start; | |
| } | |
| .hero-radar-top span { | |
| max-width: 300px; | |
| text-align: right; | |
| line-height: 1.35; | |
| } | |
| .hero-radar-layout { | |
| grid-template-columns: 1fr; | |
| gap: 12px; | |
| } | |
| .hero-radar-frame img { | |
| height: clamp(380px, 33vw, 540px); | |
| min-height: 360px; | |
| } | |
| .hero-radar-copy { | |
| gap: 8px; | |
| } | |
| .hero-radar-copy h2 { | |
| font-size: 22px; | |
| line-height: 1.06; | |
| } | |
| .hero-radar-copy p { | |
| font-size: 12px; | |
| line-height: 1.40; | |
| } | |
| .hero-paths { | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| max-width: 520px; | |
| } | |
| .hero-path { | |
| min-height: 116px; | |
| padding: 13px; | |
| } | |
| .hero-radar-stats { | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| } | |
| .hero-task-strip { | |
| grid-template-columns: repeat(auto-fit, minmax(112px, 1fr)); | |
| } | |
| .hero-task-strip span { | |
| font-size: 9.5px; | |
| padding: 5px 6px; | |
| } | |
| .task-axis-icon { | |
| width: 24px; | |
| height: 24px; | |
| } | |
| .hero-method { | |
| padding: 8px; | |
| } | |
| .hero-method span { | |
| font-size: 10.5px; | |
| line-height: 1.25; | |
| } | |
| } | |
| @media (max-width: 1280px) { | |
| :root { | |
| --nav-height: 68px; | |
| --tab-stack-offset: 84px; | |
| } | |
| .site-nav .wrap, | |
| .project-tabs-shell .wrap { | |
| width: min(100% - 48px, var(--max)); | |
| } | |
| .brand { | |
| font-size: 20px; | |
| } | |
| .brand-logo { | |
| width: 42px; | |
| height: 42px; | |
| } | |
| .nav-links { display: none; } | |
| .site-language { | |
| min-height: 42px; | |
| padding: 0 10px; | |
| } | |
| .site-language label { | |
| display: none; | |
| } | |
| .site-language select { | |
| min-width: 104px; | |
| } | |
| .project-tabs { | |
| gap: 10px; | |
| } | |
| .project-tab { | |
| min-height: 84px; | |
| padding: 16px 15px; | |
| border-radius: 42px; | |
| } | |
| .project-tab strong { | |
| font-size: 17px; | |
| } | |
| .project-tab span { | |
| font-size: 12px; | |
| } | |
| .glossary-panel { | |
| grid-template-columns: 1fr; | |
| } | |
| .glossary-summary { | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| } | |
| .source-map-grid, | |
| .source-map-legend { | |
| grid-template-columns: 1fr; | |
| } | |
| .section-tab { | |
| min-height: 42px; | |
| padding: 0 14px; | |
| font-size: 13px; | |
| } | |
| } | |
| @media (max-width: 1880px) { | |
| .nav-links a.nav-optional { | |
| display: none; | |
| } | |
| } | |
| @media (max-width: 1740px) { | |
| .nav-links { | |
| gap: 6px; | |
| font-size: clamp(12px, 0.76vw, 15px); | |
| } | |
| .nav-links a { | |
| min-height: 42px; | |
| padding: 0 11px; | |
| } | |
| .site-language { | |
| width: 58px; | |
| max-width: 58px; | |
| justify-content: center; | |
| } | |
| .site-language::before { | |
| display: block; | |
| position: absolute; | |
| left: 12px; | |
| top: 50%; | |
| transform: translateY(-50%); | |
| color: var(--ink); | |
| font-weight: 780; | |
| line-height: 1; | |
| } | |
| .site-language select { | |
| min-width: 0; | |
| max-width: none; | |
| color: transparent; | |
| padding-right: 18px; | |
| background-position: | |
| calc(100% - 9px) 52%, | |
| calc(100% - 4px) 52%; | |
| } | |
| .nav-external-actions { | |
| gap: 5px; | |
| } | |
| .nav-action { | |
| height: 38px; | |
| min-width: 52px; | |
| padding: 0 13px; | |
| } | |
| .nav-action-hf { | |
| min-width: 54px; | |
| } | |
| .nav-action-official { | |
| min-width: 64px; | |
| } | |
| .nav-action-dev { | |
| min-width: 58px; | |
| } | |
| .nav-action-repo { | |
| min-width: 72px; | |
| } | |
| .nav-action-text-full { | |
| display: none; | |
| } | |
| .nav-action-text-short { | |
| display: inline; | |
| } | |
| } | |
| @media (max-width: 1560px) { | |
| .brand-logo { | |
| width: 46px; | |
| height: 46px; | |
| } | |
| } | |
| @media (max-width: 1360px) { | |
| .nav-links { display: none; } | |
| } | |
| @media (max-width: 960px) { | |
| .hero-inner, .two-col { grid-template-columns: 1fr; } | |
| .hero, | |
| .hero-inner { min-height: 0; } | |
| .hero-inner { padding: 76px 0 58px; } | |
| .hero-panel { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| .hero-radar-layout { grid-template-columns: 1fr; } | |
| .hero-radar-frame img { height: min(430px, 56vw); } | |
| .signal:nth-child(2n + 1) { border-right: 0; } | |
| .project-tabs { grid-template-columns: repeat(3, minmax(0, 1fr)); } | |
| .section-tabs { padding-top: 12px; } | |
| .section-orientation { | |
| align-items: start; | |
| display: grid; | |
| grid-template-columns: 1fr auto; | |
| } | |
| .figure-brief, | |
| .split-radar-grid, | |
| .foundation-pipeline-card { display: block; } | |
| .foundation-pipeline-card img { | |
| height: auto; | |
| min-height: 0; | |
| aspect-ratio: auto; | |
| border-right: 0; | |
| border-bottom: 1px solid var(--line); | |
| } | |
| .hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .reading-grid, .snapshot-grid, .roadmap-grid, .brief-grid, .surface-map, .boundary-strip, .callout-row, .direction-grid, .baseline-strip, .extension-grid, .resource-mode-grid, .suite-lines, .result-reading-order, .result-rules-panel, .data-analysis-grid, .data-analysis-chart-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| .data-scope-detail { grid-template-columns: 1fr; } | |
| .reader-route-panel, | |
| .task-axis-head, | |
| .method-flow-head { | |
| grid-template-columns: 1fr; | |
| } | |
| .reader-route-list, | |
| .task-axis-grid, | |
| .task-axis-summary, | |
| .task-axis-reader-rule, | |
| .task-axis-key, | |
| .task-axis-visual figcaption, | |
| .method-flow-steps { | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| } | |
| .task-axis-flow { | |
| grid-template-columns: 1fr; | |
| } | |
| .task-axis-flow span { | |
| display: none; | |
| } | |
| .suite-task-index-head, | |
| .task-map-details summary, | |
| .split-radar-details summary, | |
| .data-analysis-table-header { | |
| grid-template-columns: 1fr; | |
| align-items: start; | |
| } | |
| .task-index-grid { | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| } | |
| .brief-panel-head { grid-template-columns: 1fr; align-items: start; } | |
| .task-player { grid-template-columns: 1fr; } | |
| .task-selector { grid-template-columns: repeat(3, minmax(0, 1fr)); } | |
| .task-method-grid { grid-template-columns: repeat(3, minmax(0, 1fr)); } | |
| .storyboard-steps { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| .raw-sample-layout, | |
| .raw-detail-grid { grid-template-columns: 1fr; } | |
| .hdf5-map { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| .artifact-group-head { grid-template-columns: 1fr; align-items: start; } | |
| .chart-grid { grid-template-columns: 1fr; } | |
| .task-icon-atlas { grid-template-columns: 1fr; } | |
| .section-head { display: block; } | |
| .section-head p { margin-top: 14px; } | |
| .roadmap-entry-panel, | |
| .roadmap-resource-head { | |
| grid-template-columns: 1fr; | |
| } | |
| .roadmap-entry-steps, | |
| .roadmap-resource-grid { | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| } | |
| .roadmap-resource-card.primary { | |
| grid-column: 1 / -1; | |
| } | |
| } | |
| @media (max-width: 1120px) { | |
| .nav-links { display: none; } | |
| } | |
| @media (max-width: 640px) { | |
| :root { | |
| --nav-height: 64px; | |
| --tab-stack-offset: 82px; | |
| } | |
| .site-nav { | |
| min-height: 0; | |
| } | |
| .scroll-companion { | |
| right: max(12px, env(safe-area-inset-right)); | |
| bottom: max(12px, env(safe-area-inset-bottom)); | |
| grid-template-columns: minmax(0, 1fr) auto; | |
| max-width: calc(100vw - 24px); | |
| } | |
| .scroll-status { | |
| min-width: 0; | |
| padding: 7px 10px; | |
| } | |
| .scroll-status span { | |
| max-width: 52vw; | |
| } | |
| .back-to-top { | |
| min-width: 48px; | |
| padding: 0 10px; | |
| } | |
| .wrap { width: calc(100vw - 28px); max-width: calc(100vw - 28px); } | |
| .site-nav .wrap, | |
| .project-tabs-shell .wrap { | |
| width: calc(100vw - 28px); | |
| max-width: calc(100vw - 28px); | |
| } | |
| .nav-inner { | |
| position: static; | |
| height: var(--nav-height); | |
| gap: 10px; | |
| } | |
| .task-results-head { | |
| display: grid; | |
| gap: 4px; | |
| } | |
| .task-results-head em { | |
| text-align: left; | |
| } | |
| .task-method-grid { | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 7px; | |
| } | |
| .task-method-score { | |
| min-height: 76px; | |
| padding: 8px; | |
| } | |
| .task-method-value { | |
| font-size: 15px; | |
| } | |
| .task-method-top strong { | |
| font-size: 10px; | |
| } | |
| .brand { | |
| font-size: 16px; | |
| gap: 9px; | |
| } | |
| .brand span { | |
| display: none; | |
| } | |
| .brand-logo { | |
| width: 38px; | |
| height: 38px; | |
| } | |
| .site-language { | |
| min-height: 36px; | |
| padding: 0 7px; | |
| font-size: 12px; | |
| width: 52px; | |
| max-width: 52px; | |
| flex: 0 0 52px; | |
| justify-content: center; | |
| position: relative; | |
| left: auto; | |
| right: auto; | |
| top: auto; | |
| transform: none; | |
| } | |
| .site-language::before { | |
| display: block; | |
| position: absolute; | |
| left: 11px; | |
| top: 50%; | |
| transform: translateY(-50%); | |
| color: var(--ink); | |
| font-weight: 780; | |
| line-height: 1; | |
| } | |
| .site-language select { | |
| width: 100%; | |
| min-width: 0; | |
| max-width: none; | |
| color: transparent; | |
| padding-right: 17px; | |
| background-position: | |
| calc(100% - 9px) 52%, | |
| calc(100% - 4px) 52%; | |
| } | |
| .nav-tools { | |
| gap: 5px; | |
| padding: 4px; | |
| border: 1px solid rgba(204, 255, 160, 0.16); | |
| border-radius: 999px; | |
| background: rgba(2, 5, 2, 0.76); | |
| box-shadow: 0 10px 32px rgba(0, 0, 0, 0.26); | |
| flex: 0 1 auto; | |
| width: auto; | |
| max-width: calc(100% - 50px); | |
| margin-left: auto; | |
| margin-right: 0; | |
| justify-content: flex-end; | |
| overflow: hidden; | |
| position: relative; | |
| } | |
| .nav-external-actions { | |
| padding: 0; | |
| flex: 0 0 auto; | |
| position: static; | |
| left: auto; | |
| right: auto; | |
| top: auto; | |
| transform: none; | |
| gap: 5px; | |
| } | |
| .nav-action { | |
| min-width: 40px; | |
| height: 36px; | |
| padding: 0 8px; | |
| font-size: 11.5px; | |
| gap: 5px; | |
| } | |
| .nav-action::before { | |
| width: 22px; | |
| height: 22px; | |
| font-size: 9px; | |
| } | |
| .nav-action-repo { | |
| display: inline-flex; | |
| min-width: 40px; | |
| padding: 0 8px; | |
| } | |
| .nav-action-repo .nav-action-text-short { | |
| font-size: 0; | |
| } | |
| .nav-action-repo .nav-action-text-short::after { | |
| content: "GH"; | |
| font-size: 11.5px; | |
| } | |
| .nav-action-text-short { | |
| display: inline; | |
| } | |
| .project-tabs-shell { top: var(--nav-height); padding: 10px 0; } | |
| .project-tabs { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 6px; | |
| overflow: visible; | |
| padding-bottom: 0; | |
| scroll-snap-type: none; | |
| } | |
| .project-tab { | |
| display: flex; | |
| align-items: center; | |
| justify-content: center; | |
| min-height: 42px; | |
| padding: 8px 5px; | |
| border-radius: 999px; | |
| text-align: center; | |
| scroll-snap-align: none; | |
| } | |
| .project-tab strong { | |
| font-size: 12px; | |
| line-height: 1.05; | |
| text-wrap: balance; | |
| } | |
| .project-tab span { | |
| display: none; | |
| } | |
| .section-tabs { | |
| flex-wrap: nowrap; | |
| gap: 8px; | |
| overflow-x: auto; | |
| padding-top: 8px; | |
| padding-bottom: 4px; | |
| } | |
| .section-tab { | |
| flex: 0 0 auto; | |
| min-height: 34px; | |
| padding: 7px 10px; | |
| font-size: 12px; | |
| white-space: nowrap; | |
| } | |
| .section-orientation { | |
| grid-template-columns: 1fr; | |
| gap: 7px; | |
| margin-top: 8px; | |
| padding: 10px; | |
| font-size: 12px; | |
| } | |
| .section-orientation strong { | |
| font-size: 12.5px; | |
| } | |
| .content-tabs { | |
| flex-wrap: nowrap; | |
| overflow-x: auto; | |
| margin-bottom: 14px; | |
| } | |
| .content-tab { | |
| flex: 0 0 auto; | |
| min-width: min(76vw, 210px); | |
| min-height: 52px; | |
| } | |
| .source-map-card { | |
| min-height: 0; | |
| } | |
| .source-map-panel { | |
| padding: 12px; | |
| } | |
| .source-map-chart-frame .chart { | |
| min-width: 840px; | |
| } | |
| .source-map-caption { | |
| display: grid; | |
| gap: 10px; | |
| } | |
| main > section { scroll-margin-top: var(--tab-stack-offset); } | |
| html, | |
| body { | |
| max-width: 100%; | |
| overflow-x: hidden; | |
| } | |
| .hero-inner, | |
| .hero-inner > *, | |
| .hero-radar-panel, | |
| .hero-radar-layout, | |
| .hero-radar-copy { | |
| min-width: 0; | |
| max-width: 100%; | |
| } | |
| .hero-inner { | |
| display: block; | |
| width: 100%; | |
| } | |
| .hero, | |
| main, | |
| .site-nav { | |
| max-width: 100vw; | |
| overflow-x: hidden; | |
| } | |
| .hero .wrap { | |
| width: min(360px, calc(100% - 28px)); | |
| max-width: 360px; | |
| margin-inline: auto; | |
| padding-inline: 0; | |
| } | |
| h1 { | |
| max-width: 100%; | |
| font-size: 34px; | |
| line-height: 1.04; | |
| text-wrap: balance; | |
| overflow-wrap: normal; | |
| } | |
| .eyebrow { | |
| display: flex; | |
| flex-wrap: wrap; | |
| justify-content: center; | |
| max-width: 100%; | |
| font-size: 11px; | |
| line-height: 1.25; | |
| text-align: center; | |
| } | |
| .eyebrow::before { | |
| display: none; | |
| } | |
| .hero-copy { | |
| max-width: 100%; | |
| font-size: 16px; | |
| line-height: 1.56; | |
| white-space: normal; | |
| overflow-wrap: break-word; | |
| } | |
| .hero-actions { | |
| display: grid; | |
| grid-template-columns: 1fr; | |
| } | |
| .button { | |
| width: 100%; | |
| justify-content: center; | |
| } | |
| .hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .reading-grid, .snapshot-grid, .roadmap-grid, .brief-grid, .surface-map, .boundary-strip, .chart-grid, .callout-row, .direction-grid, .baseline-strip, .extension-grid, .walk-flow, .flow-steps, .storyboard-steps, .task-selector, .hero-paths, .resource-mode-grid, .suite-lines, .suite-line-facts, .result-reading-order, .result-rules-panel, .reader-route-list, .task-axis-grid, .task-axis-summary, .task-axis-reader-rule, .task-axis-key, .task-axis-task-list, .task-axis-visual figcaption, .method-flow-steps, .suite-task-index-head, .data-analysis-grid, .data-analysis-chart-grid, .data-analysis-table-header, .data-scope-detail, .data-scope-facts { grid-template-columns: 1fr; } | |
| .data-scope-explorer { padding: 12px; } | |
| .data-scope-flow { | |
| grid-template-columns: 1fr; | |
| gap: 8px; | |
| } | |
| .data-scope-flow-arrow { | |
| justify-self: center; | |
| transform: rotate(90deg); | |
| } | |
| .roadmap-entry-steps, | |
| .roadmap-resource-grid { | |
| grid-template-columns: 1fr; | |
| } | |
| .task-axis-summary-card { | |
| grid-template-columns: auto 1fr; | |
| } | |
| .task-axis-visual img { | |
| min-height: 0; | |
| } | |
| .data-chart-card img, | |
| .data-chart-card.wide img { | |
| min-height: 240px; | |
| } | |
| .task-axis-visual figcaption { | |
| position: static; | |
| padding: 10px; | |
| background: rgba(2, 5, 2, 0.88); | |
| } | |
| .task-index-grid { | |
| grid-template-columns: 1fr; | |
| } | |
| .result-matrix-head { | |
| grid-template-columns: 1fr; | |
| } | |
| .result-matrix-actions { | |
| justify-content: flex-start; | |
| } | |
| .result-matrix-stats { | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| } | |
| .result-matrix-body { | |
| padding: 18px 14px 18px; | |
| } | |
| .result-score-table { | |
| min-width: 2100px; | |
| } | |
| .result-score-table th:first-child, | |
| .result-score-table td:first-child { | |
| min-width: 176px; | |
| max-width: 176px; | |
| } | |
| .line-table, | |
| .line-table thead, | |
| .line-table tbody, | |
| .line-table tr, | |
| .line-table th, | |
| .line-table td { | |
| display: block; | |
| } | |
| .line-table thead { | |
| display: none; | |
| } | |
| .line-table tr { | |
| border-bottom: 1px solid var(--soft-line); | |
| } | |
| .line-table tr:last-child { | |
| border-bottom: 0; | |
| } | |
| .line-table td { | |
| border-bottom: 0; | |
| padding: 10px 12px; | |
| } | |
| .line-table td[data-label]::before { | |
| content: attr(data-label); | |
| display: block; | |
| margin-bottom: 4px; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 10px; | |
| font-weight: 800; | |
| letter-spacing: 0.07em; | |
| text-transform: uppercase; | |
| } | |
| .line-table td:first-child { | |
| width: auto; | |
| padding-top: 14px; | |
| } | |
| .glossary-table { | |
| min-width: 0; | |
| } | |
| .glossary-summary { | |
| grid-template-columns: 1fr; | |
| } | |
| .glossary-scroll-note { | |
| display: block; | |
| } | |
| .glossary-scroll-note p { | |
| margin-top: 6px; | |
| text-align: left; | |
| } | |
| .glossary-table-wrap { | |
| max-height: min(66vh, 560px); | |
| } | |
| .glossary-table, | |
| .glossary-table thead, | |
| .glossary-table tbody, | |
| .glossary-table tr, | |
| .glossary-table th, | |
| .glossary-table td { | |
| display: block; | |
| } | |
| .glossary-table thead { | |
| display: none; | |
| } | |
| .glossary-table tr { | |
| border-bottom: 1px solid rgba(204, 255, 160, 0.14); | |
| padding: 10px 0; | |
| } | |
| .glossary-table tr:last-child { | |
| border-bottom: 0; | |
| } | |
| .glossary-table td { | |
| border-bottom: 0; | |
| padding: 8px 12px; | |
| } | |
| .glossary-table td::before { | |
| content: attr(data-label); | |
| display: block; | |
| margin-bottom: 3px; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 9.5px; | |
| font-weight: 820; | |
| letter-spacing: 0.07em; | |
| text-transform: uppercase; | |
| } | |
| .glossary-table td:first-child { | |
| width: auto; | |
| font-size: 16px; | |
| } | |
| .about-identity { | |
| grid-template-columns: 1fr; | |
| gap: 14px; | |
| padding: 18px; | |
| } | |
| .about-identity img { | |
| width: 72px; | |
| height: 72px; | |
| } | |
| .hero::before { | |
| background: | |
| linear-gradient(180deg, rgba(2, 5, 2, 0.92) 0%, rgba(2, 5, 2, 0.76) 48%, rgba(2, 5, 2, 0.96) 100%), | |
| url("assets/task_suite_infographic.png?v=xperience10m-taskfirst-v14-modality-compact") center / cover no-repeat; | |
| } | |
| .hero-panel { grid-template-columns: 1fr; } | |
| .hero-path { | |
| min-height: 0; | |
| } | |
| .resource-mode { | |
| min-height: 0; | |
| } | |
| .hero-radar-panel { padding: 12px; } | |
| .hero-radar-top { display: block; } | |
| .hero-radar-top span { display: block; margin-top: 5px; } | |
| .hero-radar-frame img { height: 360px; min-height: 320px; } | |
| .unified-radar-chart { height: auto; min-height: 0; } | |
| .split-radar-card img { height: auto; min-height: 0; } | |
| .hero-radar-stats, | |
| .hero-task-strip { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| .signal { border-right: 0; border-bottom: 1px solid rgba(245, 247, 240, 0.10); } | |
| .signal:last-child { border-bottom: 0; } | |
| .brief-panel { padding: 18px; } | |
| .artifact-group { padding: 16px; } | |
| .raw-player-panel, | |
| .raw-file-panel, | |
| .raw-detail-card { padding: 14px; } | |
| .raw-player-top { display: block; } | |
| .raw-kind-pill { display: inline-flex; margin-top: 12px; } | |
| .raw-file-button { grid-template-columns: 64px minmax(0, 1fr); } | |
| .raw-file-button .raw-file-thumb { width: 64px; height: 48px; } | |
| .raw-file-button strong, | |
| .raw-file-button em { grid-column: 2; text-align: left; } | |
| .raw-file-button span { grid-column: 1 / -1; } | |
| .hdf5-map { grid-template-columns: 1fr; } | |
| .artifact.primary-artifact { grid-template-columns: 1fr; } | |
| .evidence-card:last-child { grid-column: auto; } | |
| .hero-inner, section { padding: 46px 0; } | |
| .signal { grid-template-columns: 1fr; } | |
| .signal strong { text-align: left; } | |
| .player-counter { width: 100%; margin-left: 0; } | |
| .task-scrubber { flex-basis: 100%; } | |
| .figure-pan { | |
| margin-inline: 0; | |
| padding-inline: 0; | |
| } | |
| .figure-pan .task-suite-image { | |
| width: 100%; | |
| min-width: 0; | |
| max-width: 100%; | |
| } | |
| } | |
| @media (max-width: 460px) { | |
| .nav-inner { | |
| gap: 7px; | |
| } | |
| .brand { | |
| gap: 7px; | |
| } | |
| .site-language { | |
| width: 50px; | |
| max-width: 50px; | |
| padding: 0 6px; | |
| } | |
| .site-language select { | |
| min-width: 0; | |
| max-width: none; | |
| padding-right: 17px; | |
| } | |
| .nav-tools { | |
| gap: 3px; | |
| padding: 4px; | |
| margin-right: 0; | |
| } | |
| .nav-external-actions { | |
| gap: 3px; | |
| padding: 0; | |
| } | |
| .nav-action { | |
| min-width: 34px; | |
| height: 30px; | |
| padding: 0 6px; | |
| font-size: 10.8px; | |
| } | |
| .nav-action::before { | |
| width: 20px; | |
| height: 20px; | |
| font-size: 8.5px; | |
| } | |
| .nav-action-text-short { | |
| display: inline; | |
| } | |
| .nav-action-repo { | |
| min-width: 34px; | |
| padding: 0 6px; | |
| } | |
| .nav-action-repo .nav-action-text-short::after { | |
| font-size: 10.8px; | |
| } | |
| } | |
| @media (prefers-reduced-motion: reduce) { | |
| html { scroll-behavior: auto; } | |
| *, *::before, *::after { | |
| transition-duration: 1ms ; | |
| animation-duration: 1ms ; | |
| animation-iteration-count: 1 ; | |
| } | |
| .motion-ready .reveal { opacity: 1; transform: none; } | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <div class="page-progress" aria-hidden="true"><span></span></div> | |
| <div class="scroll-companion" id="scrollCompanion" aria-live="polite"> | |
| <div class="scroll-status" aria-label="Reading progress"> | |
| <strong id="scrollPercent">0%</strong> | |
| <span id="scrollSection">Top</span> | |
| </div> | |
| <button class="back-to-top" id="backToTop" type="button" aria-label="Back to top">Top</button> | |
| </div> | |
| <a class="skip-link" href="#main">Skip to content</a> | |
| <nav class="site-nav"> | |
| <div class="wrap nav-inner"> | |
| <a class="brand" href="#top" aria-label="Ropedia Xperience-10M Task Suite home"> | |
| <img class="brand-logo" src="assets/brand/xperience10m-logo-mark-192.png" alt="" aria-hidden="true" width="38" height="38"> | |
| <span>Ropedia Xperience-10M Task Suite</span> | |
| </a> | |
| <div class="nav-links" aria-label="Page navigation"> | |
| <a href="#overview">Overview</a> | |
| <a href="#dataset-card">Data</a> | |
| <a href="#suite">Tasks</a> | |
| <a href="#pipeline">Method</a> | |
| <a href="#takeaways">Results</a> | |
| <a href="#directions">Directions</a> | |
| <a href="#roadmap">Roadmap</a> | |
| <a href="#glossary">Glossary</a> | |
| </div> | |
| <div class="nav-tools"> | |
| <div class="site-language notranslate" translate="no" data-short="EN"> | |
| <label for="siteLanguage">Language</label> | |
| <select id="siteLanguage" aria-label="Translate this website"> | |
| <option value="en">English</option> | |
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| <span id="siteLanguageStatus" class="site-language-status" aria-live="polite"></span> | |
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| <a class="nav-action nav-action-official" data-mark="RO" href="https://ropedia.com/" aria-label="Open official Ropedia website"> | |
| <span class="nav-action-text-full">Ropedia</span> | |
| <span class="nav-action-text-short">RO</span> | |
| </a> | |
| <a class="nav-action nav-action-dev" data-mark="Dev" href="https://ropedia-dev.com/" aria-label="Open Ropedia Dev website"> | |
| <span class="nav-action-text-full">Ropedia Dev</span> | |
| <span class="nav-action-text-short">Dev</span> | |
| </a> | |
| <a class="nav-action nav-action-hf" data-mark="HF" href="https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite" aria-label="Open Hugging Face Space"> | |
| <span class="nav-action-text-full">HF Space</span> | |
| <span class="nav-action-text-short">HF</span> | |
| </a> | |
| <a class="nav-action nav-action-repo" data-mark="GH" href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite" aria-label="Open GitHub repository"> | |
| <span class="nav-action-text-full">GitHub Repo</span> | |
| <span class="nav-action-text-short">GitHub</span> | |
| </a> | |
| </div> | |
| </div> | |
| <div id="google_translate_element" aria-hidden="true"></div> | |
| </div> | |
| </nav> | |
| <header class="hero" id="top"> | |
| <div class="wrap hero-inner"> | |
| <div> | |
| <div class="eyebrow">two evidence lines / 180 scored records</div> | |
| <h1>Ropedia Xperience-10M Task Suite.</h1> | |
| <p class="hero-copy"> | |
| This project builds on the Xperience-10M egocentric dataset released by Ropedia and turns it into public, reproducible embodied-AI evaluation materials. It defines 20 task contracts, provides a fully inspectable sample-episode line, and adds a selected-128 comparison line for baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window. Across 180 scored method-task records, direct scores stay separate from six clearly marked compact-proxy estimates, so public-safe feature results are not confused with direct raw-target measurements. | |
| </p> | |
| <div class="hero-actions"> | |
| <a class="button primary" href="#overview">Start: project scope</a> | |
| <a class="button" href="#takeaways">Cite current results</a> | |
| <a class="button" href="#suite">Inspect 20 tasks</a> | |
| <a class="button" href="single_episode_explorer.html">Explore one episode</a> | |
| </div> | |
| <div class="suite-lines" aria-label="Two evidence lines"> | |
| <article class="suite-line-card"> | |
| <small>line 1 / public sample</small> | |
| <h3>1 sample episode: task lab</h3> | |
| <p>One public episode becomes aligned windows, task targets, Minimal heads, and Neural MLP heads.</p> | |
| <div class="line-readout"> | |
| <div><span>best use</span><p>Inspect the sample files, task targets, and local baseline runs.</p></div> | |
| <div><span>read separately from</span><p>Selected-128 comparison rows and held-out model behavior.</p></div> | |
| </div> | |
| <div class="suite-line-facts"> | |
| <span><strong>5,821</strong>frames</span> | |
| <span><strong>1,161</strong>20-frame windows</span> | |
| <span><strong>40/40</strong>direct task scores</span> | |
| </div> | |
| <a href="#raw-sample">Open sample line</a> | |
| </article> | |
| <article class="suite-line-card"> | |
| <small>line 2 / 128 selected episodes</small> | |
| <h3>128 selected episodes: comparison surface</h3> | |
| <p>Seven methods share the selected-episode surface and the same 20 task axes.</p> | |
| <div class="line-readout"> | |
| <div><span>best use</span><p>Compare same-split baselines, Qwen3-Omni v6, and Cosmos3 rows.</p></div> | |
| <div><span>read separately from</span><p>The 6 compact-proxy cells, which use public-safe compact features instead of direct raw-target reads.</p></div> | |
| </div> | |
| <div class="suite-line-facts"> | |
| <span><strong>128</strong>selected episodes</span> | |
| <span><strong>34,269</strong>exported windows</span> | |
| <span><strong>140/140</strong>134 direct + 6 proxy</span> | |
| </div> | |
| <a href="#suite">Open 128-episode line</a> | |
| </article> | |
| </div> | |
| <div class="hero-paths" aria-label="Choose a project path"> | |
| <a class="hero-path" href="#dataset-card"> | |
| <small>Start with data</small> | |
| <strong>Dataset scope</strong> | |
| <span>Official Xperience-10M context, public sample files, and selected-128 boundaries.</span> | |
| </a> | |
| <a class="hero-path" href="#suite"> | |
| <small>Inspect</small> | |
| <strong>Tasks and method</strong> | |
| <span>20 task contracts, feature construction, leakage controls, and baseline heads.</span> | |
| </a> | |
| <a class="hero-path" href="#takeaways"> | |
| <small>Compare</small> | |
| <strong>Results</strong> | |
| <span>Evidence lines, 180 scored records, direct/proxy status, and model diagnostics.</span> | |
| </a> | |
| <a class="hero-path" href="#directions"> | |
| <small>Extend</small> | |
| <strong>Directions after results</strong> | |
| <span>Research directions, roadmap, and scale-up plans after the result table.</span> | |
| </a> | |
| </div> | |
| <div class="hero-stats"> | |
| <div class="stat"><strong>5,821</strong><span>frames in sample episode</span></div> | |
| <div class="stat"><strong>1,161</strong><span>20-frame windows</span></div> | |
| <div class="stat"><strong>8,546</strong><span>feature dimensions</span></div> | |
| <div class="stat"><strong>20</strong><span>unified task contracts</span></div> | |
| </div> | |
| </div> | |
| <div class="hero-radar-panel" aria-label="Project scope and result orientation"> | |
| <div class="hero-radar-top"> | |
| <strong>project overview</strong> | |
| <span>dataset scope / task suite / result records</span> | |
| </div> | |
| <div class="hero-radar-layout"> | |
| <div class="hero-radar-copy"> | |
| <h2>Scope, tasks, and evidence stay separated.</h2> | |
| <p>The project separates dataset scope, task contracts, result records, and source files so each score can be traced to its evidence line.</p> | |
| <div class="hero-radar-stats" aria-label="Radar coverage summary"> | |
| <div class="hero-radar-stat"><strong>180</strong><span>method-task records</span></div> | |
| <div class="hero-radar-stat"><strong>174</strong><span>direct scores</span></div> | |
| <div class="hero-radar-stat"><strong>6</strong><span>compact-proxy scores</span></div> | |
| <div class="hero-radar-stat"><strong>34,269</strong><span>128ep windows</span></div> | |
| </div> | |
| </div> | |
| <div class="hero-radar-copy"> | |
| <h2>Three data scopes.</h2> | |
| <p>These scopes stay separate throughout the task suite and result matrix.</p> | |
| <div class="hero-method-list" aria-label="Data scopes"> | |
| <a class="hero-method" style="--method-color:#67e8d1" href="#raw-sample"><strong>1 public sample</strong><span>Inspectable raw browser, HDF5/RRD roles, 1,161 windows, and 40 direct task scores.</span></a> | |
| <a class="hero-method" style="--method-color:#ffd166" href="#suite"><strong>128 selected episodes</strong><span>Public-safe derived comparison surface with 34,269 windows and 140 selected-128 scores.</span></a> | |
| <a class="hero-method" style="--method-color:#9bb8ff" href="#dataset-card"><strong>Full Xperience-10M</strong><span>Gated upstream dataset context only; raw full-corpus data is not redistributed here.</span></a> | |
| </div> | |
| </div> | |
| </div> | |
| <details class="split-radar-details hero-radar-details"> | |
| <summary> | |
| <span>open full 9-method radar</span> | |
| <strong>The full grouped radar is useful for pattern scanning after comparing the 1-episode and 128-episode views.</strong> | |
| </summary> | |
| <a class="hero-radar-frame" href="#suite-radars" aria-label="Open full unified 20-task model radar"> | |
| <img src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v8-readable" alt="Unified 20-task grouped radar board with method-family panels, task key, score counts, and raw128 proxy notes"> | |
| </a> | |
| </details> | |
| <details class="split-radar-details hero-task-details"> | |
| <summary> | |
| <span>show task names and source links</span> | |
| <strong>All 20 task axes, radar images, result matrix, and proxy audit remain available here without crowding the first screen.</strong> | |
| </summary> | |
| <div class="hero-task-strip" aria-label="All 20 radar task axes"> | |
| <strong class="hero-task-strip-head">All 20 radar task axes</strong> | |
| <span><img class="task-axis-icon" src="assets/task-icons/01_timeline_action.svg" alt="" loading="lazy"><span class="task-axis-label">01 Action Recognition</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/02_timeline_subtask.svg" alt="" loading="lazy"><span class="task-axis-label">02 Procedure Step Recognition</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/03_transition_detection.svg" alt="" loading="lazy"><span class="task-axis-label">03 Action Boundary Detection</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/04_next_action.svg" alt="" loading="lazy"><span class="task-axis-label">04 Next-Action Prediction</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/05_hand_trajectory_forecast.svg" alt="" loading="lazy"><span class="task-axis-label">05 Hand Trajectory Forecasting</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/06_contact_prediction.svg" alt="" loading="lazy"><span class="task-axis-label">06 Contact State Prediction</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/07_object_relevance.svg" alt="" loading="lazy"><span class="task-axis-label">07 Object Relevance Prediction</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/08_caption_grounding.svg" alt="" loading="lazy"><span class="task-axis-label">08 Language Grounding</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/09_cross_modal_retrieval.svg" alt="" loading="lazy"><span class="task-axis-label">09 Cross-Modal Retrieval</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/10_modality_reconstruction.svg" alt="" loading="lazy"><span class="task-axis-label">10 Cross-Modal Reconstruction</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/11_temporal_order.svg" alt="" loading="lazy"><span class="task-axis-label">11 Temporal Order Verification</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/12_misalignment_detection.svg" alt="" loading="lazy"><span class="task-axis-label">12 Multimodal Synchronization Detection</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/13_long_horizon_next_action.svg" alt="" loading="lazy"><span class="task-axis-label">13 Long-Horizon Next-Action Forecasting</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/14_next_subtask_forecast.svg" alt="" loading="lazy"><span class="task-axis-label">14 Long-Horizon Next-Subtask Forecasting</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/15_interaction_text_prediction.svg" alt="" loading="lazy"><span class="task-axis-label">15 Interaction Text Prediction</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/16_action_object_relation.svg" alt="" loading="lazy"><span class="task-axis-label">16 Action-Object Relation Prediction</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/17_object_set_forecast.svg" alt="" loading="lazy"><span class="task-axis-label">17 Future Object-Set Forecasting</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/18_imu_to_hand_pose.svg" alt="" loading="lazy"><span class="task-axis-label">18 IMU-to-Hand Pose Reconstruction</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/19_camera_view_sync_retrieval.svg" alt="" loading="lazy"><span class="task-axis-label">19 Camera-View Synchronization Retrieval</span></span> | |
| <span><img class="task-axis-icon" src="assets/task-icons/20_time_to_transition.svg" alt="" loading="lazy"><span class="task-axis-label">20 Time-to-Next-Transition Regression</span></span> | |
| </div> | |
| <div class="hero-radar-links"> | |
| <a href="#suite">Open full radar</a> | |
| <a href="assets/charts/unified_task_model_radar.svg">View radar source</a> | |
| <a href="assets/charts/single_episode_task_model_radar.svg">1-episode radar</a> | |
| <a href="assets/charts/episode128_task_model_radar.svg">128ep radar</a> | |
| <a href="data/unified_task_model_radar.json">Inspect radar source</a> | |
| <a href="#result-matrix-table">Open 180-result table</a> | |
| <a href="data/task_method_20_result_matrix.json">Inspect 20-task matrix</a> | |
| <a href="data/task_method_20_gap_audit.json">Review score/proxy audit</a> | |
| </div> | |
| </details> | |
| </div> | |
| </div> | |
| </header> | |
| <main id="main" class="continuous" data-active-tab="start"> | |
| <div class="project-tabs-shell" aria-label="Current section navigation" hidden aria-hidden="true"> | |
| <div class="wrap project-tabs" role="tablist" aria-label="Project sections" hidden aria-hidden="true"> | |
| <button type="button" class="project-tab active" id="tab-start" role="tab" data-tab-key="start" data-default-section="overview" aria-selected="true" aria-pressed="true" aria-controls="overview"> | |
| <strong>Start</strong> | |
| <span>scope and project identity</span> | |
| </button> | |
| <button type="button" class="project-tab" id="tab-data" role="tab" data-tab-key="data" data-default-section="dataset-card" aria-selected="false" aria-pressed="false" aria-controls="dataset-card data-analysis raw-sample suite walkthroughs" tabindex="-1"> | |
| <strong>Data & Tasks</strong> | |
| <span>dataset sample and task suite</span> | |
| </button> | |
| <button type="button" class="project-tab" id="tab-method" role="tab" data-tab-key="method" data-default-section="pipeline" aria-selected="false" aria-pressed="false" aria-controls="protocol pipeline features architectures" tabindex="-1"> | |
| <strong>Method</strong> | |
| <span>pipeline and model design</span> | |
| </button> | |
| <button type="button" class="project-tab" id="tab-results" role="tab" data-tab-key="results" data-default-section="takeaways" aria-selected="false" aria-pressed="false" aria-controls="takeaways" tabindex="-1"> | |
| <strong>Results</strong> | |
| <span>takeaways and baselines</span> | |
| </button> | |
| <button type="button" class="project-tab" id="tab-directions" role="tab" data-tab-key="directions" data-default-section="directions" aria-selected="false" aria-pressed="false" aria-controls="directions roadmap" tabindex="-1"> | |
| <strong>Directions</strong> | |
| <span>directions and roadmap</span> | |
| </button> | |
| <button type="button" class="project-tab" id="tab-resources" role="tab" data-tab-key="resources" data-default-section="reading-path" aria-selected="false" aria-pressed="false" aria-controls="evidence reading-path glossary artifacts omni-scale-up foundation-model-paths" tabindex="-1"> | |
| <strong>Resources</strong> | |
| <span>artifacts, models, commands</span> | |
| </button> | |
| </div> | |
| <div class="wrap section-tabs" id="sectionTabs" role="tablist" aria-label="Sections in the current area"></div> | |
| <div class="wrap section-orientation" id="sectionOrientation" aria-live="polite"> | |
| <strong>Start / Project Overview</strong> | |
| <span>Scope, evidence boundaries, and project entry points.</span> | |
| <a href="#reader-map">Evidence map</a> | |
| </div> | |
| </div> | |
| <section id="overview" data-project-tab="start" role="tabpanel" aria-labelledby="tab-start" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Project overview and evidence map.</h2> | |
| <p>The project separates the two evidence lines, the 20-task suite, the result table, and the public artifact mirrors before deeper archive-level files.</p> | |
| </div> | |
| <div class="about-identity" aria-label="About the Ropedia Xperience-10M Task Suite"> | |
| <img src="assets/brand/xperience10m-logo-mark-192.png" alt="Ropedia Xperience-10M Task Suite project logo" width="92" height="92" loading="eager" decoding="async"> | |
| <div> | |
| <span>Project identity</span> | |
| <strong>Shared package across GitHub, web, and Hugging Face</strong> | |
| <p>The mark identifies the Ropedia Xperience-10M Task Suite package across the GitHub repository, GitHub Pages dashboard, Hugging Face Space, artifact dataset, model mirrors, and social preview. Ropedia is credited here as the Xperience-10M data provider and releaser; this site is the task-suite, evaluation, and public-safe artifact layer built on top of that released dataset.</p> | |
| <div class="about-identity-links" aria-label="Project identity links"> | |
| <a href="assets/brand/xperience10m-logo-mark-512.png">Logo mark</a> | |
| <a href="assets/brand/xperience10m-logo-social-card.png">Social card</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite">GitHub repo</a> | |
| <a href="https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite">HF Space</a> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="reader-route-panel" aria-label="Project route through the task suite"> | |
| <div class="reader-route-copy"> | |
| <small>project route</small> | |
| <strong>Four project layers connect data, tasks, methods, and results.</strong> | |
| <p>The project starts with the Xperience-10M data scope, then explains the 20-task contracts, method design, result table, and exact reproduction or extension artifacts.</p> | |
| </div> | |
| <div class="reader-route-list"> | |
| <a href="#dataset-card"><span>01 data</span><strong>Dataset scope</strong><em>Official Xperience-10M context, public sample files, and selected-128 boundaries.</em></a> | |
| <a href="#suite"><span>02 tasks</span><strong>20-task suite</strong><em>One task vocabulary for all methods and both evidence lines.</em></a> | |
| <a href="#pipeline"><span>03 method</span><strong>Pipeline and protocol</strong><em>Feature construction, leakage controls, model heads, and evaluation rules.</em></a> | |
| <a href="#takeaways"><span>04 results</span><strong>Result ledger</strong><em>Direct scores, compact-proxy cells, model diagnostics, and citeable tables.</em></a> | |
| </div> | |
| </div> | |
| <figure class="line-map-figure"> | |
| <img src="assets/charts/two_evidence_line_map.svg?v=two-line-map-v2" alt="Two evidence-line map showing 1 sample episode, 128 selected episodes, and the combined 180 scored method-task records"> | |
| </figure> | |
| <table class="line-table" aria-label="Two evidence-line comparison"> | |
| <thead> | |
| <tr> | |
| <th>Line</th> | |
| <th>Data unit</th> | |
| <th>Score statement</th> | |
| <th>Best use</th> | |
| <th>Read separately from</th> | |
| <th>Start here</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td>1 sample episode</td> | |
| <td>One public Xperience-10M sample episode; 5,821 frames; 1,161 aligned 20-frame windows; 8,546-dimensional feature contract.</td> | |
| <td>40/40 direct scores from Minimal and Neural MLP heads.</td> | |
| <td>Raw sample inspection, file organization, task definitions, local reproduction, and controlled Minimal-vs-Neural baseline behavior.</td> | |
| <td>The selected-128 comparison rows and broader held-out model behavior.</td> | |
| <td><a href="#raw-sample">Raw browser</a><br><a href="data/single_episode_task_model_radar.json">1-episode radar data</a><br><a href="data/two_evidence_line_result_summary.json">result summary data</a><br><a href="data/two_evidence_lines.json">evidence-line data</a></td> | |
| </tr> | |
| <tr> | |
| <td>128 selected episodes</td> | |
| <td>Selected held-out 96/16/16 split; 34,269 exported windows; public-safe metadata/raw-feature artifacts linked to official gated episode paths.</td> | |
| <td>140/140 selected-128 scores: 134 direct + 6 compact-proxy.</td> | |
| <td>Same-split comparison across metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super, Cosmos3-Nano, and scale-up decisions.</td> | |
| <td>Direct raw-target interpretation for the proxy-marked cells.</td> | |
| <td><a href="data/episode128_task_model_radar.json">128-episode radar data</a><br><a href="data/xperience10m_128_episode_feature_index.json">source and feature index</a><br><a href="https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/viewer/selected_128_windows/selected_128">HF selected-128 windows</a><br><a href="data/two_evidence_line_result_summary.json">result summary data</a><br><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TWO_EVIDENCE_LINES.md">evidence-line note</a></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <table class="line-table" aria-label="Two-line method-block architecture"> | |
| <thead> | |
| <tr> | |
| <th>Evidence line</th> | |
| <th>Method block</th> | |
| <th>Methods</th> | |
| <th>Score statement</th> | |
| <th>Read as</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td>1 sample episode</td> | |
| <td>Task-head baselines</td> | |
| <td>Minimal; Neural MLP</td> | |
| <td>40/40 direct scores.</td> | |
| <td>Task-lab reproducibility and simple-vs-neural behavior.</td> | |
| </tr> | |
| <tr> | |
| <td>128 selected episodes</td> | |
| <td>Aligned baseline heads</td> | |
| <td>Metadata simple/NN; raw-feature simple/NN</td> | |
| <td>80/80 scores: 74 direct + 6 compact-proxy.</td> | |
| <td>Same-split metadata/raw-feature baseline comparison.</td> | |
| </tr> | |
| <tr> | |
| <td>128 selected episodes</td> | |
| <td>Qwen3-Omni series</td> | |
| <td>Qwen3-Omni v6 LoRA</td> | |
| <td>20/20 direct scores from verified selected-128 Qwen3-Omni LoRA and task-specific probes.</td> | |
| <td>Trainable Qwen3-Omni diagnostic baseline on the selected-128 surface.</td> | |
| </tr> | |
| <tr> | |
| <td>128 selected episodes</td> | |
| <td>Cosmos3 series</td> | |
| <td>Cosmos3-Super Reasoner; Cosmos3-Nano Future Window</td> | |
| <td>40/40 direct scores from verified public-safe reasoner and future-window artifacts.</td> | |
| <td>Cosmos3 reasoner and future-window diagnostics on the selected-128 surface.</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <p class="table-note">Cosmos3-Super Forward-Dynamics LoRA is published as a separate fine-tuned adapter with weights/results; it is not counted as a 20-task matrix method row.</p> | |
| <table class="line-table qwen-lineage-table" aria-label="Qwen3-Omni run version ladder"> | |
| <thead> | |
| <tr> | |
| <th>Qwen run</th> | |
| <th>Purpose</th> | |
| <th>Main change</th> | |
| <th>Eval signal</th> | |
| <th>Use now</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td>v1</td> | |
| <td>Prove the selected-128 LoRA/eval/package loop.</td> | |
| <td>First verified 96/16/16 selected-episode Qwen3-Omni LoRA run.</td> | |
| <td>448 eval; JSON 0.8750; contact 0.6451.</td> | |
| <td>Lineage only.</td> | |
| </tr> | |
| <tr> | |
| <td>v2</td> | |
| <td>Make answers schema-checked.</td> | |
| <td>Structured-JSON contract with full-8-GPU LoRA on the same split.</td> | |
| <td>448 eval; JSON 0.9978; contact 0.7188.</td> | |
| <td>Structured-output ablation.</td> | |
| </tr> | |
| <tr> | |
| <td>v3</td> | |
| <td>Separate prompt/eval effects from training.</td> | |
| <td>Strict-label prompt/eval over the v2 adapter; no new adapter training.</td> | |
| <td>448 eval; JSON 1.0000; contact 0.7210.</td> | |
| <td>Prompt/eval ablation.</td> | |
| </tr> | |
| <tr> | |
| <td>v4</td> | |
| <td>Test longer structured-JSON LoRA training.</td> | |
| <td>New four-epoch full-8-GPU adapter on the same selected split.</td> | |
| <td>448 eval; JSON 1.0000; contact 0.7299.</td> | |
| <td>Overfit/metric-tradeoff evidence.</td> | |
| </tr> | |
| <tr> | |
| <td>v5</td> | |
| <td>Move to denser multiscale evaluation.</td> | |
| <td>Multiscale cap96 export with 4,032 held-out predictions.</td> | |
| <td>4,032 eval; JSON 1.0000; contact 0.7865.</td> | |
| <td>Pinned prior release; stronger on several non-contact metrics.</td> | |
| </tr> | |
| <tr> | |
| <td>v6</td> | |
| <td>Publish the current Qwen 20-task row.</td> | |
| <td>Rank64/lr5e-5 multiscale LoRA plus verified task-specific probes.</td> | |
| <td>4,032 eval; JSON 0.9990; contact 0.8177.</td> | |
| <td>Current public 20-task Qwen3-Omni row.</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <p class="table-note">Qwen v1-v6 are run-lineage labels inside the selected-128 evidence line, not project evidence lines. Use v6 for the public 20-task Qwen3-Omni row; keep v5 as the pinned prior multiscale comparator; read v1-v4 as pipeline-hardening and ablation evidence. Full details are available in the <a href="data/qwen3_omni_run_lineage.json">Qwen lineage data</a> and <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/QWEN3_OMNI_RUN_LINEAGE.md">Qwen lineage note</a>.</p> | |
| <div class="brief-panel reader-map-panel" id="reader-map"> | |
| <div class="brief-panel-head"> | |
| <div> | |
| <span>Evidence map</span> | |
| <h3>Project links stay tied to the evidence trail.</h3> | |
| </div> | |
| <p>Source code, visual explanations, derived artifacts, model outputs, and release checks are organized across GitHub, GitHub Pages, and Hugging Face.</p> | |
| </div> | |
| <p class="table-note">This panel maps each published mirror to its evidence type. The <a href="#reading-path">project path</a> gives the sequence, the <a href="#glossary">glossary</a> defines terminology, and the <a href="#artifacts">artifact library</a> keeps file-level links.</p> | |
| <div class="surface-map" aria-label="Published mirror responsibilities"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite"><strong>GitHub repo</strong><span>Source of truth for docs, scripts, generated data, validators, and commit history.</span></a> | |
| <a href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/"><strong>GitHub Pages</strong><span>Visual dashboard for sample, tasks, results, directions, and resources.</span></a> | |
| <a href="https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite"><strong>HF Space</strong><span>Hub-hosted dashboard and static app assets.</span></a> | |
| <a href="https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts"><strong>HF artifacts</strong><span>Public-safe derived reports, metrics, website data, and result packages.</span></a> | |
| <a href="https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines"><strong>HF baselines</strong><span>Compact baseline weights, figures, metrics, and mirrored task artifacts.</span></a> | |
| <a href="https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results"><strong>HF weights + results</strong><span>Consolidated baseline weights, adapters, result summaries, analysis, and manifest.</span></a> | |
| <a href="https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite"><strong>HF collection</strong><span>Grouped project surfaces, baseline repos, Qwen3-Omni v6, Cosmos3-Super, and Cosmos3-Nano repos.</span></a> | |
| </div> | |
| <div class="brief-actions"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PUBLIC_READER_MAP.md">Open evidence-map guide</a> | |
| <a href="data/public_reader_map.json">Open map data</a> | |
| <a href="#artifacts">Open artifact index</a> | |
| </div> | |
| </div> | |
| <div class="brief-panel"> | |
| <div class="brief-panel-head"> | |
| <div> | |
| <span>Project brief</span> | |
| <h3>From one public episode to an extensible embodied-AI task lab.</h3> | |
| </div> | |
| <p>Xperience-10M is much larger than the public sample. This project focuses on the sample available now, turns it into clear task contracts and baseline artifacts, and keeps the same data contract ready for held-out multi-episode training when more episodes are prepared.</p> | |
| </div> | |
| <div class="brief-grid" aria-label="Project brief cards"> | |
| <article class="brief-card"> | |
| <strong>What this is</strong> | |
| <p>A research-development lab for understanding synchronized egocentric multimodal data, defining embodied-AI tasks, and testing small baselines before omni-model fine-tuning.</p> | |
| </article> | |
| <article class="brief-card"> | |
| <strong>What is implemented</strong> | |
| <ul> | |
| <li>1,161 aligned windows from one public sample episode</li> | |
| <li>20 unified task contracts with minimal and neural evidence</li> | |
| <li>One shared setup across all 20 task axes</li> | |
| <li>Four research-direction maps and extension probes</li> | |
| </ul> | |
| </article> | |
| <article class="brief-card"> | |
| <strong>What comes next</strong> | |
| <p>The next model-quality stage is stronger action/subtask modeling on the same held-out split, using dense/multiscale windows before requiring more raw episodes.</p> | |
| </article> | |
| </div> | |
| <div class="brief-grid" aria-label="Research capability map"> | |
| <article class="brief-card"> | |
| <strong>Data understanding</strong> | |
| <p>Maps one public episode into synchronized windows across video, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals.</p> | |
| </article> | |
| <article class="brief-card"> | |
| <strong>Task design</strong> | |
| <p>Defines embodied-AI inputs, process modules, outputs, metrics, and case-study walkthroughs instead of treating the sample as a generic classification file.</p> | |
| </article> | |
| <article class="brief-card"> | |
| <strong>Evaluation discipline</strong> | |
| <p>Keeps chronological splits, predictions, confusion matrices, leakage notes, and single-episode limits visible before moving to broader model-quality reads.</p> | |
| </article> | |
| <article class="brief-card"> | |
| <strong>Scale-up readiness</strong> | |
| <p>Connects the same data contract to 128-episode baselines, a no-new-episode enhancement pack, Qwen3-Omni LoRA, Cosmos-style world modeling, policy/VLA tracks, and the later Xperience-native pretraining goal.</p> | |
| </article> | |
| </div> | |
| <div class="brief-actions"> | |
| <a href="#roadmap">Open roadmap summary</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PROJECT_BRIEF.md">Read the brief</a> | |
| <a href="data/project_brief.json">Project summary</a> | |
| <a href="research_roadmap.html">Roadmap details</a> | |
| <a href="#reading-path">Project path</a> | |
| <a href="#takeaways">Current takeaways</a> | |
| </div> | |
| </div> | |
| <div class="brief-panel"> | |
| <div class="brief-panel-head"> | |
| <div> | |
| <span>Result snapshot</span> | |
| <h3>One comparison story, detailed charts in the task-suite section.</h3> | |
| </div> | |
| <p>The overview keeps only the headline comparison so the same radar visuals are not repeated. Open the task-suite section for the enlarged unified radar, the 1-episode split radar, the 128-episode split radar, and the source-linked 180-record table.</p> | |
| </div> | |
| <div class="brief-grid"> | |
| <article class="brief-card"> | |
| <strong>1-episode line</strong> | |
| <p>Minimal and Neural MLP baselines cover 40/40 method-task records on the original public-sample episode.</p> | |
| </article> | |
| <article class="brief-card"> | |
| <strong>128-episode line</strong> | |
| <p>Metadata, raw-feature, Qwen3-Omni, and Cosmos3 methods cover 140/140 rows with direct/proxy evidence notes preserved.</p> | |
| </article> | |
| <article class="brief-card"> | |
| <strong>Best detailed view</strong> | |
| <p>The task-suite section holds the large readable radars, normalized-score notes, and the downloadable chart data.</p> | |
| </article> | |
| </div> | |
| <div class="brief-actions"> | |
| <a href="#suite">Open detailed radars</a> | |
| <a href="data/task_method_20_result_matrix.json">Inspect 180-result table</a> | |
| <a href="data/unified_task_model_radar.json">Inspect radar source</a> | |
| </div> | |
| </div> | |
| <div class="snapshot-grid"> | |
| <article class="snapshot-card"> | |
| <span class="status-pill">featured</span> | |
| <h3>Research roadmap details</h3> | |
| <p>This section links the unified 20 tasks, four research tracks, current sample evidence, and the multi-episode Qwen3-Omni scale-up path.</p> | |
| <div class="snapshot-meta"> | |
| <span>tracks <strong>4</strong></span> | |
| <span>tasks <strong>20</strong></span> | |
| <span>setup <strong>unified</strong></span> | |
| <span>roadmap phases <strong>5</strong></span> | |
| </div> | |
| <div class="snapshot-actions"> | |
| <a href="#roadmap">Open roadmap summary</a> | |
| <a href="research_roadmap.html">Roadmap details</a> | |
| </div> | |
| </article> | |
| <article class="snapshot-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Multimodal episode pipeline</h3> | |
| <p>One Xperience-10M public sample episode is converted into aligned windows and a documented feature contract.</p> | |
| <div class="snapshot-meta"> | |
| <span>frames <strong>5,821</strong></span> | |
| <span>windows <strong>1,161</strong></span> | |
| <span>features <strong>8,546</strong></span> | |
| </div> | |
| </article> | |
| <article class="snapshot-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Task suite and baseline heads</h3> | |
| <p>The unified task suite has minimal and neural baseline evidence across one 20-axis task surface with shared windows, splits, and label discipline.</p> | |
| <div class="snapshot-meta"> | |
| <span>tasks <strong>20</strong></span> | |
| <span>minimal heads <strong>20</strong></span> | |
| <span>neural heads <strong>20</strong></span> | |
| </div> | |
| </article> | |
| <article class="snapshot-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Dataset source alignment</h3> | |
| <p>The public description is aligned to the official gated Xperience-10M Hugging Face dataset card, including modalities, scale, access, and current project coverage. The source snapshot records 31.9 TB on the HF dataset surface, an about-1PB full-scale storage statement, 12,103 episode folders as upstream metadata, not a local data inventory, public sample license cc-by-nc-4.0, HOMIE Toolkit and Rerun 0.29.0 source tooling, and the official limited diversity note. See the source-alignment record for exact provenance.</p> | |
| <div class="snapshot-meta"> | |
| <span>full dataset <strong>HF gated</strong></span> | |
| <span>sample scope <strong>1 episode</strong></span> | |
| <span>raw data mirrored <strong>no</strong></span> | |
| </div> | |
| </article> | |
| <article class="snapshot-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Public research artifacts</h3> | |
| <p>Metrics, figures, walkthroughs, baseline weights, Qwen3-Omni results, and Cosmos3 public-safe packages are staged across GitHub, GitHub Pages, and Hugging Face.</p> | |
| <div class="snapshot-meta"> | |
| <span>tasks <strong>20</strong></span> | |
| <span>baselines <strong>minimal + neural</strong></span> | |
| <span>project route <strong>tabs</strong></span> | |
| </div> | |
| </article> | |
| <article class="snapshot-card gated"> | |
| <span class="status-pill">verified diagnostic</span> | |
| <h3>Qwen3-Omni held-out pilot</h3> | |
| <p>The first selected-episode LoRA pilot is packaged with real held-out predictions and metrics. It proves the pipeline, while the weak scores make it a baseline for error analysis.</p> | |
| <div class="snapshot-meta"> | |
| <span>split <strong>96 / 16 / 16</strong></span> | |
| <span>test windows <strong>4,032</strong></span> | |
| <span>JSON validity <strong>99.90%</strong></span> | |
| </div> | |
| </article> | |
| <article class="snapshot-card gated"> | |
| <span class="status-pill">current plan</span> | |
| <h3>No-new-episode stress plan</h3> | |
| <p>Shows how the current selected split can be stressed without more episodes: dense windows, hierarchical labels, raw-feature shards, and `multiscale_20s10_40s20_80s40` as the next export target.</p> | |
| <div class="snapshot-meta"> | |
| <span>current windows <strong>3,808</strong></span> | |
| <span>multiscale estimate <strong>106,095</strong></span> | |
| <span>data reference <strong>selected-128 enhancement plan</strong></span> | |
| </div> | |
| </article> | |
| <article class="snapshot-card gated"> | |
| <span class="status-pill">not redistributed</span> | |
| <h3>Data governance</h3> | |
| <p>Raw MP4/HDF5/RRD files, private gated Xperience-10M data, and full Qwen weights are excluded from the public repo and HF mirrors.</p> | |
| <div class="snapshot-meta"> | |
| <span>raw Xperience-10M <strong>excluded</strong></span> | |
| <span>full Qwen weights <strong>excluded</strong></span> | |
| <span>derived artifacts <strong>included</strong></span> | |
| </div> | |
| </article> | |
| </div> | |
| <div class="snapshot-actions"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PROJECT_BRIEF.md">Project brief</a> | |
| <a href="data/project_brief.json">Summary mirror</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PROJECT_STATUS.md">Project status</a> | |
| <a href="data/project_status.json">Status mirror</a> | |
| <a href="data/research_roadmap.json">Roadmap mirror</a> | |
| <a href="data/project_packet.json">Project packet</a> | |
| <a href="#artifacts">Project materials</a> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="dataset-card" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Aligned with the official dataset card.</h2> | |
| <p>The official Xperience-10M card describes a gated, large-scale 4D egocentric multimodal dataset. This project records that full upstream scope while focusing the implemented artifacts on one public sample episode. The source-alignment record keeps 31.9 TB, about-1PB, 12,103 episode folders, cc-by-nc-4.0, HOMIE Toolkit, Rerun 0.29.0, not a local data inventory, and limited diversity visible on the public site.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"><div><h3>Official dataset</h3><p>Xperience-10M is a gated large-scale egocentric multimodal dataset for embodied AI, robotics, spatial intelligence, and world modeling.</p></div><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">official HF dataset</a></article> | |
| <article class="artifact"><h3>Line 1 public sample</h3><p>The one-episode line builds the inspectable 20-task lab. Use Line 2 for selected-128 held-out comparison.</p><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">sample dataset</a></article> | |
| <article class="artifact"><h3>Public sample inspection</h3><p>One public sample episode is exposed through the raw browser and modality atlas: synchronized video, embedded audio, HDF5 annotation groups, depth, pose/SLAM, mocap, IMU, calibration, language-derived signals, and source links in one place.</p><a href="#raw-sample">open raw browser</a><a href="data/raw_sample_files.json">raw manifest</a><a href="data/modality_atlas.json">stream metadata</a></article> | |
| <article class="artifact"><h3>Data explorer analysis</h3><p>Compare the one-sample line, selected-128 feature exports, and Hugging Face gated full-dataset metadata with scope stats, split counts, modality breakdowns, and readable charts.</p><a href="#data-analysis">open analysis</a><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/DATA_EXPLORER_ANALYSIS.md">analysis report</a></article> | |
| <article class="artifact"><h3>Multi-episode pilot</h3><p>The selected 128-episode Qwen3-Omni LoRA v6 diagnostic run is verified with 4,032 held-out test predictions and 99.90% JSON validity. Action/subtask metrics are still weak, so this remains a baseline for error analysis.</p><a href="https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep">LoRA adapter</a><a href="data/qwen3_v5_v6_comparison.json">v5/v6 comparison</a></article> | |
| <article class="artifact"><h3>Data boundary</h3><p>Raw MP4, HDF5, RRD files are streamed from the official public sample source when opened here; private gated data and full Qwen weights are not redistributed in this project.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/DATA_NOTICE.md">data notice</a></article> | |
| <article class="artifact"><h3>Implemented subset</h3><p>The public task lab uses one sample episode with 5,821 frames, 1,161 aligned windows, 8,546-dimensional task inputs, and 20 integrated task contracts.</p><a href="#suite">task suite</a><a href="data/task_suite_20.json">task contract data</a></article> | |
| <article class="artifact"><h3>Covered now</h3><p>Action/subtask labels, next-action prediction, temporal diagnostics, hand trajectory, contact, object relevance, caption grounding, retrieval, reconstruction, misalignment, long-horizon forecasting, interaction text, action-object relation, sensor bridging, camera sync, and transition timing.</p><a href="data/summary_metrics.json">summary metrics</a></article> | |
| <article class="artifact"><h3>Responsible use</h3><p>This project is for research exploration and excludes identity recognition, surveillance, biometric profiling, sensitive-attribute inference, and safety-critical deployment.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/DATA_NOTICE.md">use notes</a></article> | |
| <article class="artifact"><h3>Later milestones</h3><p>Full audio-visual learning, caption generation, depth-pixel prediction, SLAM estimation, neural rendering, policy learning, cross-episode generalization, held-out Qwen3-Omni evaluation, and future Xperience-native pretraining.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">native pretraining</a></article> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="data-analysis" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Data explorer analysis.</h2> | |
| <p>Read the data at three separate scales: the official public sample episode, the selected 128-episode feature surface, and authenticated file metadata for the Hugging Face-hosted gated full dataset. The charts are generated by a reproducible script and keep raw data boundaries explicit.</p> | |
| </div> | |
| <div class="data-scope-explorer" data-scope-explorer data-default-scope="sample" aria-label="Interactive comparison of three Xperience-10M data scopes"> | |
| <div class="data-analysis-grid" role="group" aria-label="Choose a data scope"> | |
| <button type="button" class="data-analysis-card" data-scope="sample" aria-pressed="true"> | |
| <small>scope 1 / public sample</small> | |
| <h3>One inspectable episode.</h3> | |
| <p>The browser-facing sample is the concrete data lab: six synchronized videos, embedded audio, one annotation HDF5, one Rerun recording, and aligned 20-frame windows.</p> | |
| <div class="data-analysis-metrics"> | |
| <span><strong>5,821</strong>frames</span> | |
| <span><strong>1,161</strong>20-frame windows</span> | |
| <span><strong>8,546</strong>input dimensions</span> | |
| <span><strong>4.76 GiB</strong>sample files</span> | |
| </div> | |
| </button> | |
| <button type="button" class="data-analysis-card" data-scope="selected" aria-pressed="false"> | |
| <small>scope 2 / selected 128</small> | |
| <h3>Held-out comparison surface.</h3> | |
| <p>The selected-128 line keeps one episode per top-level session, a fixed train/val/test split, public-safe feature matrices, and multiscale JSONL windows for model scoring.</p> | |
| <div class="data-analysis-metrics"> | |
| <span><strong>96/16/16</strong>train/val/test episodes</span> | |
| <span><strong>34,269</strong>Qwen v6 rows</span> | |
| <span><strong>106,095</strong>dense compact rows</span> | |
| <span><strong>277.71 GiB</strong>source size view</span> | |
| </div> | |
| </button> | |
| <button type="button" class="data-analysis-card" data-scope="full" aria-pressed="false"> | |
| <small>scope 3 / HF gated full dataset</small> | |
| <h3>Full-corpus metadata.</h3> | |
| <p>The official dataset remains gated. This project records file-level Hub metadata to show scale without downloading private raw MP4, HDF5, or RRD files.</p> | |
| <div class="data-analysis-metrics"> | |
| <span><strong>804</strong>top-level sessions</span> | |
| <span><strong>12,103</strong>episode-like folders</span> | |
| <span><strong>85,257</strong>files</span> | |
| <span><strong>24.63 TiB</strong>training bytes</span> | |
| </div> | |
| </button> | |
| </div> | |
| <div class="data-scope-detail" aria-live="polite"> | |
| <div> | |
| <small data-scope-detail-kicker>selected scope / public sample</small> | |
| <h3 data-scope-detail-title>Public sample episode: inspectable raw evidence.</h3> | |
| <p data-scope-detail-body>The public sample is the only raw episode mirrored into the browser. Use it to understand file roles, synchronized streams, HDF5 contents, modality features, and how one episode becomes 20-frame task windows.</p> | |
| </div> | |
| <div class="data-scope-facts" data-scope-detail-facts aria-label="Selected data-scope facts"> | |
| <span><strong>Raw contents</strong>6 MP4 streams, audio in MP4, annotation.hdf5, and visualization.rrd.</span> | |
| <span><strong>Training view</strong>1,161 aligned windows with 8,546 feature dimensions.</span> | |
| <span><strong>Best use</strong>File inspection, task construction, and local reproducibility.</span> | |
| <span><strong>Boundary</strong>One official episode; no diversity or scale claim.</span> | |
| </div> | |
| </div> | |
| <div class="data-scope-flow" aria-label="How the three data scopes relate"> | |
| <div class="data-scope-flow-item"><strong>1. Inspect one episode</strong><span>Open media, HDF5, RRD, labels, and synchronized windows directly.</span></div> | |
| <div class="data-scope-flow-arrow" aria-hidden="true">→</div> | |
| <div class="data-scope-flow-item"><strong>2. Score selected 128</strong><span>Use public-safe derived rows for same-split baselines and model probes.</span></div> | |
| <div class="data-scope-flow-arrow" aria-hidden="true">→</div> | |
| <div class="data-scope-flow-item"><strong>3. Read HF full scale</strong><span>Use authenticated Hugging Face file metadata to understand upstream corpus size.</span></div> | |
| </div> | |
| </div> | |
| <div class="data-analysis-table"> | |
| <div class="data-analysis-table-header"> | |
| <div> | |
| <small>evidence map</small> | |
| <h3>What each data scale can answer.</h3> | |
| </div> | |
| <p>The goal is to make the data understandable without mixing raw public sample inspection, selected-128 derived artifacts, and Hugging Face gated full-dataset metadata into one ambiguous inventory.</p> | |
| </div> | |
| <table class="line-table"> | |
| <thead> | |
| <tr> | |
| <th>Data scale</th> | |
| <th>Use case</th> | |
| <th>What is counted</th> | |
| <th>Boundary</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td><strong>Public sample</strong></td> | |
| <td>Raw file roles, synchronized streams, action/object timelines, and how one episode becomes task windows.</td> | |
| <td>Files, frames, 20-frame windows, modality feature dimensions, action labels, object labels, HDF5 groups.</td> | |
| <td>One official sample episode only; it is not a diversity or generalization claim.</td> | |
| </tr> | |
| <tr> | |
| <td><strong>Selected 128</strong></td> | |
| <td>Same-split baselines, model scoring surfaces, feature export density, and 128-episode task coverage.</td> | |
| <td>Selected episodes, splits, size bands, feature rows, multiscale exports, raw20/proxy status.</td> | |
| <td>Public-safe derived artifacts; raw selected-episode data remains governed by the official gated dataset.</td> | |
| </tr> | |
| <tr> | |
| <td><strong>Full HF dataset</strong></td> | |
| <td>Scale of the upstream dataset: sessions, episode folders, file counts, file types, and storage footprint.</td> | |
| <td>Authenticated Hub file metadata: HDF5 count, MP4 count, RRD count, complete episode folders, total bytes.</td> | |
| <td>Metadata-only. Dataset Viewer row/stat endpoints are gated and raw contents are not redistributed.</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| <div class="data-analysis-chart-grid" aria-label="Generated data explorer charts"> | |
| <article class="data-chart-card wide" data-scope-card="overview"> | |
| <div class="data-chart-meta"><small>scale comparison</small><span class="chart-scope-chip">all scopes</span></div> | |
| <h3>One sample, selected 128, and the HF full dataset are different reading scopes.</h3> | |
| <p>Log-scale bars keep the large jumps visible without hiding the small public-sample line.</p> | |
| <img src="assets/charts/data_explorer_scope_ladder.svg?v=xperience10m-data-explorer-v1" alt="Scope ladder chart comparing public sample, selected 128 episodes, and the Hugging Face gated full dataset by episode count, window rows, and training bytes." loading="lazy" decoding="async"> | |
| </article> | |
| <article class="data-chart-card" data-scope-card="sample"> | |
| <div class="data-chart-meta"><small>sample feature anatomy</small><span class="chart-scope-chip">public sample</span></div> | |
| <h3>Video dominates the 8,546-dimensional public-sample input.</h3> | |
| <p>Motion capture, depth, language, pose/SLAM, audio, and inertial signals remain explicit feature groups.</p> | |
| <img src="assets/charts/data_explorer_sample_feature_modalities.svg?v=xperience10m-data-explorer-v1" alt="Bar chart of public sample feature dimensions by modality group." loading="lazy" decoding="async"> | |
| </article> | |
| <article class="data-chart-card" data-scope-card="sample"> | |
| <div class="data-chart-meta"><small>sample timeline labels</small><span class="chart-scope-chip">public sample</span></div> | |
| <h3>Window labels show which actions occupy the sample.</h3> | |
| <p>This separates what the one-episode line can and cannot represent before the task cards.</p> | |
| <img src="assets/charts/data_explorer_sample_action_distribution.svg?v=xperience10m-data-explorer-v1" alt="Bar chart of the most frequent action labels by 20-frame window count." loading="lazy" decoding="async"> | |
| </article> | |
| <article class="data-chart-card" data-scope-card="selected"> | |
| <div class="data-chart-meta"><small>selected-128 exports</small><span class="chart-scope-chip">selected 128</span></div> | |
| <h3>Feature rows grow by export density and window scale.</h3> | |
| <p>The selected-128 split is stable; sparse, Qwen v6 multiscale, and dense compact exports expose different row counts.</p> | |
| <img src="assets/charts/data_explorer_selected128_split_rows.svg?v=xperience10m-data-explorer-v1" alt="Stacked bar chart of selected-128 processed rows by train, validation, and test split." loading="lazy" decoding="async"> | |
| </article> | |
| <article class="data-chart-card" data-scope-card="full"> | |
| <div class="data-chart-meta"><small>HF full-dataset metadata</small><span class="chart-scope-chip">HF metadata</span></div> | |
| <h3>The Hugging Face full dataset is mostly repeated episode file structure.</h3> | |
| <p>The counts come from the gated <a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">ropedia-ai/xperience-10m</a> repository on Hugging Face. Each complete episode usually has one annotation HDF5 and six MP4 streams; RRD viewer recordings are optional.</p> | |
| <img src="assets/charts/data_explorer_full_file_composition.svg?v=xperience10m-data-explorer-v1" alt="Log-scale file composition chart for the gated Hugging Face full-dataset metadata." loading="lazy" decoding="async"> | |
| </article> | |
| </div> | |
| <p class="data-analysis-note"><strong>Source boundary:</strong> the full-corpus counts are the Hugging Face-hosted dataset version: authenticated Hugging Face Hub file metadata for <a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">ropedia-ai/xperience-10m</a>. They describe repository scale and file organization; they do not inspect private row contents or redistribute raw data.</p> | |
| <div class="data-analysis-actions"> | |
| <a class="button" href="#raw-sample">Open raw sample browser</a> | |
| <a class="button" href="#suite">Open 20-task suite</a> | |
| <a class="button" href="data/data_explorer_analysis.json">Open structured analysis</a> | |
| <a class="button" href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/DATA_EXPLORER_ANALYSIS.md">Open analysis report</a> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="raw-sample" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Raw public sample browser.</h2> | |
| <p>Open each official Xperience-10M sample file from the project page. Video and audio use compact browser previews derived from the official MP4 files, with direct links beside them for the full raw Hugging Face sources. HDF5 and RRD files are shown with their role, size, organization, and direct source links.</p> | |
| </div> | |
| <div class="raw-sample-layout"> | |
| <article class="raw-player-panel" id="rawPlayerPanel"> | |
| <div class="raw-player-top"> | |
| <div> | |
| <h3 id="rawFileTitle">fisheye_cam0.mp4</h3> | |
| <p id="rawFileDescription">Fisheye camera 0 stream and the public sample audio source. This file can be played as video and as the embedded audio track.</p> | |
| </div> | |
| <span class="raw-kind-pill" id="rawKindPill">video + audio</span> | |
| </div> | |
| <div class="raw-video-frame" id="rawVideoFrame"> | |
| <video id="rawVideo" controls preload="metadata" playsinline poster="assets/raw-sample-preview/fisheye_cam0_poster.jpg"> | |
| <source src="assets/raw-sample-preview/fisheye_cam0_preview.mp4" type="video/mp4"> | |
| </video> | |
| </div> | |
| <p class="raw-preview-note" id="rawPreviewNote">Playing a 12 second fast-start preview derived from the official raw MP4. Use the source link for the complete file.</p> | |
| <div class="raw-audio-frame" id="rawAudioFrame"> | |
| <strong>Embedded audio preview from fisheye_cam0.mp4</strong> | |
| <audio id="rawAudio" controls preload="metadata"> | |
| <source src="assets/raw-sample-preview/fisheye_cam0_preview.mp4" type="video/mp4"> | |
| </audio> | |
| </div> | |
| <p class="atlas-note" id="rawFileUse">Video features feed visual tasks; the embedded audio stream feeds audio ablation and acoustic feature blocks.</p> | |
| <div class="raw-file-detail-map hdf5-map" id="rawFileDetails" hidden></div> | |
| <div class="raw-link-row"> | |
| <a id="rawOpenPreview" href="assets/raw-sample-preview/fisheye_cam0_preview.mp4">open playable preview</a> | |
| <a id="rawOpenSource" href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample/resolve/main/fisheye_cam0.mp4">open full raw source</a> | |
| <a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">official sample dataset</a> | |
| <a href="data/raw_sample_files.json">machine-readable manifest</a> | |
| </div> | |
| </article> | |
| <aside class="raw-file-panel" aria-label="Raw sample files"> | |
| <button type="button" class="raw-file-button active" data-raw-file="fisheye_cam0.mp4"><img class="raw-file-thumb" src="assets/raw-sample-preview/fisheye_cam0_poster.jpg" alt="" loading="lazy"><strong>fisheye_cam0.mp4</strong><em>89.84 MB</em><span>Fisheye video plus embedded audio. Playable preview, full raw source linked.</span></button> | |
| <button type="button" class="raw-file-button" data-raw-file="fisheye_cam1.mp4"><img class="raw-file-thumb" src="assets/raw-sample-preview/fisheye_cam1_poster.jpg" alt="" loading="lazy"><strong>fisheye_cam1.mp4</strong><em>127.09 MB</em><span>Second synchronized fisheye camera stream. Playable preview.</span></button> | |
| <button type="button" class="raw-file-button" data-raw-file="fisheye_cam2.mp4"><img class="raw-file-thumb" src="assets/raw-sample-preview/fisheye_cam2_poster.jpg" alt="" loading="lazy"><strong>fisheye_cam2.mp4</strong><em>113.41 MB</em><span>Third synchronized fisheye camera stream. Playable preview.</span></button> | |
| <button type="button" class="raw-file-button" data-raw-file="fisheye_cam3.mp4"><img class="raw-file-thumb" src="assets/raw-sample-preview/fisheye_cam3_poster.jpg" alt="" loading="lazy"><strong>fisheye_cam3.mp4</strong><em>95.02 MB</em><span>Fourth synchronized fisheye camera stream. Playable preview.</span></button> | |
| <button type="button" class="raw-file-button" data-raw-file="stereo_left.mp4"><img class="raw-file-thumb" src="assets/raw-sample-preview/stereo_left_poster.jpg" alt="" loading="lazy"><strong>stereo_left.mp4</strong><em>22.67 MB</em><span>Left stereo RGB stream. Playable preview.</span></button> | |
| <button type="button" class="raw-file-button" data-raw-file="stereo_right.mp4"><img class="raw-file-thumb" src="assets/raw-sample-preview/stereo_right_poster.jpg" alt="" loading="lazy"><strong>stereo_right.mp4</strong><em>25.42 MB</em><span>Right stereo RGB stream. Playable preview.</span></button> | |
| <button type="button" class="raw-file-button" data-raw-file="annotation.hdf5"><span class="raw-file-thumb raw-file-kind" aria-hidden="true">H5</span><strong>annotation.hdf5</strong><em>1.93 GB</em><span>Canonical annotation container: captions, labels, depth, pose/SLAM, mocap, IMU, calibration.</span></button> | |
| <button type="button" class="raw-file-button" data-raw-file="visualization.rrd"><span class="raw-file-thumb raw-file-kind" aria-hidden="true">RRD</span><strong>visualization.rrd</strong><em>2.70 GB</em><span>Rerun viewer recording for visual inspection; separate from training and evaluation inputs.</span></button> | |
| </aside> | |
| </div> | |
| <div class="raw-detail-grid"> | |
| <article class="raw-detail-card"> | |
| <h3>Sample folder organization</h3> | |
| <p>The official public sample is one episode folder. The task suite reads the HDF5 annotations and six synchronized MP4 streams, then writes 20-frame windows with a 5-frame stride.</p> | |
| <pre class="raw-tree">xperience-10m-sample/ | |
| annotation.hdf5 | |
| fisheye_cam0.mp4 | |
| fisheye_cam1.mp4 | |
| fisheye_cam2.mp4 | |
| fisheye_cam3.mp4 | |
| stereo_left.mp4 | |
| stereo_right.mp4 | |
| visualization.rrd</pre> | |
| </article> | |
| <article class="raw-detail-card"> | |
| <h3>annotation.hdf5 group map</h3> | |
| <p>The raw HDF5 is a binary container, so the browser shows its organization rather than loading the whole file into memory.</p> | |
| <div class="hdf5-map"> | |
| <article><strong>calibration</strong><span>Camera intrinsics/extrinsics and static alignment values.</span></article> | |
| <article><strong>caption</strong><span>JSON text with actions, objects, interactions, segments, and global summary.</span></article> | |
| <article><strong>depth</strong><span>Depth maps and confidence channels aligned to the episode timeline.</span></article> | |
| <article><strong>full_body_mocap</strong><span>Full-body joint and contact signals for human motion modeling.</span></article> | |
| <article><strong>hand_mocap</strong><span>Left and right hand joint trajectories used by forecast tasks.</span></article> | |
| <article><strong>imu</strong><span>Accelerometer and gyroscope streams sampled above video rate.</span></article> | |
| <article><strong>metadata</strong><span>Episode metadata, frame indexing, and source bookkeeping.</span></article> | |
| <article><strong>slam</strong><span>Camera trajectory, pose, and sparse SLAM point-cloud information.</span></article> | |
| <article><strong>video</strong><span>Video metadata and per-frame alignment information.</span></article> | |
| </div> | |
| </article> | |
| <article class="raw-detail-card"> | |
| <h3>Stream-to-feature use</h3> | |
| <p>The source streams are summarized once here, next to the playable files and HDF5 map.</p> | |
| <div class="hdf5-map"> | |
| <article><strong>Video</strong><span>Six synchronized MP4 streams feed RGB, fisheye, stereo, and frame-statistic task inputs.</span></article> | |
| <article><strong>Audio</strong><span>The embedded fisheye_cam0 audio feeds acoustic feature blocks and audio ablations.</span></article> | |
| <article><strong>Depth</strong><span>Depth maps and confidence channels provide geometry signals for spatial and reconstruction probes.</span></article> | |
| <article><strong>Pose / SLAM</strong><span>Trajectory, camera pose, and sparse map values become position and orientation features.</span></article> | |
| <article><strong>Motion capture</strong><span>Body and hand joint tracks support motion, contact, hand forecast, and policy-style targets.</span></article> | |
| <article><strong>Inertial</strong><span>Accelerometer and gyroscope streams become wearable-motion statistics.</span></article> | |
| <article><strong>Language</strong><span>Object tags and caption-derived labels become semantic targets; raw caption text remains governed by the official sample.</span></article> | |
| </div> | |
| <p class="atlas-note">Small derived modality thumbnails remain in the <a href="data/modality_atlas.json">modality atlas data</a>; raw MP4, HDF5, and RRD files are not redistributed.</p> | |
| </article> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="suite" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Xperience-10M 20-task suite.</h2> | |
| <p>Task map, radar comparisons, task cards, and the 180-result table are kept in one section. The map introduces the task axes, the score surfaces compare methods, and each task card lists input, process, output, metric, and all nine public method scores.</p> | |
| </div> | |
| <nav class="suite-jump-row" aria-label="20-task suite quick links"> | |
| <a href="#suite-task-index">20-task index</a> | |
| <a href="#suite-radars">Radars</a> | |
| <a href="#tasks">Task cards</a> | |
| <a href="#task-suite-map">Full map figure</a> | |
| <a href="#result-matrix-table">180-result table</a> | |
| </nav> | |
| <div class="suite-task-index" id="suite-task-index" aria-label="Compact list of all 20 task contracts"> | |
| <div class="suite-task-index-head"> | |
| <span>unified task index</span> | |
| <h3>All 20 tasks are one suite, not a separate tier.</h3> | |
| <p>Each axis below has a task card with nine raw method scores, normalized radar values, source artifacts, and matching method-row entries in the 180-result matrix.</p> | |
| </div> | |
| <div class="task-index-grid"> | |
| <a href="#tasks"><img src="assets/task-icons/01_timeline_action.svg" alt="" loading="lazy"><strong>01 Action Recognition</strong><span>current action label</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/02_timeline_subtask.svg" alt="" loading="lazy"><strong>02 Procedure Step Recognition</strong><span>current subtask label</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/03_transition_detection.svg" alt="" loading="lazy"><strong>03 Action Boundary Detection</strong><span>temporal transition</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/04_next_action.svg" alt="" loading="lazy"><strong>04 Next-Action Prediction</strong><span>near future action</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/05_hand_trajectory_forecast.svg" alt="" loading="lazy"><strong>05 Hand Trajectory Forecasting</strong><span>future hand path</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/06_contact_prediction.svg" alt="" loading="lazy"><strong>06 Contact State Prediction</strong><span>hand/object contact</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/07_object_relevance.svg" alt="" loading="lazy"><strong>07 Object Relevance Prediction</strong><span>task-relevant object</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/08_caption_grounding.svg" alt="" loading="lazy"><strong>08 Language Grounding</strong><span>caption target</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/09_cross_modal_retrieval.svg" alt="" loading="lazy"><strong>09 Cross-Modal Retrieval</strong><span>matched modality window</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/10_modality_reconstruction.svg" alt="" loading="lazy"><strong>10 Cross-Modal Reconstruction</strong><span>missing stream features</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/11_temporal_order.svg" alt="" loading="lazy"><strong>11 Temporal Order Verification</strong><span>before/after order</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/12_misalignment_detection.svg" alt="" loading="lazy"><strong>12 Multimodal Sync Detection</strong><span>aligned vs shifted streams</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/13_long_horizon_next_action.svg" alt="" loading="lazy"><strong>13 Long-Horizon Next-Action Forecasting</strong><span>future action label</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/14_next_subtask_forecast.svg" alt="" loading="lazy"><strong>14 Long-Horizon Next-Subtask Forecasting</strong><span>future subtask label</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/15_interaction_text_prediction.svg" alt="" loading="lazy"><strong>15 Interaction Text Prediction</strong><span>natural interaction class</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/16_action_object_relation.svg" alt="" loading="lazy"><strong>16 Action-Object Relation Prediction</strong><span>action/object pair</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/17_object_set_forecast.svg" alt="" loading="lazy"><strong>17 Future Object-Set Forecasting</strong><span>future object set</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/18_imu_to_hand_pose.svg" alt="" loading="lazy"><strong>18 IMU-to-Hand Pose Reconstruction</strong><span>inertial to hand pose</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/19_camera_view_sync_retrieval.svg" alt="" loading="lazy"><strong>19 Camera-View Sync Retrieval</strong><span>matching camera view</span></a> | |
| <a href="#tasks"><img src="assets/task-icons/20_time_to_transition.svg" alt="" loading="lazy"><strong>20 Time-to-Next-Transition Regression</strong><span>seconds to boundary</span></a> | |
| </div> | |
| </div> | |
| <div class="figure-pan" id="task-suite-map"> | |
| <details class="task-map-details"> | |
| <summary> | |
| <span>Open full generated task-map figure</span> | |
| <strong>Large overview kept here for visual scanning after the compact index.</strong> | |
| </summary> | |
| <div class="task-map-figure-frame"> | |
| <img class="task-suite-image" src="assets/task_suite_infographic.png?v=xperience10m-taskfirst-v14-modality-compact" alt="Infographic showing Xperience-10M task families with compact modality cards and visible thumbnails"> | |
| </div> | |
| </details> | |
| </div> | |
| <div class="figure-brief"> | |
| <article class="figure-brief-card"> | |
| <h3>Unified plus split radars</h3> | |
| <p>The unified radar keeps all nine methods in one comparison board, but groups them into small-multiple panels so each method family can be read directly. The split radars separate the 1-episode Minimal/NN baseline comparison from the 128-episode metadata/raw, Qwen3-Omni v6 LoRA, and Cosmos3-Super/Nano comparison.</p> | |
| </article> | |
| <article class="figure-brief-card"> | |
| <h3>Metric normalization</h3> | |
| <p>Higher-is-better metrics are normalized to 0-1; lower-is-better metrics are converted to best/value within the task. The SVG uses sqrt(normalized score) only for visual radius, while raw values, linear normalized scores, status reasons, sources, and compact proxy notes remain in the data mirrors.</p> | |
| </article> | |
| <article class="figure-brief-card"> | |
| <h3>Score/proxy audit</h3> | |
| <p>The matrix has 180/180 scored method-task records: 174 direct scores and 6 compact-proxy scores. The audit records the source artifact, metric key, and proxy reason for each marked cell.</p> | |
| </article> | |
| </div> | |
| <div class="suite-radar-block" id="suite-radars"> | |
| <div class="split-radar-grid" aria-label="Default split 20-task radar comparisons"> | |
| <article class="split-radar-card"> | |
| <h3>1-Episode 20-Task Radar</h3> | |
| <p>Minimal and Neural MLP are both scored on all 20 public-sample task contracts in one enlarged panel without 128-episode methods competing for attention.</p> | |
| <img src="assets/charts/single_episode_task_model_radar.svg?v=xperience10m-split-radar-v3-readable" alt="Single-episode 20-task radar comparing Minimal and Neural MLP across all 20 scored task axes"> | |
| <div class="split-radar-links"> | |
| <a href="assets/charts/single_episode_task_model_radar.svg">View chart source</a> | |
| <a href="data/single_episode_task_model_radar.json">Inspect chart source</a> | |
| </div> | |
| </article> | |
| <article class="split-radar-card"> | |
| <h3>128-Episode 20-Task Radar</h3> | |
| <p>Seven aligned 128-episode methods cover all 20 axes across metadata/text, raw-feature, and foundation-model panels. Proxy axes stay labeled in the chart and source data.</p> | |
| <img src="assets/charts/episode128_task_model_radar.svg?v=xperience10m-split-radar-v3-readable" alt="128-episode grouped 20-task radar comparing raw-feature baselines, metadata baselines, Qwen3-Omni, and Cosmos3 series with explicit score counts"> | |
| <div class="split-radar-links"> | |
| <a href="assets/charts/episode128_task_model_radar.svg">View chart source</a> | |
| <a href="data/episode128_task_model_radar.json">Inspect chart source</a> | |
| <a href="data/task_method_20_gap_audit.json">Review score/proxy audit</a> | |
| </div> | |
| </article> | |
| </div> | |
| <details class="split-radar-details full-radar-details"> | |
| <summary> | |
| <span>Open full 9-method radar board</span> | |
| <strong>Use after the split radars when you need all methods in one grouped visual analysis board.</strong> | |
| </summary> | |
| <div class="full-radar-frame"> | |
| <img class="chart radar-chart unified-radar-chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v8-readable" alt="Unified grouped 20-task radar comparing Minimal, Neural MLP, 128-episode metadata/raw baselines, Qwen3-Omni, and Cosmos3 with task names, method details, 20-record counts, score counts, and proxy notes"> | |
| <div class="split-radar-links"> | |
| <a href="assets/charts/unified_task_model_radar.svg">View chart source</a> | |
| <a href="data/unified_task_model_radar.json">Inspect chart source</a> | |
| <a href="#result-matrix-table">Read raw-score table</a> | |
| </div> | |
| </div> | |
| </details> | |
| </div> | |
| <div class="suite-task-cards-block" id="tasks"> | |
| <div class="task-suite-subhead"> | |
| <h3>All 20 task cards in the same suite.</h3> | |
| <p>Each card uses its assigned icon and shows the task name, input sources, process, output target, metric, and compact nine-method score panel. Use the filters for scanning; the cards stay tied to the task map, radar axes, and 180-record matrix.</p> | |
| </div> | |
| <article class="task-icon-atlas"> | |
| <div> | |
| <h3>Assigned visual language for the 20 tasks.</h3> | |
| <p>The overall generated atlas keeps the icon family visible, while each task card below uses its own crisp assigned SVG for reliable loading and public mirrors.</p> | |
| </div> | |
| <img src="assets/task-icons/task-icon-atlas.png" alt="Generated 4 by 5 atlas of the 20 Xperience-10M task icons" loading="lazy"> | |
| </article> | |
| <div class="task-toolbar" aria-label="Task filters"> | |
| <button class="filter active" data-filter="all">All tasks</button> | |
| <button class="filter" data-filter="supervised">Supervised</button> | |
| <button class="filter" data-filter="forecast">Forecast</button> | |
| <button class="filter" data-filter="retrieval">Retrieval</button> | |
| <button class="filter" data-filter="diagnostic">Diagnostic</button> | |
| </div> | |
| <div class="task-grid" id="taskGrid" aria-live="polite"></div> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="walkthroughs" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Interactive task walkthrough.</h2> | |
| <p>Each task uses a common research name and a concrete case study, then opens into the input, middle modules, output, modality evidence, metric, and current limitation.</p> | |
| </div> | |
| <div class="task-player" id="taskPlayer"> | |
| <div class="player-stage"> | |
| <div class="player-screen"> | |
| <img id="playerPoster" src="assets/modalities/video.jpg" alt="Representative sample modality for the selected task"> | |
| <div class="player-frame-chip" id="playerFrameChip">Step 1 / 4 · Input</div> | |
| <div class="player-badge"> | |
| <img class="player-task-icon" id="playerTaskIcon" src="assets/task-icons/01_timeline_action.svg" alt="" loading="lazy"> | |
| <strong id="playerBadgeTitle">Action Recognition</strong> | |
| <span id="playerBadgeMeta">Egocentric Action Recognition</span> | |
| </div> | |
| </div> | |
| <p class="player-frame-caption" id="playerFrameCaption">Input: inspect the 20-frame multimodal window before choosing the target.</p> | |
| <div class="modality-strip" id="playerModalities" aria-label="Selected task modality evidence"></div> | |
| <div class="player-controls"> | |
| <button type="button" id="playerPrev">Previous</button> | |
| <button type="button" class="primary-control" id="playerPlay">Play</button> | |
| <button type="button" id="playerNext">Next</button> | |
| <input class="task-scrubber" id="playerScrub" type="range" min="0" max="11" value="0" step="1" aria-label="Scrub through task cards"> | |
| <span class="player-counter" id="playerCounter">01 / 12</span> | |
| </div> | |
| <div class="storyboard-steps" id="playerStoryboard" aria-label="Interactive walkthrough chapters"> | |
| <button type="button" class="story-button active" data-stage="0" aria-pressed="true"><strong>Input</strong><span>What enters the model</span></button> | |
| <button type="button" class="story-button" data-stage="1" aria-pressed="false"><strong>Process</strong><span>How the target is built</span></button> | |
| <button type="button" class="story-button" data-stage="2" aria-pressed="false"><strong>Output</strong><span>What is predicted</span></button> | |
| <button type="button" class="story-button" data-stage="3" aria-pressed="false"><strong>Evaluate</strong><span>Metric and limitation</span></button> | |
| </div> | |
| <div class="player-progress" aria-hidden="true"><span id="playerProgress"></span></div> | |
| </div> | |
| <article class="player-copy" aria-live="polite"> | |
| <div class="player-kicker"> | |
| <span class="tag supervised" id="playerFamily">supervised</span> | |
| <span class="status-pill" id="playerArchitecture">multiclass classifier</span> | |
| </div> | |
| <h3 id="playerTitle">Action Recognition</h3> | |
| <p class="player-case" id="playerCase">In the coffee-making sample, a pouring window maps to the current action label.</p> | |
| <div class="flow-steps"> | |
| <button type="button" class="flow-step active" data-stage="0" aria-pressed="true"><strong>Input</strong><em id="playerInput">20-frame multimodal window</em></button> | |
| <button type="button" class="flow-step" data-stage="1" aria-pressed="false"><strong>Process</strong><em id="playerProcess">window features -> classifier</em></button> | |
| <button type="button" class="flow-step" data-stage="2" aria-pressed="false"><strong>Output</strong><em id="playerOutput">current action class</em></button> | |
| </div> | |
| <ul class="module-list" id="playerModules"></ul> | |
| <p id="playerMetric">Metric: macro-F1. Minimal 0.0500; neural MLP 0.0148.</p> | |
| <p id="playerLimit">Current limitation: single-episode chronological split.</p> | |
| </article> | |
| </div> | |
| <div class="task-selector" id="walkthroughSelector" aria-label="Task walkthrough selector"></div> | |
| </div> | |
| </section> | |
| <section id="protocol" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Evaluation protocol is explicit.</h2> | |
| <p>The protocol is generated from committed metric artifacts and records the exact data unit, split, task targets, leakage controls, and current limitations before score comparison.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"><div><h3>Data unit</h3><p>One 20-frame aligned window from the public sample episode, stride 5 frames, 1,161 windows total, represented by 8,546 synchronized multimodal dimensions.</p></div><a href="data/evaluation_protocol.json">evaluation protocol</a></article> | |
| <article class="artifact"><h3>Split policy</h3><p>Single-episode chronological 70/30 train/test split. This avoids random future-window mixing; cross-episode generalization is measured in the later multi-episode pilot.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/EVALUATION_PROTOCOL.md">protocol document</a></article> | |
| <article class="artifact"><h3>Metric contract</h3><p>All 20 tasks list input, target, primary metric, baseline score, and source artifact path in the unified suite file.</p><a href="data/task_suite_20.json">task contract data</a></article> | |
| <article class="artifact"><h3>Leakage controls</h3><p>Scalers fit on train windows only; future labels, target-side signals, caption/object labels, and contact labels stay on the target side unless explicitly queried.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_evaluation_protocol.py">builder script</a></article> | |
| <article class="artifact"><h3>Audio ablation</h3><p>Audio and no-audio variants are evaluated across the walkthrough-backed task contracts under the same chronological split.</p><a href="data/audio_ablation_summary.json">audio summary</a></article> | |
| <article class="artifact"><h3>Foundation track selection</h3><p>Qwen3-Omni is the first trainable baseline, Cosmos 3 is the world-model track with a camera-pose proxy forward-dynamics contract ready for trainer work, policy models wait for robot-compatible action targets, and Xperience-native pretraining remains a later full-corpus goal.</p><a href="data/foundation_model_plan.json">backbone plan</a></article> | |
| <article class="artifact"><h3>Next evaluation stage</h3><p>This public-sample run covers single-episode task development. The selected multi-episode Qwen3-Omni final diagnostic result is verified and meets the JSON-validity target; Cosmos3-Nano has a verified future-window compatibility package; and Cosmos3-Super has a verified base-weight JSON-task evaluation plus a fine-tuned forward-dynamics LoRA branch. The next stage is action/subtask error analysis, stronger held-out metrics, and policy-target conversion.</p><a href="data/omni_model_comparison.json">result comparison</a></article> | |
| <article class="artifact"><h3>Selected-128 next stressor</h3><p>Before adding episodes, the suite should try `multiscale_20s10_40s20_80s40`, hierarchical action/subtask targets, label-normalized scoring, and compact raw-feature shards for unsupported tasks.</p><a href="data/task_suite_enhancement_128.json">enhancement data</a></article> | |
| <article class="artifact"><h3>Public-safe scale-up gate</h3><p>Future Omni, Cosmos, and policy tracks use the same episode split discipline, training metadata, held-out predictions, metrics, run report, and public-safe package gate.</p><a href="data/foundation_model_plan.json">scale-up status</a></article> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="pipeline" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>From raw episode to research artifacts.</h2> | |
| <p>Every script works from one data contract: aligned multimodal windows, explicit labels, cached feature extraction, and a manifest that makes omitted modalities visible.</p> | |
| </div> | |
| <div class="method-flow-panel" aria-label="From data to score method flow"> | |
| <div class="method-flow-head"> | |
| <div> | |
| <small>from data to score</small> | |
| <strong>One pipeline, five verifiable checkpoints.</strong> | |
| </div> | |
| <p>This is the shortest way to understand the method section: start from official synchronized files, build aligned windows, create task targets, run method heads or model probes, then publish source-linked metrics.</p> | |
| </div> | |
| <div class="method-flow-steps"> | |
| <article><strong>Official streams</strong><p>RGB/fisheye/stereo video, audio, HDF5 annotations, pose, depth, calibration, IMU, mocap, and Rerun visualization sources.</p></article> | |
| <article><strong>Aligned windows</strong><p>20-frame windows with a 5-frame stride keep the input unit consistent across task heads and selected-128 exports.</p></article> | |
| <article><strong>Task targets</strong><p>Each window maps to one of the 20 contracts: classification, forecasting, retrieval, reconstruction, synchronization, or regression.</p></article> | |
| <article><strong>Method rows</strong><p>Minimal, Neural MLP, selected-128 metadata/raw baselines, Qwen3-Omni v6, Cosmos3-Super, and Cosmos3-Nano produce the public method rows.</p></article> | |
| <article><strong>Verified outputs</strong><p>Metrics, predictions, matrices, radar data, validators, and mirror parity checks are published with direct/proxy status preserved.</p></article> | |
| </div> | |
| </div> | |
| <img class="pipeline-image" src="assets/pipeline_diagram.png?v=xperience10m-nn" alt="Verified Xperience-10M multimodal pipeline diagram"> | |
| <div class="figure-brief"> | |
| <article class="figure-brief-card"> | |
| <h3>Qwen3-Omni LoRA training flow</h3> | |
| <p>Raw valid episodes move through split validation, parallel export, video/audio/text formatting, sensor-bridge features, LoRA training, and sealed held-out evaluation.</p> | |
| </article> | |
| <article class="figure-brief-card"> | |
| <h3>What the figure represents</h3> | |
| <p>It documents the selected 128-episode final diagnostic result and the action/subtask improvement path for stronger held-out metrics.</p> | |
| </article> | |
| </div> | |
| <img class="pipeline-image lora-pipeline-image" src="assets/qwen3_omni_lora_pipeline.png?v=qwen3-lora-v1" alt="Detailed Qwen3-Omni LoRA training pipeline from raw Xperience-10M episodes to adapter outputs, predictions, metrics, and reports"> | |
| <div class="callout-row"> | |
| <div class="callout"> | |
| <h3>What this project enables</h3> | |
| <p>It demonstrates the full development loop: reading Xperience-10M sample data, aligning modalities, converting them into model-ready windows, defining meaningful tasks, producing metrics, and packaging artifacts for continued research.</p> | |
| </div> | |
| <div class="callout"> | |
| <h3>What still needs more data</h3> | |
| <p>General embodied-intelligence model quality requires many episodes and held-out episode splits; the public sample is the development harness for that next stage.</p> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="features" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Model inputs are traceable.</h2> | |
| <p>Each task reads an aligned window, not a black-box feature blob. The cards below show the path from public-sample streams to window rows and feature groups, then the chart shows the size of each input block.</p> | |
| </div> | |
| <div class="source-map-grid" aria-label="Model input provenance path"> | |
| <article class="source-map-card"> | |
| <small>1 / raw source</small> | |
| <h3>Start from official streams</h3> | |
| <p>Six synchronized MP4 camera streams, embedded audio, HDF5 annotations, depth, pose/SLAM, mocap, IMU, calibration, and language-derived fields remain tied to the public sample episode.</p> | |
| <a href="#raw-sample">Open sample browser</a> | |
| </article> | |
| <article class="source-map-card"> | |
| <small>2 / aligned window</small> | |
| <h3>Slice into the same unit</h3> | |
| <p>The task suite builds 20-frame windows with a 5-frame stride. Each row records episode id, start frame, end frame, center frame, timestamps, and split assignment.</p> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/windows.csv">Open window manifest</a> | |
| </article> | |
| <article class="source-map-card"> | |
| <small>3 / feature contract</small> | |
| <h3>Name every input block</h3> | |
| <p>The feature contract groups the 8,546 dimensions by modality and derived signal. Omitted or target-side fields stay visible so leakage checks can be audited.</p> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/feature_manifest.json">Open feature contract</a> | |
| </article> | |
| </div> | |
| <div class="source-map-panel"> | |
| <div class="source-map-legend" aria-label="Feature block chart legend"> | |
| <article><small>source modality</small><p>Where the signal comes from: video, audio, depth, pose/SLAM, mocap, IMU, calibration, or language-derived annotation fields.</p></article> | |
| <article><small>feature block</small><p>The named column group used by Minimal baselines, Neural MLP heads, and public-sample task cards.</p></article> | |
| <article><small>chart width</small><p>Bar length means number of input dimensions. It is not a model score and it is not a task ranking.</p></article> | |
| </div> | |
| <figure class="source-map-chart-frame"> | |
| <img class="chart" src="assets/charts/feature_blocks.svg" alt="Feature block chart showing input dimension counts for hand joints, body joints, pose, IMU, depth, six video streams, audio, language text, SLAM point cloud, and calibration"> | |
| </figure> | |
| <div class="source-map-caption"> | |
| <p><strong>Read this as provenance.</strong> A task card may say “20-frame multimodal window”; this section shows which source streams and derived feature groups make up that window before any task-specific label is attached.</p> | |
| <a href="#tasks">Back to task cards</a> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="architectures" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>The baseline task heads share four head families.</h2> | |
| <p>The diagram separates the shared episode-window representation from the task-specific heads, so the task contracts stay readable before scaling to larger models.</p> | |
| </div> | |
| <img class="architecture-image" src="assets/task_architectures.png?v=xperience10m-nn" alt="Verified minimal and neural architecture diagram for Xperience-10M task heads"> | |
| </div> | |
| </section> | |
| <section id="takeaways" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Results by evidence line.</h2> | |
| <p>Read results in this order: choose the line, open the matching radar, inspect the matrix row, then check proxy flags before interpreting totals.</p> | |
| </div> | |
| <nav class="result-jump-row" aria-label="Results quick links"> | |
| <a href="#result-lines">Evidence lines</a> | |
| <a href="#result-matrix-table">180-result table</a> | |
| <a href="#models">Baseline snapshots</a> | |
| <a href="#neural">Neural heads</a> | |
| <a href="#diagnostics">Diagnostic charts</a> | |
| </nav> | |
| <div class="result-citation-banner" role="note" aria-label="Result citation rule"> | |
| <span>i</span> | |
| <div> | |
| <strong>Cite raw metric values; use radar values only as visual summaries.</strong> | |
| <p>The raw value keeps the task metric and scale, such as macro-F1, MRR, MAE, or R2. The normalized radar value is a 0-1 plotting transform for comparing shapes across tasks.</p> | |
| </div> | |
| </div> | |
| <div class="result-rules-panel" aria-label="Result reading rules"> | |
| <article> | |
| <small>cite this</small> | |
| <strong>Raw metric value</strong> | |
| <p>The score printed in each table cell is the value emitted by the runner or verified package. Use this value in text, tables, and comparisons.</p> | |
| </article> | |
| <article> | |
| <small>visual only</small> | |
| <strong>Normalized radar value</strong> | |
| <p>The radar converts mixed metrics to a 0-1 plotting scale. It helps pattern recognition, but it is not a replacement for the raw metric.</p> | |
| </article> | |
| <article> | |
| <small>status first</small> | |
| <strong>Direct vs compact-proxy</strong> | |
| <p>Direct scores use the task target. Compact-proxy scores are bounded substitutes where a raw public target is unavailable, and stay marked before comparison.</p> | |
| </article> | |
| </div> | |
| <table class="line-table citation-ledger" aria-label="What result values can be cited"> | |
| <caption>Result citation ledger</caption> | |
| <thead> | |
| <tr> | |
| <th>Use case</th> | |
| <th>Use this value</th> | |
| <th>Why</th> | |
| <th>Where it appears</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td>Write a paper or README number</td> | |
| <td>Raw metric value</td> | |
| <td>This is emitted by the runner or verified result package and keeps the original metric scale.</td> | |
| <td>180-result table cells, result JSON, method summaries.</td> | |
| </tr> | |
| <tr> | |
| <td>Compare shapes across many tasks</td> | |
| <td>Normalized radar value</td> | |
| <td>This is a 0-1 plotting transform for visual comparison only; cite the raw metric beside it.</td> | |
| <td>Unified radar and split radar charts.</td> | |
| </tr> | |
| <tr> | |
| <td>Interpret a low-availability target</td> | |
| <td>Compact-proxy score plus its audit note</td> | |
| <td>The task target is represented by a bounded substitute, so the proxy label and reason travel with the value.</td> | |
| <td>Proxy audit, matrix badges, selected-128 baseline rows.</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <div class="result-reading-order" id="result-lines" aria-label="Result evidence lines"> | |
| <article class="result-reading-step"> | |
| <span>01</span> | |
| <strong>Choose the line</strong> | |
| <p>Use 1 episode for the task lab. Use 128 episodes for the selected comparison surface.</p> | |
| </article> | |
| <article class="result-reading-step"> | |
| <span>02</span> | |
| <strong>Open the radar</strong> | |
| <p>Single-episode radar shows Minimal vs Neural MLP. The 128-episode radar shows metadata/raw baselines, Qwen3-Omni v6, Cosmos3-Super, and Cosmos3-Nano.</p> | |
| </article> | |
| <article class="result-reading-step"> | |
| <span>03</span> | |
| <strong>Inspect the matrix</strong> | |
| <p>Each score keeps method, task, metric key, source artifact, and status.</p> | |
| </article> | |
| <article class="result-reading-step"> | |
| <span>04</span> | |
| <strong>Check proxy cells</strong> | |
| <p>Six selected-128 scores are compact proxies and stay marked in the audit.</p> | |
| </article> | |
| </div> | |
| <div class="suite-lines" aria-label="Results split by evidence line"> | |
| <article class="suite-line-card"> | |
| <small>1 episode results</small> | |
| <h3>Task-lab evidence</h3> | |
| <p>Minimal and Neural MLP heads are both scored on all 20 public-sample task contracts. All 40 scores are direct task-target metrics.</p> | |
| <div class="line-readout"> | |
| <div><span>best read as</span><p>A reproducible public task suite and baseline behavior check.</p></div> | |
| </div> | |
| <div class="suite-line-facts"> | |
| <span><strong>2</strong>methods</span> | |
| <span><strong>20</strong>task axes</span> | |
| <span><strong>40/40</strong>scores</span> | |
| </div> | |
| <a href="assets/charts/single_episode_task_model_radar.svg">Open 1-episode radar</a> | |
| </article> | |
| <article class="suite-line-card"> | |
| <small>128 episode results</small> | |
| <h3>Scale-up evidence</h3> | |
| <p>Metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window use the aligned 128-episode surface. It has 134 direct scores plus 6 compact-proxy scores.</p> | |
| <div class="line-readout"> | |
| <div><span>best read as</span><p>A same-split comparison table with explicit source and proxy status.</p></div> | |
| </div> | |
| <div class="suite-line-facts"> | |
| <span><strong>7</strong>methods</span> | |
| <span><strong>20</strong>task axes</span> | |
| <span><strong>140/140</strong>scores</span> | |
| </div> | |
| <a href="assets/charts/episode128_task_model_radar.svg">Open 128-episode radar</a> | |
| </article> | |
| </div> | |
| <table class="line-table" aria-label="Direct and proxy score ledger by evidence line"> | |
| <thead> | |
| <tr> | |
| <th>Line</th> | |
| <th>Methods</th> | |
| <th>Tasks</th> | |
| <th>Scored records</th> | |
| <th>Direct scores</th> | |
| <th>Proxy scores</th> | |
| <th>Machine-readable source</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td>1 sample episode</td> | |
| <td>2</td> | |
| <td>20</td> | |
| <td>40/40</td> | |
| <td>40</td> | |
| <td>0</td> | |
| <td><a href="data/single_episode_task_model_radar.json">single-episode radar data</a></td> | |
| </tr> | |
| <tr> | |
| <td>128 selected episodes</td> | |
| <td>7</td> | |
| <td>20</td> | |
| <td>140/140</td> | |
| <td>134</td> | |
| <td>6 compact-proxy scores, each source-linked and reasoned.</td> | |
| <td><a href="data/episode128_task_model_radar.json">128-episode radar data</a></td> | |
| </tr> | |
| <tr> | |
| <td>Total public matrix</td> | |
| <td>9</td> | |
| <td>20</td> | |
| <td>180/180</td> | |
| <td>174</td> | |
| <td>6</td> | |
| <td><a href="data/two_evidence_line_result_summary.json">two-line result summary data</a></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <table class="line-table" aria-label="Method ownership inside each evidence line"> | |
| <thead> | |
| <tr> | |
| <th>Line</th> | |
| <th>Block</th> | |
| <th>Methods</th> | |
| <th>Records</th> | |
| <th>Evidence type</th> | |
| <th>Primary artifact</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td>1 sample episode</td> | |
| <td>Task-head baselines</td> | |
| <td>Minimal; Neural MLP</td> | |
| <td>40 direct</td> | |
| <td>Direct target metrics on the public sample windows.</td> | |
| <td><a href="data/single_episode_task_model_radar.json">single-episode radar data</a></td> | |
| </tr> | |
| <tr> | |
| <td>128 selected episodes</td> | |
| <td>Aligned baseline heads</td> | |
| <td>Metadata simple/NN; raw-feature simple/NN</td> | |
| <td>74 direct + 6 compact-proxy</td> | |
| <td>Processed-target metrics where available; proxy cells remain source-linked.</td> | |
| <td><a href="data/task_method_20_gap_audit.json">score/proxy audit</a></td> | |
| </tr> | |
| <tr> | |
| <td>128 selected episodes</td> | |
| <td>Qwen3-Omni series</td> | |
| <td>Qwen3-Omni v6 LoRA</td> | |
| <td>20 direct</td> | |
| <td>Verified selected-128 LoRA and task-specific probe artifacts.</td> | |
| <td><a href="data/omni_model_comparison.json">model comparison data</a></td> | |
| </tr> | |
| <tr> | |
| <td>128 selected episodes</td> | |
| <td>Cosmos3 series</td> | |
| <td>Cosmos3-Super Reasoner; Cosmos3-Nano Future Window</td> | |
| <td>40 direct</td> | |
| <td>Verified reasoner and future-window public-safe artifacts; forward-dynamics LoRA is a separate adapter artifact outside the 20-task method rows.</td> | |
| <td><a href="data/omni_model_comparison.json">model comparison data</a></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <div class="result-matrix-panel" id="result-matrix-table" aria-labelledby="result-matrix-title"> | |
| <div class="result-matrix-head"> | |
| <div> | |
| <span>180-result table</span> | |
| <h3 id="result-matrix-title">All methods x all 20 tasks, in one source-linked table.</h3> | |
| <p>Each cell shows the raw metric value to cite, the normalized radar value, the metric key, and a direct/proxy badge. The table is generated from the same structured matrix data used by the radar, so values stay aligned across GitHub, the website, and Hugging Face mirrors.</p> | |
| </div> | |
| <div class="result-matrix-actions" aria-label="Open result matrix source files"> | |
| <a href="data/task_method_20_result_matrix.json">Matrix data</a> | |
| <a href="data/task_method_20_gap_audit.json">Proxy audit</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TASK_METHOD_20_RESULT_MATRIX.md">Full table note</a> | |
| </div> | |
| </div> | |
| <div class="result-matrix-stats" id="resultMatrixStats" aria-label="180-result matrix summary"> | |
| <div class="result-matrix-stat"><strong>180</strong><span>method-task records</span></div> | |
| <div class="result-matrix-stat"><strong>9</strong><span>method rows</span></div> | |
| <div class="result-matrix-stat"><strong>20</strong><span>task columns</span></div> | |
| <div class="result-matrix-stat"><strong>174</strong><span>direct scores</span></div> | |
| <div class="result-matrix-stat"><strong>6</strong><span>compact-proxy scores</span></div> | |
| </div> | |
| <div class="result-matrix-legend" aria-label="Result table legend"> | |
| <span class="matrix-legend-pill" style="--pill-color:#ccffa0"><span class="term-with-help"><span>direct task score</span><span class="term-help" tabindex="0" aria-label="Direct score: A metric computed against the task target directly." title="A metric computed against the task target directly.">i<span class="term-tooltip" role="tooltip">A metric computed against the task target directly. This is the preferred score type in the 20-task matrix.</span></span></span></span> | |
| <span class="matrix-legend-pill" style="--pill-color:#f472b6"><span class="term-with-help"><span>compact proxy score</span><span class="term-help" tabindex="0" aria-label="Compact-proxy score: A bounded proxy metric when a direct raw target is not publicly available." title="A bounded proxy metric when a direct raw target is not publicly available.">i<span class="term-tooltip" role="tooltip">A bounded proxy metric when a direct raw target is not publicly available. It stays explicit so proxy targets are not mistaken for direct raw-target reads.</span></span></span></span> | |
| <span class="matrix-legend-pill" style="--pill-color:#9bb8ff"><span class="term-with-help"><span>raw value is citeable</span><span class="term-help" tabindex="0" aria-label="Raw metric value: The original metric value emitted by the runner or verified package." title="The original metric value emitted by the runner or verified package.">i<span class="term-tooltip" role="tooltip">The original metric value emitted by the runner or verified package. This is the value to cite.</span></span></span></span> | |
| <span class="matrix-legend-pill" style="--pill-color:#67e8d1"><span class="term-with-help"><span>normalized value is radar-only</span><span class="term-help" tabindex="0" aria-label="Normalized radar value: A 0-1 plotting value used only to draw comparable radar polygons." title="A 0-1 plotting value used only to draw comparable radar polygons.">i<span class="term-tooltip" role="tooltip">A 0-1 plotting value used only to draw comparable radar polygons across metrics with different scales.</span></span></span></span> | |
| </div> | |
| <details class="result-matrix-details"> | |
| <summary> | |
| <span>open full 180-score audit table</span> | |
| <strong>Use this after the citation ledger. It contains the method summary, every method-task cell, direct/proxy badges, raw metric values, normalized radar values, and source-aligned evidence notes.</strong> | |
| </summary> | |
| <div class="result-matrix-body"> | |
| <div class="method-summary-scroll" aria-label="Method summary table container"> | |
| <table class="method-summary-table" id="resultMethodSummary"> | |
| <thead> | |
| <tr> | |
| <th><span class="term-with-help"><span>Method</span><span class="term-help" tabindex="0" aria-label="Method row: One named method family in the matrix." title="One named method family in the matrix.">i<span class="term-tooltip" role="tooltip">One named method family in the matrix, such as Minimal, 128ep Raw NN, Qwen3-Omni v6, or Cosmos3-Super.</span></span></span></th> | |
| <th><span class="term-with-help"><span>Line</span><span class="term-help" tabindex="0" aria-label="Evidence line: A reading lane for a group of results." title="A reading lane for a group of results.">i<span class="term-tooltip" role="tooltip">A reading lane for a group of results: Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison.</span></span></span></th> | |
| <th><span class="term-with-help"><span>Records</span><span class="term-help" tabindex="0" aria-label="Task-method record: One method evaluated on one task." title="One method evaluated on one task.">i<span class="term-tooltip" role="tooltip">One method evaluated on one task. 9 methods x 20 tasks gives 180 public result records.</span></span></span></th> | |
| <th><span class="term-with-help"><span>Direct</span><span class="term-help" tabindex="0" aria-label="Direct score: A metric computed against the task target directly." title="A metric computed against the task target directly.">i<span class="term-tooltip" role="tooltip">A metric computed against the task target directly. This is the preferred score type in the 20-task matrix.</span></span></span></th> | |
| <th><span class="term-with-help"><span>Proxy</span><span class="term-help" tabindex="0" aria-label="Compact-proxy score: A bounded proxy metric when a direct raw target is not publicly available." title="A bounded proxy metric when a direct raw target is not publicly available.">i<span class="term-tooltip" role="tooltip">A bounded proxy metric when a direct raw target is not publicly available. It stays explicit so proxy targets are not mistaken for direct raw-target reads.</span></span></span></th> | |
| <th>Scope</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr><td colspan="6">Loading result summary...</td></tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| <div class="result-matrix-scroll" aria-label="Scrollable 180-result score table"> | |
| <table class="result-score-table" id="resultScoreTable"> | |
| <caption class="sr-only">Raw and normalized scores for 9 methods across 20 Xperience-10M tasks.</caption> | |
| <thead> | |
| <tr><th>Method</th><th>Loading tasks...</th></tr> | |
| </thead> | |
| <tbody> | |
| <tr><td colspan="2">Loading 180-result matrix...</td></tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| <p class="result-matrix-foot">Best-practice reading rule: compare methods within the same evidence line first, then use the proxy badges before interpreting cross-method totals. Six compact-proxy cells are intentionally visible rather than blended into direct raw-target scores.</p> | |
| </div> | |
| </details> | |
| </div> | |
| <div class="result-subsection" id="models"> | |
| <div class="result-subsection-head"> | |
| <h3>Small baselines, no hidden machinery.</h3> | |
| <p>Motion-only and all-feature classifiers use lightweight heads so the comparison stays readable on a laptop and easy to inspect. They now sit beside the result matrix instead of opening a separate results page.</p> | |
| </div> | |
| <div class="models"> | |
| <article class="model"><h3>Motion-only action</h3><span class="score">0.9688</span><span class="meta">macro-F1, 18 classes</span></article> | |
| <article class="model"><h3>Current all-feature action</h3><span class="score">0.9829</span><span class="meta">macro-F1, 8,546 dimensions</span></article> | |
| <article class="model"><h3>Motion-only subtask</h3><span class="score">0.9528</span><span class="meta">macro-F1, 14 classes</span></article> | |
| <article class="model"><h3>Current all-feature subtask</h3><span class="score">0.9173</span><span class="meta">macro-F1, chronological caveats</span></article> | |
| </div> | |
| <img class="chart" src="assets/charts/model_macro_f1.svg" alt="Macro-F1 comparison chart"> | |
| </div> | |
| <div class="result-subsection" id="neural"> | |
| <div class="result-subsection-head"> | |
| <h3>Neural MLP heads, same task contracts.</h3> | |
| <p>The neural baseline keeps the same windows, splits, and leakage filters, then swaps in small PyTorch MLP heads. Read it as a nonlinear-head check before heavier Qwen3/Cosmos model branches.</p> | |
| </div> | |
| <div class="models"> | |
| <article class="model"><h3>Neural hand forecast</h3><span class="score">0.1079</span><span class="meta">MPJPE, down from 0.8647 minimal</span></article> | |
| <article class="model"><h3>Neural temporal order</h3><span class="score">0.8520</span><span class="meta">F1, adjacent-window diagnostic</span></article> | |
| <article class="model"><h3>Neural misalignment</h3><span class="score">0.7153</span><span class="meta">F1, shifted motion/visual/audio pairs</span></article> | |
| <article class="model"><h3>Neural cross-modal retrieval</h3><span class="score">0.1300</span><span class="meta">MRR; ridge remains stronger here</span></article> | |
| </div> | |
| <div class="chart-grid"> | |
| <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP episode task score chart"> | |
| <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural MLP episode task score chart"> | |
| </div> | |
| </div> | |
| <div class="result-subsection" id="diagnostics"> | |
| <div class="result-subsection-head"> | |
| <h3>Diagnostics separate memorization from signal.</h3> | |
| <p>These charts keep the main lesson visible: within-episode labels can be easy under some splits, while retrieval, grounding, forecasting, and alignment remain the useful probes.</p> | |
| </div> | |
| <div class="chart-grid"> | |
| <img class="chart" src="assets/charts/episode_task_scores.svg" alt="Episode task suite score chart"> | |
| <img class="chart" src="assets/charts/cross_modal_retrieval.svg" alt="Cross modal retrieval chart"> | |
| <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP task score chart"> | |
| <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural score chart"> | |
| <img class="chart" src="assets/charts/audio_ablation_delta.svg" alt="Measured audio delta chart across walkthrough-backed task contracts"> | |
| </div> | |
| <p class="section-note"><a href="single_episode_explorer.html">Open the single-episode explorer</a> to inspect window-level labels, predictions, modality statistics, object labels, and diagnostic scores. The <a href="data/audio_ablation_summary.json">audio ablation summary</a> records the task-by-task audio contribution.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"> | |
| <div> | |
| <h3>One episode becomes a benchmark contract</h3> | |
| <p>The public sample is converted into 5,821 frames, 1,161 aligned 20-frame windows, and an 8,546-dimensional representation for repeatable task evaluation.</p> | |
| </div> | |
| <a href="data/research_takeaways.json">research takeaways</a> | |
| </article> | |
| <article class="artifact"> | |
| <h3>Chronological split exposes class shift</h3> | |
| <p>All-feature action reaches 0.9829 macro-F1 on its local split, while the chronological action head in the core task suite is 0.0500 macro-F1 with four unseen later action labels.</p> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/RESEARCH_TAKEAWAYS.md">takeaways</a> | |
| </article> | |
| <article class="artifact"> | |
| <h3>Neural heads help dynamics</h3> | |
| <p>Hand MPJPE improves from 0.8647 to 0.1079; temporal-order F1 rises from 0.5400 to 0.8520; misalignment F1 rises from 0.5052 to 0.7153.</p> | |
| <a href="data/research_takeaways.json">metrics</a> | |
| </article> | |
| <article class="artifact"> | |
| <h3>Retrieval and reconstruction remain open</h3> | |
| <p>Ridge/cosine retrieval remains stronger than the neural projection here, and cross-modal feature reconstruction still has negative R2.</p> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/cross_modal_retrieval/metrics.json">retrieval metrics</a> | |
| </article> | |
| <article class="artifact"> | |
| <h3>Scale means held-out episodes</h3> | |
| <p>The next credible model-quality unit is a held-out multi-episode pilot across different sessions, not more adjacent windows from one sample.</p> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">scale-up status</a> | |
| </article> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="directions" data-project-tab="directions" role="tabpanel" aria-labelledby="tab-directions" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Four research directions over the same 20 tasks.</h2> | |
| <p>This is the 4-direction layer of the public structure. Each direction groups task evidence by research question; it does not create a separate task set. Each task is mapped as direct, proxy, or diagnostic evidence using the same minimal and Neural MLP baseline contracts.</p> | |
| </div> | |
| <div class="task-axis-panel" aria-label="Relationship between tasks, research directions, training pipelines, and unified embodied model target"> | |
| <div class="task-axis-head"> | |
| <div> | |
| <small>relationship map</small> | |
| <strong>20 tasks / 4 directions / 3 pipelines / 1 unified model target.</strong> | |
| </div> | |
| <p>The structure is fixed: the 20 tasks are scored contracts, the four directions group what those scores study, the three pipelines define training recipes, and the unified embodied model is the long-term integration target.</p> | |
| </div> | |
| <div class="task-axis-summary" aria-label="Public structure key"> | |
| <article class="task-axis-summary-card"> | |
| <span class="task-axis-count">20</span> | |
| <div> | |
| <small>scored task layer</small> | |
| <strong>One unified task suite.</strong> | |
| <p>Every metric, radar axis, and method row uses these same 20 task contracts.</p> | |
| </div> | |
| </article> | |
| <article class="task-axis-summary-card"> | |
| <span class="task-axis-count">4</span> | |
| <div> | |
| <small>4-direction layer</small> | |
| <strong>Four research views over the same tasks.</strong> | |
| <p>Human motion, 3D/4D reconstruction, egocentric interaction, and world modeling are groupings over the same tasks.</p> | |
| </div> | |
| </article> | |
| <article class="task-axis-summary-card"> | |
| <span class="task-axis-count">3</span> | |
| <div> | |
| <small>3-pipeline layer</small> | |
| <strong>Three ways to train larger models.</strong> | |
| <p>Spatial intelligence, human-video world models, and vision-language-action models reuse the same files with different input-output recipes.</p> | |
| </div> | |
| </article> | |
| <article class="task-axis-summary-card"> | |
| <span class="task-axis-count">1</span> | |
| <div> | |
| <small>unified target</small> | |
| <strong>One embodied foundation model goal.</strong> | |
| <p>The pipeline outputs converge toward perception, 3D memory, language, action, and planning in one model family.</p> | |
| </div> | |
| </article> | |
| </div> | |
| <div class="task-axis-reader-rule" aria-label="Public layer labels"> | |
| <article> | |
| <span>score question</span> | |
| <strong>If it has a metric, it belongs to the 20-task layer.</strong> | |
| <p>Task cards, radars, and the 180-record table all use the same numbered task IDs.</p> | |
| </article> | |
| <article> | |
| <span>research question</span> | |
| <strong>If it explains what the evidence studies, it belongs to the 4-direction layer.</strong> | |
| <p>Directions A-D group the same 20 tasks. They are not extra tasks or a second tier.</p> | |
| </article> | |
| <article> | |
| <span>training question</span> | |
| <strong>If it describes model inputs and targets, it belongs to the 3-pipeline layer.</strong> | |
| <p>Spatial, world-model, and VLA pipelines are recipes for future scale-up and task-grounded model training.</p> | |
| </article> | |
| <article> | |
| <span>synthesis question</span> | |
| <strong>If it combines pipeline abilities, it belongs to the unified-model target.</strong> | |
| <p>This is the expandable integration goal, not an extra scored task axis in the 180-result matrix.</p> | |
| </article> | |
| </div> | |
| <figure class="task-axis-visual"> | |
| <img src="assets/charts/task_direction_pipeline_relationship.png?v=relationship-map-v4-unified" alt="Labeled Ropedia Xperience-10M Task Suite relationship map showing 20 task contracts flowing into four named research directions, three foundation-model training pipelines, and one unified embodied foundation model target, with each layer open to expand."> | |
| <figcaption aria-label="Relationship map legend"> | |
| <span><strong>20 tasks</strong>Score axes used by every method row.</span> | |
| <span><strong>4 directions</strong>Research questions for reading those scores.</span> | |
| <span><strong>3 pipelines</strong>Training recipes for larger foundation models.</span> | |
| <span><strong>1 unified model</strong>Future integration target, not a new score axis.</span> | |
| </figcaption> | |
| </figure> | |
| <div class="task-axis-key" aria-label="Explicit relationship key for the map"> | |
| <article class="task-axis-key-card"> | |
| <span>20 task contracts</span> | |
| <strong>Every score axis is one of these tasks.</strong> | |
| <p>The generated image uses one node per task; this list gives the exact public task names.</p> | |
| <ol class="task-axis-task-list"> | |
| <li><b>01</b>Action Recognition</li> | |
| <li><b>02</b>Procedure Step Recognition</li> | |
| <li><b>03</b>Action Boundary Detection</li> | |
| <li><b>04</b>Next-Action Prediction</li> | |
| <li><b>05</b>Hand Trajectory Forecasting</li> | |
| <li><b>06</b>Contact State Prediction</li> | |
| <li><b>07</b>Object Relevance Prediction</li> | |
| <li><b>08</b>Language Grounding</li> | |
| <li><b>09</b>Cross-Modal Retrieval</li> | |
| <li><b>10</b>Cross-Modal Reconstruction</li> | |
| <li><b>11</b>Temporal Order Verification</li> | |
| <li><b>12</b>Multimodal Synchronization Detection</li> | |
| <li><b>13</b>Long-Horizon Next-Action Forecasting</li> | |
| <li><b>14</b>Long-Horizon Next-Subtask Forecasting</li> | |
| <li><b>15</b>Interaction Text Prediction</li> | |
| <li><b>16</b>Action-Object Relation Prediction</li> | |
| <li><b>17</b>Future Object-Set Forecasting</li> | |
| <li><b>18</b>IMU-to-Hand Pose Reconstruction</li> | |
| <li><b>19</b>Camera-View Synchronization Retrieval</li> | |
| <li><b>20</b>Time-to-Next-Transition Regression</li> | |
| </ol> | |
| </article> | |
| <article class="task-axis-key-card"> | |
| <span>4 research directions</span> | |
| <strong>The same tasks are grouped by research question.</strong> | |
| <p>Tasks can support more than one direction. This layer answers what the scores study; it is not a separate benchmark or task tier.</p> | |
| <ol class="task-axis-mini-list"> | |
| <li><b>A</b>Human Modeling & Motion Understanding</li> | |
| <li><b>B</b>3D/4D Reconstruction & Neural Rendering</li> | |
| <li><b>C</b>Egocentric Vision & Interaction</li> | |
| <li><b>D</b>Scene Reconstruction & World Modeling</li> | |
| </ol> | |
| </article> | |
| <article class="task-axis-key-card"> | |
| <span>3 foundation pipelines</span> | |
| <strong>The training tracks reuse the task and modality contracts.</strong> | |
| <p>These are scale-up recipes for model training. They answer how the same files can be turned into larger model inputs and targets.</p> | |
| <ol class="task-axis-mini-list"> | |
| <li><b>1</b>Spatial intelligence models</li> | |
| <li><b>2</b>Human-video world models</li> | |
| <li><b>3</b>Vision-language-action models</li> | |
| </ol> | |
| </article> | |
| <article class="task-axis-key-card"> | |
| <span>1 unified target</span> | |
| <strong>The pipelines converge into one embodied model goal.</strong> | |
| <p>This target integrates perception, 3D memory, language, action, and planning. It is a research direction for scale-up, not a tenth method or twenty-first task.</p> | |
| <ol class="task-axis-mini-list"> | |
| <li><b>P</b>Perception and multimodal input</li> | |
| <li><b>M</b>3D memory and scene state</li> | |
| <li><b>L</b>Language-grounded reasoning</li> | |
| <li><b>A</b>Action and planning</li> | |
| </ol> | |
| </article> | |
| </div> | |
| <div class="task-axis-grid"> | |
| <article class="task-axis-card"> | |
| <span>scored contracts</span> | |
| <strong>20 tasks</strong> | |
| <p>Action, subtask, object, language, motion, synchronization, retrieval, and forecasting targets evaluated by each public method row.</p> | |
| </article> | |
| <article class="task-axis-card"> | |
| <span>research grouping</span> | |
| <strong>4 directions</strong> | |
| <p>Human Modeling & Motion Understanding; 3D/4D Reconstruction & Neural Rendering; Egocentric Vision & Interaction; Scene Reconstruction & World Modeling.</p> | |
| </article> | |
| <article class="task-axis-card"> | |
| <span>training tracks</span> | |
| <strong>3 foundation pipelines</strong> | |
| <p>Spatial intelligence models; Human-video world models; Vision-language-action models.</p> | |
| </article> | |
| <article class="task-axis-card"> | |
| <span>synthesis target</span> | |
| <strong>1 unified embodied model</strong> | |
| <p>A long-term model target where perception, spatial memory, language, action, and planning are trained against the same evidence structure.</p> | |
| </article> | |
| </div> | |
| <div class="task-axis-flow" aria-label="Tasks directions and pipelines reading formula"> | |
| <strong>20 tasks are the score axes.</strong> | |
| <span>-></span> | |
| <strong>4 directions organize what the scores study.</strong> | |
| <span>-></span> | |
| <strong>3 pipelines describe how to train larger models.</strong> | |
| <span>-></span> | |
| <strong>1 unified model is the integration target.</strong> | |
| </div> | |
| </div> | |
| <nav class="direction-jump-row" aria-label="Direction evidence quick links"> | |
| <a href="#direction-coverage">Direction coverage</a> | |
| <a href="#direction-baselines">Baselines</a> | |
| <a href="#extensions">Unified provenance</a> | |
| <a href="#suite">20-task suite</a> | |
| </nav> | |
| <div class="direction-grid" id="direction-coverage"> | |
| <article class="direction-card"> | |
| <span class="status-pill">partially implemented</span> | |
| <h3>A. Human Modeling & Motion Understanding</h3> | |
| <p>Direct evidence comes from hand trajectory forecasting and contact prediction; action and object relevance are supporting proxies.</p> | |
| <div class="direction-counts"><span><strong>2</strong>direct</span><span><strong>2</strong>proxy</span><span><strong>0</strong>diagnostic</span></div> | |
| </article> | |
| <article class="direction-card"> | |
| <span class="status-pill">prerequisite evidence</span> | |
| <h3>B. 3D/4D Reconstruction & Neural Rendering</h3> | |
| <p>Cross-modal retrieval, modality reconstruction, and misalignment detection check reconstruction prerequisites, not full geometry.</p> | |
| <div class="direction-counts"><span><strong>0</strong>direct</span><span><strong>2</strong>proxy</span><span><strong>1</strong>diagnostic</span></div> | |
| </article> | |
| <article class="direction-card"> | |
| <span class="status-pill">strongest implemented</span> | |
| <h3>C. Egocentric Vision & Interaction</h3> | |
| <p>Action, subtask, transition, next-action, object, caption, order, and alignment tasks directly stress egocentric understanding.</p> | |
| <div class="direction-counts"><span><strong>6</strong>direct</span><span><strong>2</strong>proxy</span><span><strong>3</strong>diagnostic</span></div> | |
| </article> | |
| <article class="direction-card"> | |
| <span class="status-pill">world-model prerequisites</span> | |
| <h3>D. Scene Reconstruction & World Modeling</h3> | |
| <p>Current probes cover task state, object relevance, retrieval, reconstruction, temporal order, and alignment but no persistent map yet.</p> | |
| <div class="direction-counts"><span><strong>0</strong>direct</span><span><strong>6</strong>proxy</span><span><strong>3</strong>diagnostic</span></div> | |
| </article> | |
| </div> | |
| <img class="chart" src="assets/charts/research_direction_coverage.svg" alt="Coverage of the original Xperience-10M tasks across four research directions"> | |
| <div class="baseline-strip" id="direction-baselines"> | |
| <div class="callout"> | |
| <h3>Baseline 1: minimal heads</h3> | |
| <p>Softmax, logistic, ridge, and retrieval heads keep every input/output contract readable. They are the first sanity check for whether a task is well-posed.</p> | |
| </div> | |
| <div class="callout"> | |
| <h3>Baseline 2: neural MLP heads</h3> | |
| <p>Small PyTorch MLP classifiers/regressors reuse the same features and splits. They test nonlinear gains before heavier Omni fine-tuning.</p> | |
| </div> | |
| </div> | |
| <div class="direction-subsection" id="extensions"> | |
| <div class="direction-subsection-head"> | |
| <h3>Unified 20-task evidence and provenance.</h3> | |
| <p>All 20 tasks live in the same task table, task-card grid, radar, and 180-record result matrix. Historical result paths are retained only for exact provenance links.</p> | |
| </div> | |
| <div class="callout-row"> | |
| <div class="callout"> | |
| <h3>Unified task artifact package</h3> | |
| <p>The public task package has one structured 20-task contract, per-task metrics, prediction/rank files, summaries, radar charts, and the 180-record method-task matrix.</p> | |
| <p><a href="data/task_suite_20.json">Inspect task-contract source</a> · <a href="data/task_method_20_result_matrix.json">Read 180-result table</a> · <a href="assets/charts/unified_task_model_radar.svg">View unified radar</a></p> | |
| </div> | |
| <div class="callout"> | |
| <h3>One setup, one task surface</h3> | |
| <p>Every task uses the same 20-frame window unit, 5-frame stride, 8,546-dimensional feature manifest, chronological split discipline, and minimal/neural comparison pattern unless a task-specific leakage rule removes target-side features.</p> | |
| <p><a href="data/tier2_task_suite.json">Historical provenance data</a> and <a href="assets/charts/tier2_task_suite.svg">historical provenance chart</a> remain available for exact source tracing.</p> | |
| </div> | |
| </div> | |
| <img class="chart" src="assets/charts/research_direction_extension_tasks.svg?v=xperience10m-ext" alt="Four Xperience-10M research-direction extension probes with minimal and neural metrics"> | |
| <div class="extension-grid"> | |
| <article class="extension-card"> | |
| <span class="status-pill">A / motion</span> | |
| <h3>Body and Hand Motion Intensity</h3> | |
| <p><strong>Case:</strong> classify fast reach/pour windows as high motion and steady holding windows as low motion.</p> | |
| <p><strong>Input:</strong> non-mocap video, depth, pose, IMU, SLAM, calibration, and language features.</p> | |
| <p><strong>Output:</strong> high_motion or low_motion.</p> | |
| <div class="extension-metrics"><span><strong>0.7827</strong>minimal macro-F1</span><span><strong>0.7986</strong>neural macro-F1</span></div> | |
| </article> | |
| <article class="extension-card"> | |
| <span class="status-pill">B / views</span> | |
| <h3>Multi-View Consistency Retrieval</h3> | |
| <p><strong>Case:</strong> retrieve the synchronized stereo-left window from a fisheye-camera query.</p> | |
| <p><strong>Input:</strong> fisheye_cam0 video features against stereo_left candidate features.</p> | |
| <p><strong>Output:</strong> ranked synchronized view candidates.</p> | |
| <div class="extension-metrics"><span><strong>0.5534</strong>minimal MRR</span><span><strong>0.3469</strong>neural MRR</span></div> | |
| </article> | |
| <article class="extension-card"> | |
| <span class="status-pill">C / phase</span> | |
| <h3>Action Phase Progress Estimation</h3> | |
| <p><strong>Case:</strong> estimate whether a Pour coffee window is near the start, middle, or end of its action segment.</p> | |
| <p><strong>Input:</strong> non-caption multimodal features.</p> | |
| <p><strong>Output:</strong> 0-to-1 progress inside the current action.</p> | |
| <div class="extension-metrics"><span><strong>0.3416</strong>minimal MAE</span><span><strong>0.3038</strong>neural MAE</span></div> | |
| </article> | |
| <article class="extension-card"> | |
| <span class="status-pill">D / world</span> | |
| <h3>Short-Horizon Ego-Motion Forecasting</h3> | |
| <p><strong>Case:</strong> predict how the camera translation changes over the next 20 frames.</p> | |
| <p><strong>Input:</strong> current sensors excluding camera translation and captions.</p> | |
| <p><strong>Output:</strong> future camera-translation delta vector.</p> | |
| <div class="extension-metrics"><span><strong>0.1989</strong>minimal MAE</span><span><strong>0.0989</strong>neural MAE</span></div> | |
| </article> | |
| </div> | |
| <div class="callout-row"> | |
| <div class="callout"> | |
| <h3>What changed</h3> | |
| <p>The four research directions now have coded extension probes, prediction/rank CSVs, structured metrics, project summaries, and charts generated from real sample-window features.</p> | |
| </div> | |
| <div class="callout"> | |
| <h3>What still needs scale</h3> | |
| <p>A full research result still needs many Xperience-10M episodes, held-out episode splits, stronger encoders, and direction-specific models such as body priors, renderers, or persistent scene graphs.</p> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="roadmap" data-project-tab="directions" role="tabpanel" aria-labelledby="tab-directions" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Research roadmap.</h2> | |
| <p>The project path moves from the current public-sample task lab to the latest verified Qwen3-Omni diagnostic run, same-split 128-episode baseline alignment, a no-new-episode enhancement pack, action/subtask error analysis, robustness runs, world/policy tracks, and the future Xperience Embodied Foundation Model pretraining goal.</p> | |
| </div> | |
| <div class="roadmap-entry-panel" aria-label="Roadmap stage overview"> | |
| <div class="roadmap-entry-copy"> | |
| <span>Roadmap stages</span> | |
| <h3>Current results, next gates, and scale-up targets.</h3> | |
| <p>The roadmap connects shipped public-sample work, the selected-128 pilot, active quality work, and later model-training goals.</p> | |
| <div class="roadmap-quick-actions"> | |
| <a href="#roadmap-stages">Review stage cards</a> | |
| <a href="research_roadmap.html">Roadmap details</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/THREE_FOUNDATION_PIPELINES.md">Pipeline note</a> | |
| <a href="#result-matrix-table">180-result table</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/RESEARCH_ROADMAP.md">Roadmap note</a> | |
| </div> | |
| </div> | |
| <ol class="roadmap-entry-steps"> | |
| <li><strong>1. Public-sample task lab</strong><span>Task contracts, windows, baselines, neural heads, walkthroughs, and figures.</span></li> | |
| <li><strong>2. Selected-128 pilot</strong><span>Same-split baselines, Qwen3-Omni diagnostics, and public-safe result packages.</span></li> | |
| <li><strong>3. Scale-up path</strong><span>Quality analysis, robustness runs, world/policy tracks, and Xperience-native pretraining.</span></li> | |
| </ol> | |
| </div> | |
| <div class="roadmap-grid" id="roadmap-stages" aria-label="Research roadmap stages"> | |
| <article class="roadmap-card" data-status="implemented"> | |
| <span class="roadmap-status">implemented</span> | |
| <h3>Public-Sample Task Lab</h3> | |
| <p>One public episode is converted into aligned windows, task contracts, minimal baselines, neural heads, walkthroughs, and figures.</p> | |
| <div class="roadmap-meta"> | |
| <strong>Entry</strong><p>Public Xperience-10M sample episode available.</p> | |
| <strong>Evidence</strong><p>Status, protocol, takeaways, summary metrics, and episode-task outputs.</p> | |
| </div> | |
| </article> | |
| <article class="roadmap-card" data-status="implemented_for_first_pilot"> | |
| <span class="roadmap-status">implemented</span> | |
| <h3>Multi-Episode Data Preparation</h3> | |
| <p>Prepare official gated episodes while preserving episode-level separation and recording missing-view coverage. The first selected split is available for Qwen3-Omni diagnostics.</p> | |
| <div class="roadmap-meta"> | |
| <strong>Entry</strong><p>Gated data access and enough storage for selected episodes.</p> | |
| <strong>Evidence</strong><p>Selected-episode plan, data boundary, preparation notes, and verified package summary.</p> | |
| </div> | |
| </article> | |
| <article class="roadmap-card" data-status="verified_latest_branch"> | |
| <span class="roadmap-status">verified latest run</span> | |
| <h3>Qwen3-Omni LoRA Latest Diagnostic Branch</h3> | |
| <p>Train lightweight adapters on selected prepared episodes and evaluate on held-out episodes with committed predictions, metrics, and run reports.</p> | |
| <div class="roadmap-meta"> | |
| <strong>Entry</strong><p>Selected episodes prepared with no train/test episode leakage.</p> | |
| <strong>Evidence</strong><p>Verified result summary, v5/v6 comparison, dataset manifest, training metadata, progress logs, metrics, and predictions.</p> | |
| </div> | |
| </article> | |
| <article class="roadmap-card" data-status="verified_companion_result"> | |
| <span class="roadmap-status">verified companion result</span> | |
| <h3>128-Episode Same-Split Simple/NN Baselines</h3> | |
| <p>Align simple metadata/text baselines, raw-feature proxies, and neural MLP baselines to the same selected 96/16/16 split and the unified 20-task axes used by the public result matrix.</p> | |
| <div class="roadmap-meta"> | |
| <strong>Entry</strong><p>Derived Qwen JSONL export for the selected 96/16/16 split.</p> | |
| <strong>Evidence</strong><p>Baseline alignment report, summary metrics, task metrics, and the 128-task baseline runner.</p> | |
| </div> | |
| </article> | |
| <article class="roadmap-card" data-status="current"> | |
| <span class="roadmap-status">current no-new-episode plan</span> | |
| <h3>Selected-128 enhancement stage</h3> | |
| <p>Use the same selected split, estimate dense/multiscale window exports, define hierarchical action/subtask targets, and prioritize raw-feature shards for tasks that metadata baselines cannot cover.</p> | |
| <div class="roadmap-meta"> | |
| <strong>Entry</strong><p>Current 3,808-window selected 96/16/16 export and verified Qwen v4 metrics.</p> | |
| <strong>Evidence</strong><p>Enhancement note, structured enhancement data, dense-window CSV, and the enhancement builder script.</p> | |
| </div> | |
| </article> | |
| <article class="roadmap-card" data-status="active_next_step"> | |
| <span class="roadmap-status">active next step</span> | |
| <h3>Action/Subtask Error-Analysis Pass</h3> | |
| <p>Keep the 96/16/16 split, tighten JSON decoding or target formatting, and analyze action/subtask failures before presenting stronger model-quality numbers.</p> | |
| <div class="roadmap-meta"> | |
| <strong>Entry</strong><p>The final diagnostic package is verified, meets strict JSON validity, and exposes weak action/subtask quality.</p> | |
| <strong>Evidence</strong><p>Updated quality-target report, error-analysis tables, held-out metrics, and public-safe package.</p> | |
| </div> | |
| </article> | |
| <article class="roadmap-card" data-status="current"> | |
| <span class="roadmap-status">current</span> | |
| <h3>Foundation-Model Selection Matrix</h3> | |
| <p>Keep Qwen3-Omni as the first trainable held-out pilot, use Cosmos 3 for world modeling and forward-dynamics trainer development, and stage policy candidates after robot-compatible action targets are explicit.</p> | |
| <div class="roadmap-meta"> | |
| <strong>Entry</strong><p>Completed 128-episode preparation or a smaller 3-8 episode preprocessing dry run.</p> | |
| <strong>Evidence</strong><p>Foundation model plan, source links, model-specific entry conditions, and evaluation additions.</p> | |
| </div> | |
| </article> | |
| <article class="roadmap-card" data-status="partially_implemented"> | |
| <span class="roadmap-status">partially implemented</span> | |
| <h3>64-128 Episode Robustness Run</h3> | |
| <p>Test whether pilot conclusions survive broader sessions, missing modalities, and stronger ablations.</p> | |
| <div class="roadmap-meta"> | |
| <strong>Entry</strong><p>Selected multi-episode pilot trains and evaluates cleanly.</p> | |
| <strong>Evidence</strong><p>Metrics by session, task, modality, ablation, and failure type.</p> | |
| </div> | |
| </article> | |
| <article class="roadmap-card" data-status="planned"> | |
| <span class="roadmap-status">planned</span> | |
| <h3>Cosmos 3 and Policy-Model Extensions</h3> | |
| <p>Extend toward future-window prediction, action-conditioned world modeling, synthetic-data tests, policy-style next action, and affordance reasoning.</p> | |
| <div class="roadmap-meta"> | |
| <strong>Entry</strong><p>Enough multi-episode data, compute budget, and model-specific action or world-state targets.</p> | |
| <strong>Evidence</strong><p>Task-specific held-out evaluations, qualitative inspection, and updated model cards.</p> | |
| </div> | |
| </article> | |
| <article class="roadmap-card" data-status="future"> | |
| <span class="roadmap-status">future</span> | |
| <h3>Xperience Embodied Foundation Model Pretraining</h3> | |
| <p>Pretrain an Xperience-native domain model over synchronized video, audio, depth, pose, mocap, IMU, and language after smaller scaling stages prove value.</p> | |
| <div class="roadmap-meta"> | |
| <strong>Entry</strong><p>Full-corpus access, PB-scale storage path, multi-node compute, and positive scaling evidence.</p> | |
| <strong>Evidence</strong><p>Pretraining manifests, scaling curves, held-out evaluations, checkpoint inventory, model card, and data-boundary report.</p> | |
| </div> | |
| </article> | |
| </div> | |
| <div class="roadmap-resource-panel" aria-label="Roadmap resources and source records"> | |
| <div class="roadmap-resource-head"> | |
| <div> | |
| <span>Roadmap resources</span> | |
| <h3>Roadmap details and source records.</h3> | |
| </div> | |
| <p>The roadmap links the concise stage view, the full track-and-task detail page, and structured data records used for the public release.</p> | |
| </div> | |
| <div class="roadmap-resource-grid"> | |
| <article class="roadmap-resource-card primary"> | |
| <small>detail page</small> | |
| <h3>Focused roadmap detail page</h3> | |
| <p>The detail page keeps the four research directions, linked task heads, stage gates, current evidence, and next steps in one sequence without nested controls.</p> | |
| <div class="roadmap-resource-actions"> | |
| <a href="research_roadmap.html">Open details</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/RESEARCH_ROADMAP.md">Read roadmap note</a> | |
| </div> | |
| </article> | |
| <article class="roadmap-resource-card"> | |
| <small>training plan</small> | |
| <h3>Foundation model paths</h3> | |
| <p>Spatial intelligence, human-video world modeling, and VLA training use different inputs and outputs while sharing the Xperience-10M sample structure.</p> | |
| <div class="roadmap-resource-actions"> | |
| <a href="data/three_foundation_pipelines.json">Pipeline data</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/THREE_FOUNDATION_PIPELINES.md">Pipeline note</a> | |
| </div> | |
| </article> | |
| <article class="roadmap-resource-card"> | |
| <small>scale-up state</small> | |
| <h3>Selected-128 and data-boundary evidence</h3> | |
| <p>Use these notes to understand which selected-episode artifacts are public-safe, which raw files remain gated, and how the 128-episode comparison is staged.</p> | |
| <div class="roadmap-resource-actions"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PROJECT_STATUS.md">Project status</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_ACCESS_STATUS.md">Data status</a> | |
| </div> | |
| </article> | |
| <article class="roadmap-resource-card"> | |
| <small>future model</small> | |
| <h3>Xperience-native pretraining plan</h3> | |
| <p>The long-range goal is an Xperience-native embodied foundation model after smaller selected-episode stages prove value and infrastructure is ready.</p> | |
| <div class="roadmap-resource-actions"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">Pretraining plan</a> | |
| <a href="#directions">Four directions</a> | |
| </div> | |
| </article> | |
| </div> | |
| <details class="structured-mirrors"> | |
| <summary>Structured roadmap data</summary> | |
| <p class="structured-mirror-note">These records keep roadmap stages, foundation-model plans, and selected-128 enhancement data aligned across the public release.</p> | |
| <div class="structured-mirror-grid"> | |
| <a href="data/research_roadmap.json">Roadmap stage mirror</a> | |
| <a href="data/research_roadmap_interactive.json">Roadmap detail data</a> | |
| <a href="data/foundation_model_plan.json">Foundation-plan mirror</a> | |
| <a href="data/three_foundation_pipelines.json">Three-pipeline mirror</a> | |
| <a href="data/additional_development_directions.json">Development-direction mirror</a> | |
| <a href="data/task_suite_enhancement_128.json">Selected-128 enhancement mirror</a> | |
| </div> | |
| </details> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="evidence" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Current experiments and next milestones.</h2> | |
| <p>The project shows the completed public-sample task suite and the first verified multi-episode Qwen3-Omni diagnostic pilot, then lays out the next quality-improvement and model-extension steps.</p> | |
| </div> | |
| <div class="evidence-grid"> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Sample windows and 20-task results</h3> | |
| <p>5,821 frames become 1,161 synchronized 20-frame windows with an 8,546-dimensional representation, 20 task contracts, and the current 180-record public result matrix.</p> | |
| <div class="evidence-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/summary_report.json">task results</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/feature_manifest.json">feature inputs</a> | |
| <a href="data/task_method_20_result_matrix.json">result matrix</a> | |
| <a href="#suite">20-task suite</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Audio and modality evidence</h3> | |
| <p>Audio variants improve the primary metric on 6 walkthrough-backed task contracts, while the feature manifest and raw browser keep each modality source inspectable.</p> | |
| <div class="evidence-links"> | |
| <a href="data/audio_ablation_summary.json">audio summary</a> | |
| <a href="assets/charts/audio_ablation_delta.svg">delta chart</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/audio_ablation/AUDIO_ABLATION_SUMMARY.md">audio findings</a> | |
| <a href="#raw-sample">raw browser</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Research directions and foundation pipelines</h3> | |
| <p>The Ropedia directions are labeled as direct, proxy, or diagnostic coverage and connected to spatial intelligence, human-video world-model, and vision-language-action training paths.</p> | |
| <div class="evidence-links"> | |
| <a href="data/research_directions.json">direction map</a> | |
| <a href="data/research_direction_extensions.json">extension probes</a> | |
| <a href="data/three_foundation_pipelines.json">pipeline data</a> | |
| <a href="#directions">directions</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">current plan</span> | |
| <h3>Foundation backbones are separated by role</h3> | |
| <p>Qwen3-Omni is the trainable held-out LoRA track; Cosmos3 covers world-model diagnostics; OpenVLA/openpi/GR00T become policy candidates after robot-compatible action conversion; Xperience-native pretraining remains the later full-corpus goal.</p> | |
| <div class="evidence-links"> | |
| <a href="data/foundation_model_plan.json">foundation model plan</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/FOUNDATION_MODEL_PLAN.md">plan doc</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">pretraining plan</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified diagnostic</span> | |
| <h3>Qwen3-Omni and Cosmos3 model evidence</h3> | |
| <p>The selected 96/16/16 split has a verified Qwen3-Omni v6 package with 4,032 held-out predictions and 99.90% JSON validity. Cosmos3-Nano, Cosmos3-Super Reasoner, and Cosmos3-Super Forward-Dynamics LoRA remain separate public-safe diagnostic branches.</p> | |
| <div class="evidence-links"> | |
| <a href="data/omni_model_comparison.json">result comparison</a> | |
| <a href="data/omni_finetune_verified_result.json">pilot result</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/omni_finetune/verified_public">verified package</a> | |
| <a href="https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep">LoRA adapter</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">current plan</span> | |
| <h3>Selected-128 export and enhancement path</h3> | |
| <p>The current selected-128 line has a source/feature index and can be pushed through dense/multiscale windows without changing the held-out episode split; the recommended scenario is `multiscale_20s10_40s20_80s40`.</p> | |
| <div class="evidence-links"> | |
| <a href="data/xperience10m_128_episode_feature_index.json">source/feature index</a> | |
| <a href="data/task_suite_enhancement_128.json">enhancement data</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TASK_SUITE_ENHANCEMENT_128.md">enhancement report</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/task_suite_enhancement_128_v1_20260608/dense_window_scenarios.csv">dense scenarios</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Published mirrors and source anchors</h3> | |
| <p>The website, GitHub repo, HF Space, artifact dataset, baseline model repo, consolidated weights/results repo, collection, official dataset, and HOMIE toolkit point to the same research package.</p> | |
| <div class="evidence-links"> | |
| <a href="https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite">HF Space</a> | |
| <a href="https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts">artifact dataset</a> | |
| <a href="https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite">collection</a> | |
| <a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">official dataset</a> | |
| <a href="https://github.com/Ropedia/HOMIE-toolkit">HOMIE toolkit</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Visual system and figure index</h3> | |
| <p>The logo, raw-sample stream thumbnails, task-suite figure, unified radar, model-architecture figures, and LoRA training-flow figure are indexed and reused across published mirrors.</p> | |
| <div class="evidence-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/FIGURE_INDEX.md">figure guide</a> | |
| <a href="assets/charts/unified_task_model_radar.svg">20-task radar</a> | |
| <a href="assets/qwen3_omni_lora_pipeline.png">LoRA figure</a> | |
| <a href="assets/brand/xperience10m-logo-social-card.png">logo card</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Governance and reproduction</h3> | |
| <p>The project shares derived task artifacts, figures, reports, lightweight baselines, scripts, validators, and audit records. Raw videos, raw annotations, RRD visualizations, gated data, and full Qwen weights stay outside the repo.</p> | |
| <div class="evidence-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/DATA_NOTICE.md">data notice</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/REPRODUCIBILITY.md">reproducibility</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/notes/reproducibility_audit.md">latest rebuild</a> | |
| <a href="data/publication_audit.json">publication audit</a> | |
| </div> | |
| </article> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="reading-path" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Research reading path.</h2> | |
| <p>A newcomer should be able to move from the dataset sample to the task design, model baselines, current limitations, and scale-up plan without reading every file first.</p> | |
| </div> | |
| <div class="reading-grid"> | |
| <article class="reading-card"> | |
| <span class="step-index">01</span> | |
| <h3>Understand the current scope</h3> | |
| <p>Start with the project brief, status, dataset context, task results, roadmap, and Qwen3-Omni scale-up notes. They separate implemented single-episode work from the prepared multi-episode stage.</p> | |
| <div class="reading-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PROJECT_BRIEF.md">brief</a> | |
| <a href="data/project_status.json">current status</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE10M_DATASET_CARD_ALIGNMENT.md">dataset notes</a> | |
| <a href="data/summary_metrics.json">task metrics</a> | |
| <a href="data/research_roadmap.json">roadmap</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_ACCESS_STATUS.md">scale-up</a> | |
| </div> | |
| </article> | |
| <article class="reading-card"> | |
| <span class="step-index">02</span> | |
| <h3>Inspect one model input</h3> | |
| <p>Use the window table and feature manifest to see the aligned sample unit, modality sources, and leakage controls.</p> | |
| <div class="reading-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/windows.csv">windows</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/feature_manifest.json">features</a> | |
| </div> | |
| </article> | |
| <article class="reading-card"> | |
| <span class="step-index">03</span> | |
| <h3>Compare minimal vs neural heads</h3> | |
| <p>Every task has a small interpretable baseline and a matching neural MLP head over the same feature contract and chronological split.</p> | |
| <div class="reading-links"> | |
| <a href="data/summary_metrics.json">summary metrics</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/neural_mlp">neural heads</a> | |
| </div> | |
| </article> | |
| <article class="reading-card"> | |
| <span class="step-index">04</span> | |
| <h3>Check the scale-up gate</h3> | |
| <p>The multi-episode Qwen3-Omni path now has a final verified diagnostic package and public LoRA adapter. The native-pretraining plan shows how this can grow into a full-corpus research direction after action/subtask improvements and stronger task metrics.</p> | |
| <div class="reading-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_ACCESS_STATUS.md">scale-up status</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">data access</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">native pretraining</a> | |
| <a href="data/project_packet.json">project packet</a> | |
| </div> | |
| </article> | |
| <article class="reading-card"> | |
| <span class="step-index">05</span> | |
| <h3>Push the current 128 episodes harder</h3> | |
| <p>Use the no-new-episode enhancement pack before requesting more storage: it records dense-window estimates, `multiscale_20s10_40s20_80s40`, hierarchical labels, and raw-feature shard priorities.</p> | |
| <div class="reading-links"> | |
| <a href="data/task_suite_enhancement_128.json">enhancement data</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TASK_SUITE_ENHANCEMENT_128.md">enhancement report</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/omni/build_task_suite_enhancement_128.py">builder script</a> | |
| </div> | |
| </article> | |
| </div> | |
| <div class="boundary-strip"> | |
| <div class="boundary-item"><strong>Verified now</strong><span>One public episode, 5,821 frames, 1,161 aligned windows, 8,546 dimensions, 20 unified task contracts, 12 original neural heads, and 4 direction-extension probes.</span></div> | |
| <div class="boundary-item"><strong>Next: no-new-episode scale</strong><span>The selected 128-episode suite should next use dense/multiscale windows, hierarchical labels, and raw-feature shards before adding more episodes.</span></div> | |
| <div class="boundary-item"><strong>Next: error analysis</strong><span>The selected 128-episode Qwen3-Omni LoRA result has a final verified diagnostic package; JSON validity meets target, and the next pass should improve action/subtask quality.</span></div> | |
| <div class="boundary-item"><strong>Not redistributed</strong><span>Raw videos, raw annotations, full Qwen weights, and private gated Xperience-10M data are not included in the public repo or HF bundles.</span></div> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="glossary" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Glossary for project and field terms.</h2> | |
| <p>This glossary covers the overloaded project terms plus adjacent technical terms from embodied AI, egocentric multimodal data, spatial geometry, world models, VLA/policy learning, training, evaluation, and public artifact reading.</p> | |
| </div> | |
| <div class="glossary-panel"> | |
| <div class="glossary-summary" aria-label="Glossary categories"> | |
| <article> | |
| <small>data</small> | |
| <strong>Read multimodal sample terms</strong> | |
| <p>Episode, window, modality, fisheye, depth, IMU, calibration, and synchronization terms are grouped with the project data boundary terms.</p> | |
| </article> | |
| <article> | |
| <small>space + time</small> | |
| <strong>Decode geometry and world-model language</strong> | |
| <p>Camera pose, SLAM, point clouds, rollouts, forward dynamics, long-horizon forecasting, and temporal leakage are defined next to the relevant tasks.</p> | |
| </article> | |
| <article> | |
| <small>robotics</small> | |
| <strong>Connect VLA and policy terms</strong> | |
| <p>Action chunks, policies, imitation learning, behavior cloning, end effectors, dexterity, contact, and language grounding are included for extension work.</p> | |
| </article> | |
| <article> | |
| <small>evidence</small> | |
| <strong>Keep scores and published mirrors distinct</strong> | |
| <p>Direct/proxy scores, raw metrics, radar values, held-out evaluation, Qwen/Cosmos branches, adapters, and HF mirrors remain explicitly separated.</p> | |
| </article> | |
| </div> | |
| <div> | |
| <div class="glossary-controls" aria-label="Glossary search and filters"> | |
| <label class="glossary-search" for="glossarySearch"> | |
| <span>Search glossary</span> | |
| <input id="glossarySearch" type="search" placeholder="Search terms, meanings, task names, metrics, models..." autocomplete="off"> | |
| </label> | |
| <div class="glossary-filter-bar" aria-label="Filter glossary by category"> | |
| <button type="button" class="glossary-filter active" data-glossary-filter="all">All</button> | |
| <button type="button" class="glossary-filter" data-glossary-filter="data">Data</button> | |
| <button type="button" class="glossary-filter" data-glossary-filter="tasks">Tasks</button> | |
| <button type="button" class="glossary-filter" data-glossary-filter="metrics">Metrics</button> | |
| <button type="button" class="glossary-filter" data-glossary-filter="models">Models</button> | |
| <button type="button" class="glossary-filter" data-glossary-filter="robotics">Robotics</button> | |
| <button type="button" class="glossary-filter" data-glossary-filter="public">Public artifacts</button> | |
| </div> | |
| <p class="glossary-count" id="glossaryCount" aria-live="polite">Loading glossary terms...</p> | |
| </div> | |
| <div class="glossary-table-shell"> | |
| <div class="glossary-scroll-note"> | |
| <span>Scrollable term index</span> | |
| <p>Search and filters update this box; scroll inside it to scan the full glossary without stretching the page.</p> | |
| </div> | |
| <div class="glossary-table-wrap" tabindex="0" role="region" aria-label="Scrollable glossary term table"> | |
| <table class="glossary-table"> | |
| <thead> | |
| <tr> | |
| <th>Term</th> | |
| <th>Meaning here</th> | |
| <th>Use it for</th> | |
| <th>Do not confuse with</th> | |
| </tr> | |
| </thead> | |
| <tbody id="glossaryRows"> | |
| <tr><td>Egocentric video<small>multimodal sensing</small></td><td>Video captured from a first-person or body-mounted viewpoint.</td><td>The sample streams are egocentric views of human interaction and are the visual basis for many tasks.</td><td>Third-person robot-camera footage.</td></tr> | |
| <tr><td>Camera pose<small>spatial geometry</small></td><td>The camera position and orientation at a time step.</td><td>Supports spatial-intelligence tasks, view synchronization, and geometry diagnostics.</td><td>The human body pose.</td></tr> | |
| <tr><td>Forward dynamics<small>temporal and world models</small></td><td>Predicting the next state from the current state and action/context.</td><td>The Cosmos3-Super LoRA branch uses a forward-dynamics-style diagnostic contract.</td><td>Reverse inference from result back to cause.</td></tr> | |
| <tr><td>Vision-language-action model<small>robotics and VLA</small></td><td>A model that maps visual context and language into actions.</td><td>The VLA direction is a future path after action targets are converted into robot-compatible chunks.</td><td>A vision-language model that only answers text.</td></tr> | |
| <tr><td>Direct score<small>tasks and metrics</small></td><td>A metric computed against the task target directly.</td><td>The preferred score type in the 20-task matrix.</td><td>Compact-proxy score.</td></tr> | |
| <tr><td>Held-out evaluation<small>training and evaluation</small></td><td>Testing on examples not used for training.</td><td>Required before promoting Qwen/Cosmos results to public evidence.</td><td>Training-set loss.</td></tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| </div> | |
| <div class="glossary-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/GLOSSARY.md">Open complete glossary note</a> | |
| <a href="data/glossary.json">Inspect glossary source</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PUBLIC_READER_MAP.md">Evidence map</a> | |
| <a href="data/task_method_20_gap_audit.json">Review score/proxy audit</a> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="artifacts" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Choose the artifact by the question you have.</h2> | |
| <p>For public links, score checks, reproducible runs, or the next model-training package, start with the route cards below. The full artifact library remains available after that first choice. Raw Xperience-10M data and Qwen weights are not redistributed.</p> | |
| </div> | |
| <div class="resource-mode-grid" aria-label="Resource entry modes"> | |
| <article class="resource-mode"> | |
| <small>if you want public links</small> | |
| <strong>Open the right published mirror</strong> | |
| <p>Open GitHub, HF Space, artifact dataset, baseline models, or consolidated weights/results without guessing.</p> | |
| <a href="#artifact-panel-public-surfaces">Open published mirrors</a> | |
| </article> | |
| <article class="resource-mode"> | |
| <small>if you want to trust a score</small> | |
| <strong>Check result parity</strong> | |
| <p>Check source alignment, mirror parity, live URL hashes, and result audits before citing a number.</p> | |
| <a href="#artifact-panel-checks">Open checks</a> | |
| </article> | |
| <article class="resource-mode"> | |
| <small>if you want technical files</small> | |
| <strong>Open GitHub and Hugging Face</strong> | |
| <p>Scripts, manifests, task contracts, model cards, and derived artifacts stay in the source and mirror repositories.</p> | |
| <a href="#artifact-panel-public-surfaces">Open repositories</a> | |
| </article> | |
| <article class="resource-mode"> | |
| <small>if you want next models</small> | |
| <strong>Continue model work</strong> | |
| <p>Use Qwen3-Omni v6, Cosmos3-Super/Nano packages, the 128-episode feature index, and foundation-model plans for the next runs.</p> | |
| <a href="#omni-scale-up">Open scale-up</a> | |
| </article> | |
| </div> | |
| <div class="artifact-library"> | |
| <div class="content-tabs" role="tablist" aria-label="Artifact categories"> | |
| <button type="button" class="content-tab active" id="artifact-tab-task-heads" role="tab" data-panel-target="artifact-panel-task-heads" aria-selected="true" aria-pressed="true" aria-controls="artifact-panel-task-heads"> | |
| <strong>Task Heads</strong> | |
| <span>windows, tasks, metrics</span> | |
| </button> | |
| <button type="button" class="content-tab" id="artifact-tab-public-surfaces" role="tab" data-panel-target="artifact-panel-public-surfaces" aria-selected="false" aria-pressed="false" aria-controls="artifact-panel-public-surfaces" tabindex="-1"> | |
| <strong>Published Mirrors</strong> | |
| <span>repo, HF, project map</span> | |
| </button> | |
| <button type="button" class="content-tab" id="artifact-tab-scale-up" role="tab" data-panel-target="artifact-panel-scale-up" aria-selected="false" aria-pressed="false" aria-controls="artifact-panel-scale-up" tabindex="-1"> | |
| <strong>Scale-Up</strong> | |
| <span>Omni scale-up path</span> | |
| </button> | |
| <button type="button" class="content-tab" id="artifact-tab-checks" role="tab" data-panel-target="artifact-panel-checks" aria-selected="false" aria-pressed="false" aria-controls="artifact-panel-checks" tabindex="-1"> | |
| <strong>Checks</strong> | |
| <span>audits and parity</span> | |
| </button> | |
| </div> | |
| <section class="artifact-group tabbed-panel" id="artifact-panel-task-heads" role="tabpanel" aria-labelledby="artifact-tab-task-heads"> | |
| <div class="artifact-group-head"> | |
| <div><span>Research artifacts</span><h3>From one episode to task heads</h3></div> | |
| <p>Start with the files that define the sample windows, modality inputs, task contracts, metrics, walkthroughs, and research-direction mapping.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"><div><h3>Task results</h3><p>Every task definition, split detail, feature dimension, and minimal/neural metric in one project output.</p></div><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/summary_report.json">task results</a></article> | |
| <article class="artifact"><h3>Windows table</h3><p>Window start/end frames and aligned action/subtask labels for the public sample episode.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/windows.csv">window table</a></article> | |
| <article class="artifact"><h3>Feature inputs</h3><p>Source map for the current modality inputs used by the task suite.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/feature_manifest.json">feature inputs</a></article> | |
| <article class="artifact"><h3>Neural MLP task results</h3><p>Per-task PyTorch MLP metrics, predictions, histories, and checkpoints for the unified task contracts, with historical result-bundle paths retained for provenance.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/neural_mlp">neural MLP outputs</a></article> | |
| <article class="artifact"><h3>Four-direction taxonomy</h3><p>Maps the walkthrough-backed task contracts to the four research tracks: human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/research_directions">research direction outputs</a></article> | |
| <article class="artifact"><h3>Direction extension probes</h3><p>Four coded probes, one per research direction, with minimal and neural metrics plus prediction/rank CSVs.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/research_direction_extensions">extension probe outputs</a></article> | |
| <article class="artifact"><h3>Task walkthroughs</h3><p>Case studies for the walkthrough-backed task contracts, including input, middle process modules, output, metric, limitation, and task-player data.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/task_walkthroughs">walkthrough outputs</a></article> | |
| <article class="artifact"><h3>Audio ablation and raw upgrade</h3><p>All 72 task/variant rows comparing current audio, no audio, raw audio, replacement, and combined-input settings.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/audio_ablation">audio ablation outputs</a></article> | |
| <article class="artifact"><h3>Single-episode explorer</h3><p>Interactive window-level view of labels, predictions, modality statistics, object labels, and diagnostics.</p><a href="single_episode_explorer.html">open explorer</a></article> | |
| <article class="artifact"><h3>Cross-modal retrieval</h3><p>The strongest self-supervised signal from the single episode.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/cross_modal_retrieval/metrics.json">retrieval metrics</a></article> | |
| </div> | |
| </section> | |
| <section class="artifact-group tabbed-panel" id="artifact-panel-public-surfaces" role="tabpanel" aria-labelledby="artifact-tab-public-surfaces" hidden> | |
| <div class="artifact-group-head"> | |
| <div><span>Published mirrors</span><h3>Project map, mirrors, and runnable code</h3></div> | |
| <p>Project files, published mirrors, and scripts for the public-sample pipeline.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"><div><h3>Evidence map</h3><p>Single navigation view for GitHub, GitHub Pages, HF Space, artifact dataset, baseline model repo, Qwen3-Omni/Cosmos3 repos, and result lanes.</p></div><a href="data/public_reader_map.json">evidence map</a></article> | |
| <article class="artifact primary-artifact"><div><h3>Glossary</h3><p>Definitions for project-specific terms plus broader embodied-AI, egocentric multimodal data, spatial geometry, world-model, VLA, training, evaluation, adapter, and public mirror terms.</p></div><a href="data/glossary.json">glossary data</a><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/GLOSSARY.md">full glossary note</a></article> | |
| <article class="artifact primary-artifact"><div><h3>Artifact guide</h3><p>Human-readable map from project scope to data contract, task evidence, platform mirrors, and scale-up status.</p></div><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/ARTIFACT_GUIDE.md">artifact guide</a></article> | |
| <article class="artifact"><h3>Reproduction scripts</h3><p>Training, visualization, taxonomy, walkthrough, validator, and omni-readiness scripts.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/scripts">scripts/</a></article> | |
| <article class="artifact"><h3>Hugging Face Space</h3><p>The dashboard packaged as a public static Space.</p><a href="https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite">HF Space</a></article> | |
| <article class="artifact"><h3>GitHub Package</h3><p>Static dashboard container published to GitHub Container Registry for local browsing with Docker, without raw data or model weights.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/pkgs/container/ropedia-xperience-10m-task-suite">GHCR package</a></article> | |
| <article class="artifact"><h3>Derived HF artifacts</h3><p>Metrics, predictions, docs, and lightweight derived files without raw data redistribution.</p><a href="https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts">artifact collection</a></article> | |
| <article class="artifact"><h3>HF baseline models</h3><p>Minimal NumPy softmax, ridge baselines, and neural task-head model files.</p><a href="https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines">model repo</a></article> | |
| <article class="artifact"><h3>HF weights + results</h3><p>Consolidated public-safe baseline weights, Qwen3-Omni and Cosmos3 adapters/packages, verified results, analysis files, and manifest.</p><a href="https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results">weights/results repo</a></article> | |
| <article class="artifact"><h3>HF collection</h3><p>Space, artifacts, baseline models, Qwen3-Omni v6 LoRA, Cosmos3-Super, and Cosmos3-Nano repos grouped into one public project collection.</p><a href="https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite">collection</a></article> | |
| <article class="artifact"><h3>Current all-feature action model</h3><p>Classifier metrics, predictions, confusion matrix, and model weights.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/min_all_modalities_action_model/metrics.json">model metrics</a></article> | |
| <article class="artifact"><h3>Project packet</h3><p>Compact route from project scope to results after choosing a mirror.</p><a href="data/project_packet.json">project packet</a></article> | |
| </div> | |
| </section> | |
| <section class="artifact-group tabbed-panel" id="artifact-panel-scale-up" role="tabpanel" aria-labelledby="artifact-tab-scale-up" hidden> | |
| <div class="artifact-group-head"> | |
| <div><span>Scale-up path</span><h3>Verified diagnostic pilot</h3></div> | |
| <p>The multi-episode Qwen3-Omni path is documented, scripted, and verified as a validation-monitored held-out pilot. The next stronger metrics come from structured-output and error-analysis improvements.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"><div><h3>Two-line model comparison</h3><p>Groups Line 1 task-head baselines and Line 2 selected-128 methods: metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Nano Future Window, and Cosmos3-Super Reasoner.</p></div><a href="data/omni_model_comparison.json">result comparison</a></article> | |
| <article class="artifact primary-artifact"><div><h3>128-episode source + features</h3><p>Maps every selected official Xperience-10M episode id to its gated source tree and the public-safe processed features: Qwen v6 multiscale windows, dense multiscale rows, and metadata matrices.</p></div><a href="data/xperience10m_128_episode_feature_index.json">source/feature index</a></article> | |
| <article class="artifact"><h3>128-Episode Task Suite Enhancement Pack</h3><p>Canonical no-new-episode plan for denser supervision: `multiscale_20s10_40s20_80s40`, hierarchical action/subtask labels, stronger scoring slices, and raw-feature shard priorities.</p><a href="data/task_suite_enhancement_128.json">enhancement data</a></article> | |
| <article class="artifact"><h3>Foundation-model plan</h3><p>Backbone selection matrix covering Qwen3-Omni, Cosmos 3, GR00T, OpenVLA/openpi, Gemini Robotics, Octo, SmolVLA-style policy candidates, and the future Xperience-native pretraining goal.</p><a href="data/foundation_model_plan.json">foundation model plan</a></article> | |
| <article class="artifact"><h3>Multi-episode data access</h3><p>Public data-access path, selected 128-episode pilot plan, and preparation requirements.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">data access</a></article> | |
| <article class="artifact"><h3>Qwen3-Omni LoRA group</h3><p>Separates the 1-episode sensor-adapter smoke test from Qwen run v1-v6. v6 is the current 20-task matrix row, while v5 remains the pinned prior release.</p><a href="data/qwen3_omni_run_lineage.json">Qwen v1-v6 lineage</a><a href="data/omni_model_comparison.json">Qwen group</a></article> | |
| <article class="artifact"><h3>Cosmos3 groups</h3><p>Shows the verified Nano future-window compatibility package, the Super base-weight Reasoner JSON-task evaluation, and the Super fine-tuned forward-dynamics LoRA artifact with separate loss metrics.</p><a href="data/omni_model_comparison.json">Cosmos groups</a></article> | |
| <article class="artifact"><h3>Scale-up requirement</h3><p>Future runs need validation tracking, held-out predictions, quality-target reporting, and the same public-safe package gate.</p><a href="data/foundation_model_plan.json">training requirements</a></article> | |
| <article class="artifact"><h3>Xperience-native pretraining</h3><p>Future plan for a domain-specific embodied foundation model trained from scratch over full-corpus video, audio, geometry, motion, inertial, and language streams.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">pretraining plan</a></article> | |
| </div> | |
| </section> | |
| <section class="artifact-group tabbed-panel" id="artifact-panel-checks" role="tabpanel" aria-labelledby="artifact-tab-checks" hidden> | |
| <div class="artifact-group-head"> | |
| <div><span>Release checks</span><h3>Validators and parity records</h3></div> | |
| <p>Audit files for website integrity, mirror parity, live publication status, source alignment, and release quality gates.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"><div><h3>Publication audit</h3><p>Checks the expected public package files, docs, figures, structured data, and artifact boundaries before a release is trusted.</p></div><a href="data/publication_audit.json">publication audit</a></article> | |
| <article class="artifact"><h3>Website integrity</h3><p>Validates local website links, referenced data files, asset paths, and generated dashboard dependencies.</p><a href="data/website_integrity.json">website integrity</a></article> | |
| <article class="artifact"><h3>Mirror parity</h3><p>Compares GitHub, HF Space, artifact dataset, baseline model repo, and weights/results mirror snapshots.</p><a href="data/mirror_parity.json">mirror parity</a></article> | |
| <article class="artifact"><h3>Publication QA</h3><p>Records mirror readiness, evidence-map links, live status pointers, and cross-repo publication checks.</p><a href="data/public_surface_qa.json">publication QA</a></article> | |
| <article class="artifact"><h3>Live publication status</h3><p>Tracks live URLs and hash checks used after publishing to GitHub Pages and Hugging Face surfaces.</p><a href="data/live_publication_status.json">live status</a></article> | |
| <article class="artifact"><h3>Source alignment audit</h3><p>Separates official Xperience-10M source facts from local/public project inventory and derived artifacts.</p><a href="data/source_alignment_audit.json">source alignment</a></article> | |
| <article class="artifact"><h3>Task surface validation</h3><p>Checks task-count, task-contract, and result-matrix consistency across generated public data files.</p><a href="data/task_surface_integrity.json">task surface</a></article> | |
| <article class="artifact"><h3>Quality gates</h3><p>Summarizes build, validation, mirror, and publication gates that should pass before relying on the release.</p><a href="data/quality_gates.json">quality gates</a></article> | |
| </div> | |
| </section> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="omni-scale-up" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Qwen3-Omni diagnostic run is verified.</h2> | |
| <p>The selected pilot uses 128 source-balanced episodes across 128 different session UUIDs. The latest v6 held-out package is verified, and its weak metrics define the next structured-output and error-analysis pass.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact"><h3>Selection</h3><p>128 complete episodes selected from 128 unique top-level sessions, balanced across episode-size bands and split 96/16/16 for train/val/test.</p><a href="data/xperience10m_128_episode_feature_index.json">source/feature index</a></article> | |
| <article class="artifact"><h3>Transfer</h3><p>Download raw episodes only from official gated sources, exclude visualization.rrd, validate files, then stage them for training.</p></article> | |
| <article class="artifact"><h3>Current LoRA artifact</h3><p>The current Qwen3-Omni LoRA artifact is the verified v6 selected 128-episode diagnostic adapter. The v5 row remains pinned as the prior release, and the 1-episode Qwen entry is only a sensor-adapter smoke test.</p><a href="data/omni_model_comparison.json">model groups</a></article> | |
| <article class="artifact"><h3>No-new-episode suite push</h3><p>The next suite push does not need more episodes first: use `multiscale_20s10_40s20_80s40`, hierarchical action/subtask targets, and raw-feature shards while keeping the held-out split fixed.</p><a href="data/task_suite_enhancement_128.json">enhancement data</a></article> | |
| <article class="artifact"><h3>Backbone tracks</h3><p>Qwen3-Omni uses a separate LoRA model repo; Cosmos3-Nano remains a compatibility package; Cosmos3-Super now has a verified forward-dynamics LoRA artifact with weights in a dedicated model repo.</p><a href="https://huggingface.co/cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep">Cosmos3-Super weights</a></article> | |
| <article class="artifact"><h3>Native foundation model</h3><p>The long-term goal is a full-corpus Xperience Embodied Foundation Model trained on synchronized perception, geometry, motion, inertial, audio, and language streams after smaller scaling stages validate the approach.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">pretraining plan</a></article> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="foundation-model-paths" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Foundation-model scale-up paths.</h2> | |
| <p>These three paths translate Xperience-10M files into larger spatial, world-model, and vision-language-action training directions after the current task suite and selected-128 diagnostic results.</p> | |
| </div> | |
| <div class="foundation-pipeline-grid" aria-label="Three high-resolution foundation direction slide diagrams"> | |
| <article class="foundation-pipeline-card"> | |
| <img src="assets/foundation-pipelines/spatial-intelligence-pipeline.png?v=foundation-slides-v10" alt="High-resolution slide diagram showing the Spatial intelligence models direction for Xperience-10M."> | |
| <div class="foundation-pipeline-body"> | |
| <span>High-resolution direction slide</span> | |
| <h3>Spatial intelligence models</h3> | |
| <p>Train spatial-memory models from multiview RGB, egocentric video, depth, pose, calibration, object/contact cues, and language prompts; evaluate spatial QA, object permanence, counting, retrieval, and pose-aware consistency.</p> | |
| <div class="foundation-io-panel" aria-label="Spatial intelligence one-sample training input and output"> | |
| <div class="foundation-io-row"> | |
| <strong>Sample input</strong> | |
| <p>Use <code>windows.csv</code> and <code>shared_windows.npz</code> to slice each 20-frame window, then join six MP4 RGB streams with <code>annotation.hdf5</code> depth, camera pose, SLAM/calibration, object cues, contacts, and optional language questions.</p> | |
| </div> | |
| <div class="foundation-io-row"> | |
| <strong>Training output</strong> | |
| <p>Build targets such as camera-view match, object relevance, object-set memory, depth/pose reconstruction proxy, caption-grounded retrieval, and spatial QA answers derived from the same public annotation timeline.</p> | |
| </div> | |
| <div class="foundation-io-row"> | |
| <strong>Existing hooks</strong> | |
| <div class="foundation-io-tasks"> | |
| <a href="#suite">Tasks 8/10/12/14/16</a> | |
| <a href="data/task_suite_20.json">20-task contract data</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/windows.csv">window manifest</a> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="foundation-pipeline-links"> | |
| <a href="data/three_foundation_pipelines.json">Structured pipeline mirror</a> | |
| <a href="assets/foundation-pipelines/spatial-intelligence-pipeline.png">Open image</a> | |
| </div> | |
| </div> | |
| </article> | |
| <article class="foundation-pipeline-card"> | |
| <img src="assets/foundation-pipelines/human-video-world-model-pipeline.png?v=foundation-slides-v10" alt="High-resolution slide diagram showing the Human-video world models direction for Xperience-10M."> | |
| <div class="foundation-pipeline-body"> | |
| <span>High-resolution direction slide</span> | |
| <h3>Human-video world models</h3> | |
| <p>Train future-prediction models from observed interaction windows to score next action, next subtask, future object set, contact transition, camera-motion delta, and latent future state, with Qwen-style probes and Cosmos-style dynamics kept separate.</p> | |
| <div class="foundation-io-panel" aria-label="Human-video world-model one-sample training input and output"> | |
| <div class="foundation-io-row"> | |
| <strong>Sample input</strong> | |
| <p>Take the current 20-frame observed window at time <code>t</code> from <code>shared_windows.npz</code>: RGB/audio/sensor summaries, hand/body motion, camera pose, current object/contact state, and current action/subtask context only.</p> | |
| </div> | |
| <div class="foundation-io-row"> | |
| <strong>Training output</strong> | |
| <p>Shift the same episode timeline forward to produce next-action, next-subtask, future object-set, contact-transition, time-to-transition, camera-motion delta, or latent/future-feature targets. Future labels stay out of the input.</p> | |
| </div> | |
| <div class="foundation-io-row"> | |
| <strong>Existing hooks</strong> | |
| <div class="foundation-io-tasks"> | |
| <a href="#suite">Tasks 4/13/14/17/20</a> | |
| <a href="data/unified_task_model_radar.json">model scores</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json">future probes</a> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="foundation-pipeline-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/THREE_FOUNDATION_PIPELINES.md">Track note</a> | |
| <a href="assets/foundation-pipelines/human-video-world-model-pipeline.png">Open image</a> | |
| </div> | |
| </div> | |
| </article> | |
| <article class="foundation-pipeline-card"> | |
| <img src="assets/foundation-pipelines/vision-language-action-pipeline.png?v=foundation-slides-v11" alt="High-resolution slide diagram showing the Vision-language-action models direction for Xperience-10M."> | |
| <div class="foundation-pipeline-body"> | |
| <span>High-resolution direction slide</span> | |
| <h3>Vision-language-action models</h3> | |
| <p>Train VLA or policy-compatible heads only after converting egocentric video, captions, hand/body motion, contacts, objects, and procedures into traceable action tokens, chunks, and object-conditioned action targets.</p> | |
| <div class="foundation-io-panel" aria-label="Vision-language-action one-sample training input and output"> | |
| <div class="foundation-io-row"> | |
| <strong>Sample input</strong> | |
| <p>Use egocentric/fisheye video windows, caption/object context from <code>annotation.hdf5</code>, hand/body mocap, contact state, and current subtask text as the observation-language side of each training pair.</p> | |
| </div> | |
| <div class="foundation-io-row"> | |
| <strong>Training output</strong> | |
| <p>For the one-sample suite, output action-token proxies: current/next action, object-conditioned action relation, contact state, interaction-text class, subtask transition, or hand-trajectory/action-chunk proxy. Robot action chunks need a later retargeting converter.</p> | |
| </div> | |
| <div class="foundation-io-row"> | |
| <strong>Existing hooks</strong> | |
| <div class="foundation-io-tasks"> | |
| <a href="#suite">Tasks 1/4/5/6/15/18</a> | |
| <a href="data/task_walkthroughs.json">task walkthroughs</a> | |
| <a href="data/three_foundation_pipelines.json">track contract</a> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="foundation-pipeline-links"> | |
| <a href="data/three_foundation_pipelines.json">Structured pipeline mirror</a> | |
| <a href="assets/foundation-pipelines/vision-language-action-pipeline.png">Open image</a> | |
| </div> | |
| </div> | |
| </article> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact"><h3>Episode taxonomy and data engine</h3><p>Build an episode atlas, category tags, balance report, and split builder across activities, objects, scenes, sessions, people, and missing modalities.</p><a href="data/additional_development_directions.json">Open development plan</a></article> | |
| <article class="artifact"><h3>Standardized benchmark protocol</h3><p>Version train/val/test manifests, task cards, leakage checks, metric scripts, and reference baselines so future model scores are comparable.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/ADDITIONAL_DEVELOPMENT_DIRECTIONS.md">Open benchmark protocol note</a></article> | |
| <article class="artifact"><h3>Multimodal representation learning</h3><p>Train contrastive and masked-prediction encoders over synchronized video, audio, depth, pose, mocap, IMU, and language windows.</p><a href="data/additional_development_directions.json">Open representation-learning plan</a></article> | |
| <article class="artifact"><h3>Skill and procedure graphs</h3><p>Mine action steps, transitions, preconditions, effects, and temporal graphs that connect egocentric perception to planning.</p><a href="data/research_directions.json">current task map</a></article> | |
| <article class="artifact"><h3>Human-object affordances</h3><p>Add contact, reachable-object, tool-use, and next-affordance tasks using hands, mocap, objects, contacts, video, and language.</p><a href="data/task_walkthroughs.json">task walkthroughs</a></article> | |
| <article class="artifact"><h3>3D/4D scene and object memory</h3><p>Fuse depth, pose/SLAM, multiview video, and object cues into persistent scene/object maps for spatial reasoning and object permanence.</p><a href="data/foundation_model_plan.json">model tracks</a></article> | |
| <article class="artifact"><h3>Quality and sync diagnostics</h3><p>Track timestamp drift, missing streams, calibration consistency, corrupted files, and degraded-mode manifests before large training runs.</p><a href="data/evidence_contract.json">evidence contract</a></article> | |
| <article class="artifact"><h3>Policy and simulation transfer</h3><p>Convert mocap, hand trajectories, contacts, and object states into action tokens, robot-compatible targets, and imitation-learning examples.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/FOUNDATION_MODEL_PLAN.md">foundation plan</a></article> | |
| </div> | |
| </div> | |
| </section> | |
| </main> | |
| <footer> | |
| <div class="wrap"> | |
| Made by Chaoyue He. <span class="footer-meta">Contact: <a href="https://chaoyue0307.github.io/">chaoyue0307.github.io</a></span> | |
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| document.documentElement.style.setProperty("--scroll-progress", progress.toFixed(4)); | |
| nav?.classList.toggle("is-scrolled", window.scrollY > 12); | |
| companion?.classList.toggle("is-visible", window.scrollY > 420); | |
| if (percentLabel) percentLabel.textContent = `${percent}%`; | |
| if (sectionLabel) sectionLabel.textContent = currentReadingSection(); | |
| } | |
| function animateCounter(element) { | |
| if (element.dataset.counted === "true") return; | |
| element.dataset.counted = "true"; | |
| if (!prefersReducedMotion.matches) element.classList.add("count-highlight"); | |
| } | |
| function observeXperienceReveal(root = document) { | |
| if (!xperienceRevealObserver) return; | |
| root.querySelectorAll(xperienceRevealSelector).forEach((element, index) => { | |
| if (element.classList.contains("reveal-ready")) return; | |
| element.classList.add("reveal-ready"); | |
| if (!element.style.getPropertyValue("--reveal-delay")) { | |
| element.style.setProperty("--reveal-delay", `${Math.min(index % 4, 3) * 54}ms`); | |
| } | |
| xperienceRevealObserver.observe(element); | |
| }); | |
| } | |
| function initPageMotion() { | |
| document.body.classList.add("motion-ready"); | |
| updatePageProgress(); | |
| window.addEventListener("scroll", updatePageProgress, { passive: true }); | |
| window.addEventListener("resize", updatePageProgress); | |
| document.getElementById("backToTop")?.addEventListener("click", () => { | |
| window.scrollTo({ top: 0, behavior: prefersReducedMotion.matches ? "auto" : "smooth" }); | |
| }); | |
| const counters = Array.from(document.querySelectorAll( | |
| ".hero-stats .stat strong, .suite-line-facts strong, .hero-radar-stat strong, .result-matrix-stat strong" | |
| )); | |
| if (prefersReducedMotion.matches || !("IntersectionObserver" in window)) { | |
| document.querySelectorAll(xperienceRevealSelector).forEach((element) => element.classList.add("is-visible")); | |
| counters.forEach(animateCounter); | |
| return; | |
| } | |
| xperienceRevealObserver = new IntersectionObserver((entries) => { | |
| entries.forEach((entry) => { | |
| if (!entry.isIntersecting) return; | |
| entry.target.classList.add("is-visible"); | |
| if (entry.target.matches(".stat, .suite-line-card, .hero-radar-stat, .result-matrix-stat")) { | |
| entry.target.querySelectorAll("strong").forEach(animateCounter); | |
| } | |
| xperienceRevealObserver.unobserve(entry.target); | |
| }); | |
| }, { rootMargin: "0px 0px -12% 0px", threshold: 0.14 }); | |
| window.observeXperienceReveal = observeXperienceReveal; | |
| observeXperienceReveal(document); | |
| } | |
| initPageMotion(); | |
| function escapeScopeHtml(value) { | |
| return String(value ?? "") | |
| .replace(/&/g, "&") | |
| .replace(/</g, "<") | |
| .replace(/>/g, ">") | |
| .replace(/"/g, """) | |
| .replace(/'/g, "'"); | |
| } | |
| const dataScopeCopy = { | |
| sample: { | |
| kicker: "selected scope / public sample", | |
| title: "Public sample episode: inspectable raw evidence.", | |
| body: "The public sample is the only raw episode mirrored into the browser. Use it to understand file roles, synchronized streams, HDF5 contents, modality features, and how one episode becomes 20-frame task windows.", | |
| facts: [ | |
| ["Raw contents", "6 MP4 streams, audio in MP4, annotation.hdf5, and visualization.rrd."], | |
| ["Training view", "1,161 aligned windows with 8,546 feature dimensions."], | |
| ["Best use", "File inspection, task construction, and local reproducibility."], | |
| ["Boundary", "One official episode; no diversity or scale claim."] | |
| ] | |
| }, | |
| selected: { | |
| kicker: "selected scope / selected 128", | |
| title: "Selected 128: same-split model and baseline surface.", | |
| body: "The 128-episode line is the public-safe comparison surface for baselines and model probes. It keeps a fixed chronological split, derived feature rows, and source-linked windows without redistributing gated raw episodes.", | |
| facts: [ | |
| ["Split", "96 train, 16 validation, and 16 test episodes."], | |
| ["Rows", "34,269 Qwen v6 multiscale rows and 106,095 dense compact rows."], | |
| ["Best use", "20-task scoring, radar comparison, method rows, and result-matrix updates."], | |
| ["Boundary", "Derived public artifacts only; raw episode media remains gated upstream."] | |
| ] | |
| }, | |
| full: { | |
| kicker: "selected scope / HF gated full dataset", | |
| title: "Hugging Face full dataset: upstream scale from file metadata.", | |
| body: "The full-dataset view summarizes authenticated Hugging Face Hub file metadata for ropedia-ai/xperience-10m. It explains session count, episode-like folder count, file composition, and storage footprint without inspecting private row contents.", | |
| facts: [ | |
| ["Repository scale", "804 top-level sessions, 12,103 episode-like folders, and 85,257 files."], | |
| ["Training bytes", "24.63 TiB excluding optional Rerun visualization files."], | |
| ["Best use", "Corpus scale, file organization, and download planning."], | |
| ["Boundary", "Metadata-only; gated rows and raw files are not redistributed here."] | |
| ] | |
| } | |
| }; | |
| function initDataScopeExplorer() { | |
| const root = document.querySelector("[data-scope-explorer]"); | |
| if (!root) return; | |
| const triggers = Array.from(root.querySelectorAll("[data-scope]")); | |
| const chartCards = Array.from(document.querySelectorAll("[data-scope-card]")); | |
| const kicker = root.querySelector("[data-scope-detail-kicker]"); | |
| const title = root.querySelector("[data-scope-detail-title]"); | |
| const body = root.querySelector("[data-scope-detail-body]"); | |
| const facts = root.querySelector("[data-scope-detail-facts]"); | |
| function renderFacts(scope) { | |
| const entry = dataScopeCopy[scope] || dataScopeCopy.sample; | |
| facts.innerHTML = entry.facts.map(([label, value]) => ( | |
| `<span><strong>${escapeScopeHtml(label)}</strong>${escapeScopeHtml(value)}</span>` | |
| )).join(""); | |
| } | |
| function setScope(scope) { | |
| const entry = dataScopeCopy[scope] || dataScopeCopy.sample; | |
| root.dataset.activeScope = scope; | |
| triggers.forEach((button) => { | |
| const active = button.dataset.scope === scope; | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| }); | |
| chartCards.forEach((card) => { | |
| const cardScopes = (card.dataset.scopeCard || "").split(/\s+/); | |
| const active = cardScopes.includes(scope) || cardScopes.includes("overview"); | |
| card.classList.toggle("is-scope-active", active); | |
| }); | |
| kicker.textContent = entry.kicker; | |
| title.textContent = entry.title; | |
| body.textContent = entry.body; | |
| renderFacts(scope); | |
| } | |
| triggers.forEach((button) => { | |
| button.addEventListener("click", () => setScope(button.dataset.scope)); | |
| }); | |
| setScope(root.dataset.defaultScope || triggers[0]?.dataset.scope || "sample"); | |
| } | |
| initDataScopeExplorer(); | |
| const modalityMeta = { | |
| video: { label: "Video", src: "assets/modalities/video.jpg" }, | |
| audio: { label: "Audio", src: "assets/modalities/audio.png" }, | |
| depth: { label: "Depth", src: "assets/modalities/depth.jpg" }, | |
| pose_slam: { label: "Pose / SLAM", src: "assets/modalities/pose_slam.png" }, | |
| motion_capture: { label: "Motion Capture", src: "assets/modalities/motion_capture.png" }, | |
| inertial: { label: "Inertial", src: "assets/modalities/inertial.png" }, | |
| language: { label: "Language", src: "assets/modalities/language.png" } | |
| }; | |
| const taskIconMeta = { | |
| timeline_action: { src: "assets/task-icons/01_timeline_action.svg", alt: "Action recognition task icon" }, | |
| timeline_subtask: { src: "assets/task-icons/02_timeline_subtask.svg", alt: "Procedure step recognition task icon" }, | |
| transition_detection: { src: "assets/task-icons/03_transition_detection.svg", alt: "Action boundary detection task icon" }, | |
| next_action: { src: "assets/task-icons/04_next_action.svg", alt: "Next-action prediction task icon" }, | |
| hand_trajectory_forecast: { src: "assets/task-icons/05_hand_trajectory_forecast.svg", alt: "Hand trajectory forecasting task icon" }, | |
| contact_prediction: { src: "assets/task-icons/06_contact_prediction.svg", alt: "Contact state prediction task icon" }, | |
| object_relevance: { src: "assets/task-icons/07_object_relevance.svg", alt: "Object relevance prediction task icon" }, | |
| caption_grounding: { src: "assets/task-icons/08_caption_grounding.svg", alt: "Language grounding task icon" }, | |
| cross_modal_retrieval: { src: "assets/task-icons/09_cross_modal_retrieval.svg", alt: "Cross-modal retrieval task icon" }, | |
| modality_reconstruction: { src: "assets/task-icons/10_modality_reconstruction.svg", alt: "Cross-modal reconstruction task icon" }, | |
| temporal_order: { src: "assets/task-icons/11_temporal_order.svg", alt: "Temporal order verification task icon" }, | |
| misalignment_detection: { src: "assets/task-icons/12_misalignment_detection.svg", alt: "Multimodal synchronization detection task icon" }, | |
| long_horizon_next_action: { src: "assets/task-icons/13_long_horizon_next_action.svg", alt: "Long-horizon next-action forecasting task icon" }, | |
| next_subtask_forecast: { src: "assets/task-icons/14_next_subtask_forecast.svg", alt: "Long-horizon next-subtask forecasting task icon" }, | |
| interaction_text_prediction: { src: "assets/task-icons/15_interaction_text_prediction.svg", alt: "Interaction text prediction task icon" }, | |
| action_object_relation: { src: "assets/task-icons/16_action_object_relation.svg", alt: "Action-object relation prediction task icon" }, | |
| object_set_forecast: { src: "assets/task-icons/17_object_set_forecast.svg", alt: "Future object-set forecasting task icon" }, | |
| imu_to_hand_pose: { src: "assets/task-icons/18_imu_to_hand_pose.svg", alt: "IMU-to-hand pose reconstruction task icon" }, | |
| camera_view_sync_retrieval: { src: "assets/task-icons/19_camera_view_sync_retrieval.svg", alt: "Camera-view synchronization retrieval task icon" }, | |
| time_to_transition: { src: "assets/task-icons/20_time_to_transition.svg", alt: "Time-to-next-transition regression task icon" } | |
| }; | |
| const taskIconFor = (task) => taskIconMeta[task?.task_id] || taskIconMeta.timeline_action; | |
| const familyAccent = { | |
| supervised: "#9bdfff", | |
| forecast: "#ccffa0", | |
| retrieval: "#7ae5c3", | |
| diagnostic: "#d8f4a5" | |
| }; | |
| let taskEntries = []; | |
| let taskResultSeries = []; | |
| let taskResultRecordsByNumber = new Map(); | |
| let activeTaskIndex = 0; | |
| let activeStageIndex = 0; | |
| let activeFilter = "all"; | |
| let playerTimer = null; | |
| const storyStages = [ | |
| { key: "input", label: "Input" }, | |
| { key: "process", label: "Process" }, | |
| { key: "output", label: "Output" }, | |
| { key: "evaluate", label: "Evaluate" } | |
| ]; | |
| const rawSampleFiles = [ | |
| { | |
| name: "fisheye_cam0.mp4", | |
| kind: "video + audio", | |
| bytes: 89842251, | |
| description: "Fisheye camera 0 stream and the public sample audio source. The page plays a browser-optimized preview and links the complete raw MP4.", | |
| use: "Video features feed visual tasks; the embedded audio stream feeds audio ablation and acoustic feature blocks.", | |
| url: "https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample/resolve/main/fisheye_cam0.mp4", | |
| previewUrl: "assets/raw-sample-preview/fisheye_cam0_preview.mp4", | |
| posterUrl: "assets/raw-sample-preview/fisheye_cam0_poster.jpg", | |
| previewBytes: 506126, | |
| previewNote: "Playing a 12 second fast-start preview derived from the official raw MP4. Use the source link for the complete file.", | |
| mediaType: "video", | |
| hasAudio: true | |
| }, | |
| { | |
| name: "fisheye_cam1.mp4", | |
| kind: "video", | |
| bytes: 127085978, | |
| description: "Second synchronized fisheye camera stream from the same episode.", | |
| use: "Used as one of the synchronized visual feature streams.", | |
| url: "https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample/resolve/main/fisheye_cam1.mp4", | |
| previewUrl: "assets/raw-sample-preview/fisheye_cam1_preview.mp4", | |
| posterUrl: "assets/raw-sample-preview/fisheye_cam1_poster.jpg", | |
| previewBytes: 648798, | |
| previewNote: "Playing a 12 second fast-start preview derived from the official raw MP4. Use the source link for the complete file.", | |
| mediaType: "video", | |
| hasAudio: false | |
| }, | |
| { | |
| name: "fisheye_cam2.mp4", | |
| kind: "video", | |
| bytes: 113406041, | |
| description: "Third synchronized fisheye camera stream from the same episode.", | |
| use: "Used as one of the synchronized visual feature streams.", | |
| url: "https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample/resolve/main/fisheye_cam2.mp4", | |
| previewUrl: "assets/raw-sample-preview/fisheye_cam2_preview.mp4", | |
| posterUrl: "assets/raw-sample-preview/fisheye_cam2_poster.jpg", | |
| previewBytes: 659709, | |
| previewNote: "Playing a 12 second fast-start preview derived from the official raw MP4. Use the source link for the complete file.", | |
| mediaType: "video", | |
| hasAudio: false | |
| }, | |
| { | |
| name: "fisheye_cam3.mp4", | |
| kind: "video", | |
| bytes: 95024162, | |
| description: "Fourth synchronized fisheye camera stream from the same episode.", | |
| use: "Used as one of the synchronized visual feature streams.", | |
| url: "https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample/resolve/main/fisheye_cam3.mp4", | |
| previewUrl: "assets/raw-sample-preview/fisheye_cam3_preview.mp4", | |
| posterUrl: "assets/raw-sample-preview/fisheye_cam3_poster.jpg", | |
| previewBytes: 330306, | |
| previewNote: "Playing a 12 second fast-start preview derived from the official raw MP4. Use the source link for the complete file.", | |
| mediaType: "video", | |
| hasAudio: false | |
| }, | |
| { | |
| name: "stereo_left.mp4", | |
| kind: "video", | |
| bytes: 22674748, | |
| description: "Left stereo RGB stream, synchronized with the fisheye cameras and annotation container.", | |
| use: "Used for visual features and multi-view consistency retrieval targets.", | |
| url: "https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample/resolve/main/stereo_left.mp4", | |
| previewUrl: "assets/raw-sample-preview/stereo_left_preview.mp4", | |
| posterUrl: "assets/raw-sample-preview/stereo_left_poster.jpg", | |
| previewBytes: 594158, | |
| previewNote: "Playing a 12 second fast-start preview derived from the official raw MP4. Use the source link for the complete file.", | |
| mediaType: "video", | |
| hasAudio: false | |
| }, | |
| { | |
| name: "stereo_right.mp4", | |
| kind: "video", | |
| bytes: 25415328, | |
| description: "Right stereo RGB stream, synchronized with stereo_left and the fisheye cameras.", | |
| use: "Used as a paired stereo visual stream.", | |
| url: "https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample/resolve/main/stereo_right.mp4", | |
| previewUrl: "assets/raw-sample-preview/stereo_right_preview.mp4", | |
| posterUrl: "assets/raw-sample-preview/stereo_right_poster.jpg", | |
| previewBytes: 650983, | |
| previewNote: "Playing a 12 second fast-start preview derived from the official raw MP4. Use the source link for the complete file.", | |
| mediaType: "video", | |
| hasAudio: false | |
| }, | |
| { | |
| name: "annotation.hdf5", | |
| kind: "HDF5 annotations", | |
| bytes: 1931496028, | |
| description: "Primary synchronized annotation and sensor container for depth, pose, mocap, IMU, calibration, metadata, caption text, and labels.", | |
| use: "Loaded by HOMIE and task-suite scripts to build aligned labels, sensor features, windows, object labels, and diagnostics.", | |
| url: "https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample/resolve/main/annotation.hdf5", | |
| mediaType: "binary", | |
| hasAudio: false, | |
| details: [ | |
| { | |
| title: "Inside", | |
| text: "Caption JSON, object and interaction labels, depth/confidence arrays, camera pose and SLAM values, full-body and hand mocap, IMU, calibration, timestamps, and source metadata." | |
| }, | |
| { | |
| title: "How it relates", | |
| text: "The six MP4 streams provide synchronized video/audio. annotation.hdf5 supplies the aligned labels and non-RGB sensor timelines used to build 20-frame windows, task targets, and derived public-safe features." | |
| }, | |
| { | |
| title: "Open with", | |
| text: "Use h5py, HDFView, or HOMIE Toolkit readers. Inspect groups/datasets selectively instead of loading the whole 1.93 GB file into memory." | |
| } | |
| ] | |
| }, | |
| { | |
| name: "visualization.rrd", | |
| kind: "Rerun viewer file", | |
| bytes: 2702924036, | |
| description: "Optional Rerun visualization bundle for inspecting the episode outside the browser.", | |
| use: "Not used for training or metrics; useful for external visual inspection with Rerun 0.29.0.", | |
| url: "https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample/resolve/main/visualization.rrd", | |
| mediaType: "binary", | |
| hasAudio: false, | |
| details: [ | |
| { | |
| title: "Inside", | |
| text: "A prebuilt Rerun recording of the sample episode for visual inspection of the episode timeline and viewer state." | |
| }, | |
| { | |
| title: "How it relates", | |
| text: "It is a convenience viewer artifact beside the raw MP4 and annotation files. It helps inspect alignment, but it is not an input to the published task scores." | |
| }, | |
| { | |
| title: "Open with", | |
| text: "Download the .rrd file and open it in Rerun 0.29.0. The browser links the official source instead of embedding the 2.70 GB binary." | |
| } | |
| ] | |
| } | |
| ]; | |
| const tabSections = Array.from(document.querySelectorAll("[data-project-tab]")); | |
| const tabButtons = Array.from(document.querySelectorAll("[data-tab-key]")); | |
| const sectionTabs = document.getElementById("sectionTabs"); | |
| const sectionOrientation = document.getElementById("sectionOrientation"); | |
| const sectionConfig = [ | |
| { id: "overview", label: "Project Overview" }, | |
| { id: "dataset-card", label: "Dataset Scope" }, | |
| { id: "data-analysis", label: "Data Explorer Analysis" }, | |
| { id: "raw-sample", label: "Raw Sample Browser" }, | |
| { id: "suite", label: "20-Task Suite" }, | |
| { id: "walkthroughs", label: "Interactive Walkthrough" }, | |
| { id: "tasks", label: "Task Cards" }, | |
| { id: "protocol", label: "Evaluation Protocol" }, | |
| { id: "pipeline", label: "Pipeline" }, | |
| { id: "features", label: "Feature Blocks" }, | |
| { id: "architectures", label: "Model Architectures" }, | |
| { id: "takeaways", label: "Research Takeaways" }, | |
| { id: "models", label: "Minimal Baselines" }, | |
| { id: "neural", label: "Neural Heads" }, | |
| { id: "diagnostics", label: "Diagnostic Charts" }, | |
| { id: "directions", label: "Four Directions" }, | |
| { id: "extensions", label: "Unified Task Evidence" }, | |
| { id: "roadmap", label: "Roadmap" }, | |
| { id: "evidence", label: "Research Progress" }, | |
| { id: "reading-path", label: "Project Path" }, | |
| { id: "glossary", label: "Glossary" }, | |
| { id: "artifacts", label: "Research Artifacts" }, | |
| { id: "omni-scale-up", label: "Omni Scale-Up" }, | |
| { id: "foundation-model-paths", label: "Foundation Paths" } | |
| ]; | |
| const sectionLabels = Object.fromEntries(sectionConfig.map((section) => [section.id, section.label])); | |
| const sectionDescriptions = { | |
| overview: "Project scope, public boundaries, and entry paths.", | |
| roadmap: "Shipped stages, active work, and scale-up targets.", | |
| "reading-path": "Project path across the repo, website, and Hugging Face mirrors.", | |
| "dataset-card": "Official dataset context, public-sample boundaries, and selected-128 scope.", | |
| "data-analysis": "Public sample, selected 128 episodes, and Hugging Face gated full-dataset metadata.", | |
| "raw-sample": "Sample files, media previews, and file relationships.", | |
| suite: "Unified 20-task map, radar comparisons, task cards, and score matrix.", | |
| walkthroughs: "Case-study style task explanations.", | |
| tasks: "Integrated task-card grid inside the 20-task suite.", | |
| pipeline: "How raw episode data becomes features and results.", | |
| protocol: "Splits, leakage controls, metrics, and evaluation rules.", | |
| architectures: "Task-head and model-track organization.", | |
| features: "Modality and feature provenance.", | |
| takeaways: "Evidence lines, 180-result matrix, baseline snapshots, neural heads, and diagnostic charts.", | |
| models: "Minimal baseline evidence inside Results.", | |
| neural: "Neural MLP task-head comparisons inside Results.", | |
| directions: "How the same tasks support spatial, world-model, and VLA directions.", | |
| extensions: "Unified task provenance inside Directions.", | |
| diagnostics: "Charts and error-analysis evidence inside Results.", | |
| glossary: "Definitions for data, tasks, models, metrics, and public mirrors.", | |
| artifacts: "Files, mirrors, weights, scripts, and release checks.", | |
| evidence: "Current experiment status, model evidence, mirrors, and milestones.", | |
| "omni-scale-up": "Qwen3-Omni and Cosmos3 status.", | |
| "foundation-model-paths": "Three larger training paths after current model status." | |
| }; | |
| const sectionTabMap = Object.fromEntries(tabSections.map((section) => [section.id, section.dataset.projectTab])); | |
| ["tasks", "models", "neural", "diagnostics", "extensions"].forEach((id) => { | |
| const anchor = document.getElementById(id); | |
| const parentSection = anchor?.closest("[data-project-tab]"); | |
| if (parentSection) sectionTabMap[id] = parentSection.dataset.projectTab; | |
| }); | |
| const tabLabels = Object.fromEntries( | |
| tabButtons.map((button) => [button.dataset.tabKey, button.querySelector("strong")?.textContent?.trim() || button.dataset.tabKey]) | |
| ); | |
| const tabDefaultSections = Object.fromEntries( | |
| tabButtons.map((button) => [button.dataset.tabKey, button.dataset.defaultSection]) | |
| ); | |
| function orderedSectionsForTab(tabKey) { | |
| const configured = sectionConfig | |
| .map(({ id }) => document.getElementById(id)) | |
| .filter((section) => section?.dataset.projectTab === tabKey); | |
| const configuredIds = new Set(configured.map((section) => section.id)); | |
| const unconfigured = tabSections.filter((section) => ( | |
| section.dataset.projectTab === tabKey && !configuredIds.has(section.id) | |
| )); | |
| return [...configured, ...unconfigured]; | |
| } | |
| function labelForSection(section) { | |
| return sectionLabels[section.id] || | |
| section.querySelector("h2")?.textContent?.replace(/\.$/, "") || | |
| section.id.replaceAll("-", " "); | |
| } | |
| function updateSectionOrientation(tabKey, sectionId) { | |
| if (!sectionOrientation) return; | |
| const tabLabel = tabLabels[tabKey] || tabKey; | |
| const sectionLabel = sectionLabels[sectionId] || labelForSection(document.getElementById(sectionId)); | |
| const description = sectionDescriptions[sectionId] || "Evidence, files, and source links for this part of the suite."; | |
| sectionOrientation.innerHTML = ` | |
| <strong>${tabLabel} / ${sectionLabel}</strong> | |
| <span>${description}</span> | |
| <a href="#reader-map">Evidence map</a> | |
| `; | |
| } | |
| function renderSectionTabs(tabKey, activeSectionId) { | |
| if (!sectionTabs) return; | |
| const sections = orderedSectionsForTab(tabKey); | |
| sectionTabs.replaceChildren(); | |
| sections.forEach((section, index) => { | |
| const button = document.createElement("button"); | |
| const active = section.id === activeSectionId; | |
| button.type = "button"; | |
| button.className = `section-tab${active ? " active" : ""}`; | |
| button.id = `section-tab-${section.id}`; | |
| button.dataset.sectionTarget = section.id; | |
| button.setAttribute("role", "tab"); | |
| button.setAttribute("aria-controls", section.id); | |
| button.setAttribute("aria-selected", active ? "true" : "false"); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| button.tabIndex = active ? 0 : -1; | |
| button.textContent = labelForSection(section); | |
| button.addEventListener("click", () => { | |
| setProjectTab(tabKey, { | |
| targetId: section.id, | |
| pushHash: true, | |
| scroll: true, | |
| smooth: true | |
| }); | |
| }); | |
| button.addEventListener("keydown", (event) => { | |
| if (!["ArrowRight", "ArrowDown", "ArrowLeft", "ArrowUp", "Home", "End"].includes(event.key)) return; | |
| event.preventDefault(); | |
| const lastIndex = sections.length - 1; | |
| let nextIndex = index; | |
| if (event.key === "ArrowRight" || event.key === "ArrowDown") nextIndex = index === lastIndex ? 0 : index + 1; | |
| if (event.key === "ArrowLeft" || event.key === "ArrowUp") nextIndex = index === 0 ? lastIndex : index - 1; | |
| if (event.key === "Home") nextIndex = 0; | |
| if (event.key === "End") nextIndex = lastIndex; | |
| const nextSection = sections[nextIndex]; | |
| const nextButton = sectionTabs.querySelector(`[data-section-target="${nextSection.id}"]`); | |
| nextButton?.focus(); | |
| setProjectTab(tabKey, { | |
| targetId: nextSection.id, | |
| pushHash: true, | |
| scroll: true, | |
| smooth: true | |
| }); | |
| }); | |
| sectionTabs.appendChild(button); | |
| }); | |
| } | |
| function setProjectTab(tabKey, options = {}) { | |
| const fallbackTab = tabDefaultSections[tabKey] ? tabKey : "start"; | |
| const requestedId = options.targetId || tabDefaultSections[fallbackTab] || "overview"; | |
| const targetId = document.getElementById(requestedId) | |
| ? requestedId | |
| : tabDefaultSections[fallbackTab] || "overview"; | |
| document.getElementById("main").dataset.activeTab = fallbackTab; | |
| document.getElementById("main").dataset.activeSection = targetId; | |
| tabSections.forEach((section) => { | |
| section.hidden = false; | |
| }); | |
| tabButtons.forEach((button) => { | |
| const active = button.dataset.tabKey === fallbackTab; | |
| button.classList.toggle("active", active); | |
| button.setAttribute("aria-selected", active ? "true" : "false"); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| button.tabIndex = active ? 0 : -1; | |
| }); | |
| renderSectionTabs(fallbackTab, targetId); | |
| updateSectionOrientation(fallbackTab, targetId); | |
| if (options.pushHash) { | |
| history.pushState(null, "", `#${targetId}`); | |
| } | |
| if (options.scroll) { | |
| requestAnimationFrame(() => { | |
| document.getElementById(targetId)?.scrollIntoView({ | |
| behavior: options.smooth ? "smooth" : "auto", | |
| block: "start" | |
| }); | |
| }); | |
| } | |
| } | |
| function activateTabForHash(options = {}) { | |
| const hashId = decodeURIComponent(window.location.hash.replace(/^#/, "")); | |
| if (hashId && !sectionTabMap[hashId]) return; | |
| const tabKey = sectionTabMap[hashId] || "start"; | |
| const targetId = sectionTabMap[hashId] ? hashId : tabDefaultSections[tabKey]; | |
| setProjectTab(tabKey, { targetId, scroll: options.scroll, smooth: options.smooth }); | |
| } | |
| function moveProjectTabFocus(currentIndex, key) { | |
| const lastIndex = tabButtons.length - 1; | |
| let nextIndex = currentIndex; | |
| if (key === "ArrowRight" || key === "ArrowDown") nextIndex = currentIndex === lastIndex ? 0 : currentIndex + 1; | |
| if (key === "ArrowLeft" || key === "ArrowUp") nextIndex = currentIndex === 0 ? lastIndex : currentIndex - 1; | |
| if (key === "Home") nextIndex = 0; | |
| if (key === "End") nextIndex = lastIndex; | |
| const nextButton = tabButtons[nextIndex]; | |
| nextButton.focus(); | |
| setProjectTab(nextButton.dataset.tabKey, { | |
| targetId: nextButton.dataset.defaultSection, | |
| pushHash: true, | |
| scroll: true, | |
| smooth: true | |
| }); | |
| } | |
| tabButtons.forEach((button, index) => { | |
| button.addEventListener("click", () => { | |
| setProjectTab(button.dataset.tabKey, { | |
| targetId: button.dataset.defaultSection, | |
| pushHash: true, | |
| scroll: true, | |
| smooth: true | |
| }); | |
| }); | |
| button.addEventListener("keydown", (event) => { | |
| if (!["ArrowRight", "ArrowDown", "ArrowLeft", "ArrowUp", "Home", "End"].includes(event.key)) return; | |
| event.preventDefault(); | |
| moveProjectTabFocus(index, event.key); | |
| }); | |
| }); | |
| window.addEventListener("hashchange", () => activateTabForHash({ scroll: true })); | |
| activateTabForHash({ scroll: Boolean(window.location.hash) }); | |
| function initContentTabs() { | |
| const panelActivators = new Map(); | |
| document.querySelectorAll(".content-tabs").forEach((tablist) => { | |
| const buttons = Array.from(tablist.querySelectorAll("[data-panel-target]")); | |
| if (!buttons.length) return; | |
| const activatePanel = (activeButton, options = {}) => { | |
| buttons.forEach((button) => { | |
| const active = button === activeButton; | |
| const panel = document.getElementById(button.dataset.panelTarget); | |
| button.classList.toggle("active", active); | |
| button.setAttribute("aria-selected", active ? "true" : "false"); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| button.tabIndex = active ? 0 : -1; | |
| if (panel) panel.hidden = !active; | |
| }); | |
| if (options.focus) activeButton.focus(); | |
| if (options.hash) history.pushState(null, "", `#${activeButton.dataset.panelTarget}`); | |
| if (options.scroll) { | |
| requestAnimationFrame(() => { | |
| document.getElementById(activeButton.dataset.panelTarget)?.scrollIntoView({ | |
| behavior: options.smooth ? "smooth" : "auto", | |
| block: "start" | |
| }); | |
| }); | |
| } | |
| }; | |
| buttons.forEach((button, index) => { | |
| panelActivators.set(button.dataset.panelTarget, (options = {}) => activatePanel(button, options)); | |
| button.addEventListener("click", () => activatePanel(button)); | |
| button.addEventListener("keydown", (event) => { | |
| if (!["ArrowRight", "ArrowDown", "ArrowLeft", "ArrowUp", "Home", "End"].includes(event.key)) return; | |
| event.preventDefault(); | |
| const lastIndex = buttons.length - 1; | |
| let nextIndex = index; | |
| if (event.key === "ArrowRight" || event.key === "ArrowDown") nextIndex = index === lastIndex ? 0 : index + 1; | |
| if (event.key === "ArrowLeft" || event.key === "ArrowUp") nextIndex = index === 0 ? lastIndex : index - 1; | |
| if (event.key === "Home") nextIndex = 0; | |
| if (event.key === "End") nextIndex = lastIndex; | |
| activatePanel(buttons[nextIndex], { focus: true }); | |
| }); | |
| }); | |
| activatePanel(buttons.find((button) => button.classList.contains("active")) || buttons[0]); | |
| }); | |
| function activatePanelFromHash(hashId, options = {}) { | |
| const activatePanel = panelActivators.get(hashId); | |
| if (!activatePanel) return false; | |
| setProjectTab("resources", { targetId: "artifacts", scroll: false }); | |
| activatePanel(options); | |
| return true; | |
| } | |
| document.querySelectorAll('a[href^="#artifact-panel-"]').forEach((link) => { | |
| link.addEventListener("click", (event) => { | |
| const targetId = link.getAttribute("href").slice(1); | |
| if (!activatePanelFromHash(targetId, { hash: true, scroll: true, smooth: true })) return; | |
| event.preventDefault(); | |
| }); | |
| }); | |
| const initialHash = decodeURIComponent(window.location.hash.replace(/^#/, "")); | |
| if (initialHash) activatePanelFromHash(initialHash, { scroll: Boolean(window.location.hash) }); | |
| window.addEventListener("hashchange", () => { | |
| const hashId = decodeURIComponent(window.location.hash.replace(/^#/, "")); | |
| if (hashId) activatePanelFromHash(hashId, { scroll: true }); | |
| }); | |
| } | |
| initContentTabs(); | |
| function formatBytes(bytes) { | |
| if (!Number.isFinite(Number(bytes))) return "unknown size"; | |
| const units = ["B", "KB", "MB", "GB"]; | |
| let value = Number(bytes); | |
| let unitIndex = 0; | |
| while (value >= 1000 && unitIndex < units.length - 1) { | |
| value /= 1000; | |
| unitIndex += 1; | |
| } | |
| return `${value.toFixed(unitIndex === 0 ? 0 : 2)} ${units[unitIndex]}`; | |
| } | |
| function setRawSampleFile(file) { | |
| const video = document.getElementById("rawVideo"); | |
| const audio = document.getElementById("rawAudio"); | |
| const videoFrame = document.getElementById("rawVideoFrame"); | |
| const audioFrame = document.getElementById("rawAudioFrame"); | |
| const sourceLink = document.getElementById("rawOpenSource"); | |
| const previewLink = document.getElementById("rawOpenPreview"); | |
| const previewNote = document.getElementById("rawPreviewNote"); | |
| const detailMap = document.getElementById("rawFileDetails"); | |
| const mediaUrl = file.previewUrl || file.url; | |
| document.getElementById("rawFileTitle").textContent = file.name; | |
| document.getElementById("rawFileDescription").textContent = file.description; | |
| document.getElementById("rawKindPill").textContent = `${file.kind} · ${formatBytes(file.bytes)}`; | |
| document.getElementById("rawFileUse").textContent = file.use; | |
| sourceLink.href = file.url; | |
| sourceLink.textContent = file.mediaType === "video" ? "open full raw source" : "open/download source"; | |
| if (file.previewUrl) { | |
| previewLink.hidden = false; | |
| previewLink.href = file.previewUrl; | |
| previewLink.textContent = `open playable preview (${formatBytes(file.previewBytes)})`; | |
| previewNote.hidden = false; | |
| previewNote.textContent = file.previewNote; | |
| } else { | |
| previewLink.hidden = true; | |
| previewNote.hidden = true; | |
| } | |
| if (file.mediaType === "video") { | |
| videoFrame.hidden = false; | |
| video.poster = file.posterUrl || "assets/modalities/video.jpg"; | |
| if (video.querySelector("source").getAttribute("src") !== mediaUrl) { | |
| video.pause(); | |
| video.querySelector("source").src = mediaUrl; | |
| video.load(); | |
| } | |
| } else { | |
| video.pause(); | |
| videoFrame.hidden = true; | |
| } | |
| if (file.hasAudio) { | |
| audioFrame.hidden = false; | |
| if (audio.querySelector("source").getAttribute("src") !== mediaUrl) { | |
| audio.pause(); | |
| audio.querySelector("source").src = mediaUrl; | |
| audio.load(); | |
| } | |
| } else { | |
| audio.pause(); | |
| audioFrame.hidden = true; | |
| } | |
| if (detailMap) { | |
| detailMap.replaceChildren(); | |
| if (Array.isArray(file.details) && file.details.length) { | |
| file.details.forEach((detail) => { | |
| const card = document.createElement("article"); | |
| const title = document.createElement("strong"); | |
| const text = document.createElement("span"); | |
| title.textContent = detail.title; | |
| text.textContent = detail.text; | |
| card.append(title, text); | |
| detailMap.appendChild(card); | |
| }); | |
| detailMap.hidden = false; | |
| } else { | |
| detailMap.hidden = true; | |
| } | |
| } | |
| document.querySelectorAll("[data-raw-file]").forEach((button) => { | |
| const active = button.dataset.rawFile === file.name; | |
| button.classList.toggle("active", active); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| }); | |
| } | |
| function initRawSampleBrowser() { | |
| const buttons = Array.from(document.querySelectorAll("[data-raw-file]")); | |
| if (!buttons.length) return; | |
| buttons.forEach((button) => { | |
| button.addEventListener("click", () => { | |
| const file = rawSampleFiles.find((item) => item.name === button.dataset.rawFile); | |
| if (file) setRawSampleFile(file); | |
| }); | |
| }); | |
| setRawSampleFile(rawSampleFiles[0]); | |
| } | |
| initRawSampleBrowser(); | |
| const escapeHtml = (value) => String(value ?? "") | |
| .replaceAll("&", "&") | |
| .replaceAll("<", "<") | |
| .replaceAll(">", ">") | |
| .replaceAll('"', """) | |
| .replaceAll("'", "'"); | |
| const formatMetric = (value) => { | |
| if (value === null || value === undefined || Number.isNaN(Number(value))) return "n/a"; | |
| const numeric = Number(value); | |
| if (Math.abs(numeric) >= 10) return numeric.toFixed(2); | |
| return numeric.toFixed(4); | |
| }; | |
| const resultSourceUrl = (source) => { | |
| if (!source) return "data/task_method_20_result_matrix.json"; | |
| if (/^https?:\/\//.test(source)) return source; | |
| return `https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/${source}`; | |
| }; | |
| const evidenceLineLabel = (series) => { | |
| const scope = `${series.scope || ""} ${series.kind || ""}`.toLowerCase(); | |
| return scope.includes("1 public") || scope.includes("single") | |
| ? "1 sample episode" | |
| : "128 selected episodes"; | |
| }; | |
| let glossaryTermMap = new Map(); | |
| let activeGlossaryFilter = "all"; | |
| const fallbackTermDefinitions = { | |
| "direct score": "A metric computed against the task target directly. This is the preferred score type in the 20-task matrix.", | |
| "direct": "A metric computed against the task target directly. This is the preferred score type in the 20-task matrix.", | |
| "compact-proxy score": "A bounded proxy metric when a direct raw target is not publicly available. It stays explicit so proxy targets are not mistaken for direct raw-target reads.", | |
| "proxy": "A bounded proxy metric when a direct raw target is not publicly available. It stays explicit so proxy targets are not mistaken for direct raw-target reads.", | |
| "raw metric value": "The original metric value emitted by the runner or verified package. This is the value to cite.", | |
| "normalized radar value": "A 0-1 plotting value used only to draw comparable radar polygons across metrics with different scales.", | |
| "task-method record": "One method evaluated on one task. 9 methods x 20 tasks gives 180 public result records.", | |
| "unified 20-task suite": "The current task surface: all 20 task contracts presented together and scored across methods where real artifacts exist.", | |
| "evidence line": "A reading lane for a group of results: Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison.", | |
| "public sample episode": "The fully inspectable Line 1 unit used for raw-file browsing, task construction, and single-episode baselines.", | |
| "selected 128 episodes": "Line 2 uses derived windows/features and keeps links back to official gated episode ids and source paths.", | |
| "minimal baseline": "A simple non-neural task head used as a low-complexity comparison on the public sample episode.", | |
| "minimal": "A simple non-neural task head used as a low-complexity comparison on the public sample episode.", | |
| "minimum": "A simple non-neural task head used as a low-complexity comparison on the public sample episode.", | |
| "simple baseline": "A non-neural selected-128 baseline family used before NN and foundation-model rows.", | |
| "simple": "A non-neural selected-128 baseline family used before NN and foundation-model rows.", | |
| "neural mlp": "A compact PyTorch MLP task head used for single-episode and selected-128 baseline comparisons.", | |
| "nn": "A compact PyTorch MLP task head used for single-episode and selected-128 baseline comparisons.", | |
| "metadata baseline": "A selected-128 baseline using metadata/text-derived public-safe features.", | |
| "raw-feature baseline": "A selected-128 baseline using exported public-safe raw-feature groups, not raw gated media.", | |
| "qwen3-omni": "The multimodal foundation-model family used for the Qwen branch; v6 is the current public 20-task row.", | |
| "qwen v1-v6": "The Qwen3-Omni run lineage: v1-v4 are earlier evidence, v5 is the prior pinned release, and v6 is current.", | |
| "cosmos3-super": "The larger Cosmos3-style branch tracked as Reasoner diagnostics and a separate forward-dynamics LoRA branch.", | |
| "cosmos3-nano": "A smaller Cosmos3 compatibility/future-window branch used for the Nano Future Window row.", | |
| "lora adapter": "A lightweight trainable adapter-weight package, published only when verified and public-safe.", | |
| "method row": "One named method family in the matrix, such as Minimal, 128ep Raw NN, Qwen3-Omni v6, or Cosmos3-Super." | |
| }; | |
| const normalizeTermKey = (term) => String(term || "") | |
| .trim() | |
| .toLowerCase() | |
| .replace(/[–—]/g, "-") | |
| .replace(/\s+/g, " "); | |
| function registerGlossaryTerms(payload) { | |
| glossaryTermMap = new Map(); | |
| (payload?.entries || []).forEach((entry) => { | |
| glossaryTermMap.set(normalizeTermKey(entry.term), entry); | |
| }); | |
| } | |
| function renderGlossaryTable(payload) { | |
| const tableBody = document.getElementById("glossaryRows"); | |
| if (!tableBody || !Array.isArray(payload?.entries)) return; | |
| const categoryLabels = new Map((payload.categories || []).map((category) => [category.id, category.label])); | |
| tableBody.innerHTML = payload.entries.map((entry) => { | |
| const category = categoryLabels.get(entry.category) || entry.category || "Glossary"; | |
| const searchable = [ | |
| entry.term, | |
| category, | |
| entry.plain_meaning, | |
| entry.project_usage, | |
| entry.do_not_confuse_with | |
| ].filter(Boolean).join(" "); | |
| return ` | |
| <tr data-glossary-category="${escapeHtml(entry.category || "other")}" data-glossary-search="${escapeHtml(searchable.toLowerCase())}"> | |
| <td>${escapeHtml(entry.term)}<small>${escapeHtml(category)}</small></td> | |
| <td>${escapeHtml(entry.plain_meaning || "")}</td> | |
| <td>${escapeHtml(entry.project_usage || "")}</td> | |
| <td>${escapeHtml(entry.do_not_confuse_with || "")}</td> | |
| </tr> | |
| `; | |
| }).join(""); | |
| labelResponsiveTables(); | |
| applyGlossaryFilter(); | |
| } | |
| function glossaryCategoryMatches(category, filter) { | |
| const value = String(category || "").toLowerCase(); | |
| if (filter === "all") return true; | |
| if (filter === "data") return value.includes("data") || value.includes("scope") || value.includes("file") || value.includes("feature") || value.includes("multimodal") || value.includes("sensing") || value.includes("spatial") || value.includes("temporal"); | |
| if (filter === "tasks") return value.includes("task") || value.includes("suite") || value.includes("window"); | |
| if (filter === "metrics") return value.includes("metric") || value.includes("evaluation") || value.includes("score"); | |
| if (filter === "models") return value.includes("model") || value.includes("training") || value.includes("adapter") || value.includes("qwen") || value.includes("cosmos"); | |
| if (filter === "robotics") return value.includes("robot") || value.includes("vla") || value.includes("policy") || value.includes("action"); | |
| if (filter === "public") return value.includes("public") || value.includes("artifact") || value.includes("mirror") || value.includes("hugging") || value.includes("repo"); | |
| return value.includes(filter); | |
| } | |
| function applyGlossaryFilter() { | |
| const rows = Array.from(document.querySelectorAll("#glossaryRows tr")); | |
| if (!rows.length) return; | |
| const query = String(document.getElementById("glossarySearch")?.value || "").trim().toLowerCase(); | |
| let visible = 0; | |
| rows.forEach((row) => { | |
| const category = row.dataset.glossaryCategory || ""; | |
| const text = row.dataset.glossarySearch || row.textContent.toLowerCase(); | |
| const show = glossaryCategoryMatches(category, activeGlossaryFilter) && (!query || text.includes(query)); | |
| row.hidden = !show; | |
| if (show) visible += 1; | |
| }); | |
| const count = document.getElementById("glossaryCount"); | |
| if (count) count.textContent = `${visible} of ${rows.length} terms shown`; | |
| } | |
| function initGlossaryControls() { | |
| const search = document.getElementById("glossarySearch"); | |
| if (search) search.addEventListener("input", applyGlossaryFilter); | |
| document.querySelectorAll(".glossary-filter").forEach((button) => { | |
| button.addEventListener("click", () => { | |
| activeGlossaryFilter = button.dataset.glossaryFilter || "all"; | |
| document.querySelectorAll(".glossary-filter").forEach((candidate) => { | |
| const active = candidate === button; | |
| candidate.classList.toggle("active", active); | |
| candidate.setAttribute("aria-pressed", active ? "true" : "false"); | |
| }); | |
| applyGlossaryFilter(); | |
| }); | |
| }); | |
| } | |
| let glossaryDataPromise = null; | |
| async function loadGlossaryData() { | |
| if (!glossaryDataPromise) { | |
| glossaryDataPromise = fetch("data/glossary.json", { cache: "no-cache" }) | |
| .then((response) => { | |
| if (!response.ok) throw new Error(`glossary ${response.status}`); | |
| return response.json(); | |
| }); | |
| } | |
| const payload = await glossaryDataPromise; | |
| registerGlossaryTerms(payload); | |
| renderGlossaryTable(payload); | |
| return payload; | |
| } | |
| async function initGlossaryData() { | |
| try { | |
| await loadGlossaryData(); | |
| } catch (error) { | |
| // Keep the static fallback rows if the structured glossary is unavailable. | |
| } | |
| } | |
| function termDefinition(term) { | |
| const key = normalizeTermKey(term); | |
| const entry = glossaryTermMap.get(key); | |
| if (entry) return [entry.plain_meaning, entry.project_usage].filter(Boolean).join(" "); | |
| return fallbackTermDefinitions[key] || ""; | |
| } | |
| function infoIcon(title, body) { | |
| if (!body) return ""; | |
| return ` | |
| <span class="term-help" tabindex="0" aria-label="${escapeHtml(title)}: ${escapeHtml(body)}" title="${escapeHtml(body)}">i | |
| <span class="term-tooltip" role="tooltip">${escapeHtml(body)}</span> | |
| </span> | |
| `; | |
| } | |
| function termHelp(term, label = term) { | |
| return `<span class="term-with-help"><span>${escapeHtml(label)}</span>${infoIcon(term, termDefinition(term))}</span>`; | |
| } | |
| function initTermTooltipPortal() { | |
| const tooltip = document.createElement("div"); | |
| tooltip.className = "term-floating-tooltip"; | |
| tooltip.id = "termFloatingTooltip"; | |
| tooltip.setAttribute("role", "tooltip"); | |
| tooltip.setAttribute("aria-hidden", "true"); | |
| document.body.appendChild(tooltip); | |
| document.body.classList.add("tooltip-portal-ready"); | |
| let activeHelp = null; | |
| const tooltipText = (help) => { | |
| const inlineTooltip = help?.querySelector(".term-tooltip"); | |
| const text = inlineTooltip?.textContent || help?.getAttribute("title") || help?.getAttribute("aria-label") || ""; | |
| return text.replace(/\s+/g, " ").trim(); | |
| }; | |
| const positionTooltip = () => { | |
| if (!activeHelp) return; | |
| const text = tooltipText(activeHelp); | |
| if (!text) return; | |
| tooltip.textContent = text; | |
| tooltip.setAttribute("aria-hidden", "false"); | |
| tooltip.classList.add("show"); | |
| const helpRect = activeHelp.getBoundingClientRect(); | |
| const tooltipRect = tooltip.getBoundingClientRect(); | |
| const gap = 10; | |
| const margin = 12; | |
| let x = helpRect.left + (helpRect.width / 2) - (tooltipRect.width / 2); | |
| x = Math.max(margin, Math.min(x, window.innerWidth - tooltipRect.width - margin)); | |
| let y = helpRect.top - tooltipRect.height - gap; | |
| if (y < margin) y = helpRect.bottom + gap; | |
| y = Math.max(margin, Math.min(y, window.innerHeight - tooltipRect.height - margin)); | |
| tooltip.style.setProperty("--tooltip-x", `${Math.round(x)}px`); | |
| tooltip.style.setProperty("--tooltip-y", `${Math.round(y)}px`); | |
| }; | |
| const showTooltip = (help) => { | |
| if (!help || !tooltipText(help)) return; | |
| activeHelp = help; | |
| help.setAttribute("aria-describedby", tooltip.id); | |
| positionTooltip(); | |
| }; | |
| const hideTooltip = (help = activeHelp) => { | |
| if (help) help.removeAttribute("aria-describedby"); | |
| if (help && activeHelp && help !== activeHelp) return; | |
| activeHelp = null; | |
| tooltip.classList.remove("show"); | |
| tooltip.setAttribute("aria-hidden", "true"); | |
| }; | |
| document.addEventListener("pointerover", (event) => { | |
| const help = event.target.closest?.(".term-help"); | |
| if (!help || help.contains(event.relatedTarget)) return; | |
| showTooltip(help); | |
| }); | |
| document.addEventListener("pointerout", (event) => { | |
| const help = event.target.closest?.(".term-help"); | |
| if (!help || help.contains(event.relatedTarget)) return; | |
| hideTooltip(help); | |
| }); | |
| document.addEventListener("focusin", (event) => { | |
| const help = event.target.closest?.(".term-help"); | |
| if (help) showTooltip(help); | |
| }); | |
| document.addEventListener("focusout", (event) => { | |
| const help = event.target.closest?.(".term-help"); | |
| if (help) hideTooltip(help); | |
| }); | |
| document.addEventListener("keydown", (event) => { | |
| if (event.key === "Escape") hideTooltip(); | |
| }); | |
| window.addEventListener("scroll", positionTooltip, { passive: true }); | |
| window.addEventListener("resize", positionTooltip); | |
| } | |
| function methodGlossaryTerms(method) { | |
| const text = `${method.id || ""} ${method.label || ""} ${method.short_label || ""} ${method.method_detail || ""}`.toLowerCase(); | |
| const terms = []; | |
| if (text.includes("minimal")) terms.push("Minimal baseline"); | |
| if (text.includes("simple")) terms.push("Simple baseline"); | |
| if (text.includes("neural") || text.includes("mlp") || text.includes(" nn")) terms.push("Neural MLP"); | |
| if (text.includes("metadata")) terms.push("Metadata baseline"); | |
| if (text.includes("raw")) terms.push("Raw-feature baseline"); | |
| if (text.includes("qwen3") || text.includes("qwen")) terms.push("Qwen3-Omni"); | |
| if (text.includes("lora")) terms.push("LoRA adapter"); | |
| if (text.includes("cosmos3-super")) terms.push("Cosmos3-Super"); | |
| if (text.includes("cosmos3-nano")) terms.push("Cosmos3-Nano"); | |
| return Array.from(new Set(terms)); | |
| } | |
| function methodInfo(method) { | |
| const direct = Number(method.scored_task_count || 0) - Number(method.proxy_scored_task_count || 0); | |
| const glossaryNotes = methodGlossaryTerms(method) | |
| .map((term) => { | |
| const body = termDefinition(term); | |
| return body ? `${term}: ${body}` : ""; | |
| }) | |
| .filter(Boolean) | |
| .join(" "); | |
| return [ | |
| method.method_detail || method.scope || "", | |
| glossaryNotes, | |
| `Evidence line: ${evidenceLineLabel(method)}.`, | |
| `Records: ${method.result_record_count || 0}/20; direct ${direct}; proxy ${method.proxy_scored_task_count || 0}.` | |
| ].filter(Boolean).join(" "); | |
| } | |
| function methodLabelWithHelp(method) { | |
| return `<span class="term-with-help"><span>${escapeHtml(method.label)}</span>${infoIcon(method.label, methodInfo(method))}</span>`; | |
| } | |
| function renderResultMatrixStats(payload, records) { | |
| const stats = document.getElementById("resultMatrixStats"); | |
| if (!stats) return; | |
| const direct = records.filter((record) => record.scored && !record.proxy_scored).length; | |
| const proxy = records.filter((record) => record.proxy_scored).length; | |
| const items = [ | |
| [payload.method_task_record_count ?? records.length, "method-task records", "Task-method record"], | |
| [payload.method_count ?? 9, "method rows", "Method row"], | |
| [payload.task_count ?? 20, "task columns", "Unified 20-task suite"], | |
| [direct, "direct scores", "Direct score"], | |
| [proxy, "compact-proxy scores", "Compact-proxy score"] | |
| ]; | |
| stats.innerHTML = items.map(([value, label, term]) => ` | |
| <div class="result-matrix-stat"><strong>${escapeHtml(value)}</strong>${termHelp(term, label)}</div> | |
| `).join(""); | |
| } | |
| function renderResultMethodSummary(payload) { | |
| const table = document.getElementById("resultMethodSummary"); | |
| if (!table) return; | |
| const series = payload.series || []; | |
| table.querySelector("tbody").innerHTML = series.map((method) => { | |
| const direct = Number(method.scored_task_count || 0) - Number(method.proxy_scored_task_count || 0); | |
| return ` | |
| <tr> | |
| <td><span class="method-cell"><strong>${methodLabelWithHelp(method)}</strong><span>${escapeHtml(method.short_label || method.id)}</span></span></td> | |
| <td>${escapeHtml(evidenceLineLabel(method))}</td> | |
| <td>${escapeHtml(method.result_record_count)} / 20</td> | |
| <td>${escapeHtml(direct)}</td> | |
| <td>${escapeHtml(method.proxy_scored_task_count || 0)}</td> | |
| <td>${escapeHtml(method.scope || method.method_detail || "")}</td> | |
| </tr> | |
| `; | |
| }).join(""); | |
| } | |
| function renderResultScoreTable(payload) { | |
| const table = document.getElementById("resultScoreTable"); | |
| if (!table) return; | |
| const records = payload.records || payload.task_method_result_matrix || []; | |
| const series = payload.series || []; | |
| const taskMap = new Map(); | |
| records.forEach((record) => { | |
| if (!taskMap.has(record.task_number)) { | |
| taskMap.set(record.task_number, { | |
| task_number: record.task_number, | |
| task_id: record.task_id, | |
| task_label: record.task_label | |
| }); | |
| } | |
| }); | |
| const tasks = Array.from(taskMap.values()).sort((a, b) => Number(a.task_number) - Number(b.task_number)); | |
| const recordByKey = new Map(records.map((record) => [`${record.series_id}:${record.task_number}`, record])); | |
| renderResultMatrixStats(payload, records); | |
| renderResultMethodSummary(payload); | |
| table.querySelector("thead").innerHTML = ` | |
| <tr> | |
| <th>${termHelp("Method row", "Method")}</th> | |
| ${tasks.map((task) => ` | |
| <th title="${escapeHtml(task.task_label)}"> | |
| <span class="task-header"> | |
| <strong>${String(task.task_number).padStart(2, "0")}</strong> | |
| <span>${escapeHtml(task.task_label)}</span> | |
| </span> | |
| </th> | |
| `).join("")} | |
| </tr> | |
| `; | |
| table.querySelector("tbody").innerHTML = series.map((method) => { | |
| const line = evidenceLineLabel(method); | |
| const cells = tasks.map((task) => { | |
| const record = recordByKey.get(`${method.id}:${task.task_number}`); | |
| if (!record) return "<td>n/a</td>"; | |
| const rawValue = record.raw_text || formatMetric(record.raw); | |
| const normalized = record.normalized_score === null || record.normalized_score === undefined | |
| ? "n/a" | |
| : Number(record.normalized_score).toFixed(3); | |
| const proxy = Boolean(record.proxy_scored); | |
| const status = proxy ? "proxy" : "direct"; | |
| const color = method.color || "#ccffa0"; | |
| const statusTerm = proxy ? "Compact-proxy score" : "Direct score"; | |
| const statusHelp = termDefinition(statusTerm); | |
| const cellLabel = `${method.label}, task ${String(task.task_number).padStart(2, "0")} ${task.task_label}: ${status} score, raw ${rawValue}, normalized ${normalized}, metric ${record.metric_key || "metric"}.`; | |
| return ` | |
| <td> | |
| <a class="score-source-link" href="${escapeHtml(resultSourceUrl(record.source))}" title="${escapeHtml(record.source || "matrix source")}"> | |
| <span class="score-chip ${proxy ? "proxy" : "direct"}" style="--score-color:${escapeHtml(color)}" aria-label="${escapeHtml(cellLabel)}" title="${escapeHtml(statusHelp)}"> | |
| <strong>${escapeHtml(rawValue)}</strong> | |
| <span>norm ${escapeHtml(normalized)}<br>${escapeHtml(record.metric_key || "metric")}</span> | |
| <em title="${escapeHtml(statusHelp)}">${status}</em> | |
| </span> | |
| </a> | |
| </td> | |
| `; | |
| }).join(""); | |
| return ` | |
| <tr> | |
| <td> | |
| <span class="method-cell"> | |
| <strong>${methodLabelWithHelp(method)}</strong> | |
| <span>${escapeHtml(method.short_label || method.id)} · ${escapeHtml(line)}</span> | |
| </span> | |
| </td> | |
| ${cells} | |
| </tr> | |
| `; | |
| }).join(""); | |
| } | |
| async function initResultMatrixTable() { | |
| const table = document.getElementById("resultScoreTable"); | |
| if (!table) return; | |
| try { | |
| const [response] = await Promise.all([ | |
| fetch("data/task_method_20_result_matrix.json", { cache: "no-cache" }), | |
| loadGlossaryData().catch(() => null) | |
| ]); | |
| if (!response.ok) throw new Error(`matrix ${response.status}`); | |
| renderResultScoreTable(await response.json()); | |
| } catch (error) { | |
| table.querySelector("tbody").innerHTML = '<tr><td colspan="2">Result matrix could not be loaded. Use the source link above for the complete evidence table.</td></tr>'; | |
| } | |
| } | |
| const prepareTaskResultLookup = (payload) => { | |
| taskResultSeries = Array.isArray(payload?.series) ? payload.series : []; | |
| taskResultRecordsByNumber = new Map(); | |
| const records = Array.isArray(payload?.records) ? payload.records : []; | |
| records.forEach((record) => { | |
| const taskNumber = Number(record.task_number); | |
| if (!Number.isFinite(taskNumber)) return; | |
| if (!taskResultRecordsByNumber.has(taskNumber)) taskResultRecordsByNumber.set(taskNumber, new Map()); | |
| taskResultRecordsByNumber.get(taskNumber).set(record.series_id, record); | |
| }); | |
| }; | |
| const scoreWidthFromNormalized = (record) => { | |
| const normalized = Number(record?.normalized_score); | |
| if (!Number.isFinite(normalized)) return 6; | |
| return Math.max(6, Math.min(100, normalized * 100)); | |
| }; | |
| const compactMetricLabel = (metricKey, fallback = "metric") => { | |
| const key = String(metricKey || fallback || "metric").toLowerCase(); | |
| if (key.includes("macro_f1") || key.includes("macro-f1")) return "macro-F1"; | |
| if (key.includes("micro_f1") || key.includes("micro-f1")) return "micro-F1"; | |
| if (key.includes("f1")) return "F1"; | |
| if (key.includes("mpjpe")) return "MPJPE"; | |
| if (key.includes("mrr")) return "MRR"; | |
| if (key === "r2" || key.includes("_r2") || key.includes(" r2")) return "R2"; | |
| if (key.includes("mae")) return "MAE"; | |
| if (key.includes("rmse")) return "RMSE"; | |
| if (key.includes("quality")) return "quality"; | |
| if (key.includes("accuracy") || key.includes("acc")) return "accuracy"; | |
| if (key.includes("json")) return "JSON"; | |
| return String(metricKey || fallback || "metric").replaceAll("_", " "); | |
| }; | |
| const taskMethodResults = (task, index) => { | |
| const taskNumber = Number(task.task_number || index + 1); | |
| const recordsBySeries = taskResultRecordsByNumber.get(taskNumber) || new Map(); | |
| return taskResultSeries.map((series) => ({ | |
| series, | |
| record: recordsBySeries.get(series.id) || null | |
| })); | |
| }; | |
| const renderTaskMethodResults = (task, index) => { | |
| const results = taskMethodResults(task, index); | |
| if (!results.length) { | |
| return ` | |
| <div class="task-results-panel"> | |
| <div class="task-results-head"><strong>Method results</strong><em>Open the 180-record table for scores</em></div> | |
| </div> | |
| `; | |
| } | |
| const metricName = task.metric?.name || "primary metric"; | |
| const cells = results.map(({ series, record }) => { | |
| const color = series.color || "#ccffa0"; | |
| if (!record) { | |
| return ` | |
| <span class="task-method-score missing" style="--score-color:${escapeHtml(color)};--score-width:6%" title="${escapeHtml(series.label)} has no record for this task."> | |
| <span class="task-method-top"><strong>${escapeHtml(series.short_label || series.label || series.id)}</strong><em>n/a</em></span> | |
| <span class="task-method-value">n/a</span> | |
| <span class="task-method-meta">no record</span> | |
| </span> | |
| `; | |
| } | |
| const rawValue = record.raw_text || formatMetric(record.raw); | |
| const metricLabel = compactMetricLabel(record.metric_key, metricName); | |
| const normalized = record.normalized_score === null || record.normalized_score === undefined | |
| ? "n/a" | |
| : Number(record.normalized_score).toFixed(3); | |
| const status = record.proxy_scored ? "proxy" : "direct"; | |
| const scoreWidth = scoreWidthFromNormalized(record); | |
| const title = `${series.label}: raw ${rawValue}, normalized radar value ${normalized}, ${status} score, metric ${record.metric_key || metricName}. Source: ${record.source || "result matrix"}.`; | |
| return ` | |
| <span class="task-method-score ${record.proxy_scored ? "proxy" : "direct"}" style="--score-color:${escapeHtml(color)};--score-width:${scoreWidth.toFixed(1)}%" aria-label="${escapeHtml(title)}" title="${escapeHtml(title)}"> | |
| <span class="task-method-top"><strong>${escapeHtml(series.short_label || series.label || series.id)}</strong><em>${escapeHtml(status)}</em></span> | |
| <span class="task-method-value">${escapeHtml(rawValue)}</span> | |
| <span class="task-method-meta">${escapeHtml(metricLabel)} · norm ${escapeHtml(normalized)}</span> | |
| </span> | |
| `; | |
| }).join(""); | |
| return ` | |
| <div class="task-results-panel"> | |
| <div class="task-results-head"> | |
| <strong>9 public method scores</strong> | |
| <em>task metric ${escapeHtml(metricName)} · bar = normalized</em> | |
| </div> | |
| <div class="task-method-grid" aria-label="All nine public method scores for ${escapeHtml(task.display_name)}">${cells}</div> | |
| </div> | |
| `; | |
| }; | |
| const normalizeWalkthroughTasks = (payload) => Object.values(payload.tasks || {}); | |
| const normalizeSuiteTasks = (payload) => Array.isArray(payload.tasks) ? payload.tasks : Object.values(payload.tasks || {}); | |
| const familyFromSuiteTask = (task) => { | |
| const id = task.task_id || ""; | |
| const text = `${id} ${task.task_display_name || ""} ${task.output_short || ""}`.toLowerCase(); | |
| if (text.includes("retrieval") || text.includes("sync")) return "retrieval"; | |
| if (text.includes("forecast") || text.includes("future") || text.includes("next") || text.includes("transition regression")) return "forecast"; | |
| if (text.includes("reconstruction") || text.includes("pose") || text.includes("order") || text.includes("boundary")) return "diagnostic"; | |
| return "supervised"; | |
| }; | |
| const modalitiesFromSuiteTask = (task) => { | |
| const text = `${task.input || ""} ${task.output || ""} ${task.task_id || ""}`.toLowerCase(); | |
| const modalities = []; | |
| if (text.includes("video") || text.includes("camera") || text.includes("view") || text.includes("fisheye")) modalities.push("video"); | |
| if (text.includes("audio")) modalities.push("audio"); | |
| if (text.includes("depth")) modalities.push("depth"); | |
| if (text.includes("pose") || text.includes("slam")) modalities.push("pose_slam"); | |
| if (text.includes("hand") || text.includes("body") || text.includes("mocap")) modalities.push("motion_capture"); | |
| if (text.includes("imu") || text.includes("inertial") || text.includes("gyroscope")) modalities.push("inertial"); | |
| if (text.includes("language") || text.includes("caption") || text.includes("text") || text.includes("interaction")) modalities.push("language"); | |
| return modalities.length ? Array.from(new Set(modalities)) : ["video", "motion_capture", "inertial"]; | |
| }; | |
| const synthesizeTaskCard = (task) => { | |
| const modalities = modalitiesFromSuiteTask(task); | |
| const family = familyFromSuiteTask(task); | |
| return { | |
| task_id: task.task_id, | |
| task_number: task.task_number, | |
| display_name: task.task_display_name || task.task_id || "Task", | |
| research_name: `${task.suite_label || "Task"} · ${task.research_name || task.primary_direction || "Unified task contract"}`, | |
| task_family: family, | |
| architecture_family: task.architecture_family || task.family || "task head", | |
| poster_modality: modalities[0] || "video", | |
| modalities, | |
| card_blurb: task.meaning || "A source-linked task contract in the unified 20-task suite.", | |
| case_study: task.meaning || "This task is defined by the unified 20-task contract.", | |
| input_short: task.input_short || task.input || "20-frame multimodal window", | |
| process_short: task.process || "shared window features -> task-specific head", | |
| output_short: task.output_short || task.output || "task target", | |
| middle_modules: [ | |
| task.process || "Build the task target from the shared window table.", | |
| `Metric: ${task.metric_name || task.metric_key || "primary metric"}.`, | |
| `Source: ${(task.artifact_sources && (task.artifact_sources.minimal_metrics || task.artifact_sources.walkthrough || task.artifact_sources.legacy_result_directory)) || "docs/data/task_suite_20.json"}.` | |
| ], | |
| metric: { | |
| name: task.metric_name || task.metric_key || "metric", | |
| direction: task.metric_direction || "higher", | |
| minimal: task.minimal_primary_metric, | |
| neural_mlp: task.neural_primary_metric | |
| }, | |
| failure_mode: "single-episode chronological split; read the 180-record matrix for selected-128 method comparisons" | |
| }; | |
| }; | |
| const mergeUnifiedTaskCards = (walkthroughPayload, suitePayload) => { | |
| const walkthroughTasks = normalizeWalkthroughTasks(walkthroughPayload); | |
| const walkthroughById = Object.fromEntries( | |
| walkthroughTasks.map((task) => [task.task_id || task.artifact_id || task.display_name, task]) | |
| ); | |
| const suiteTasks = normalizeSuiteTasks(suitePayload); | |
| return suiteTasks.map((task) => { | |
| const rich = walkthroughById[task.task_id]; | |
| if (rich) { | |
| return { | |
| ...rich, | |
| task_id: task.task_id || rich.task_id, | |
| task_number: task.task_number || rich.task_number, | |
| display_name: task.task_display_name || rich.display_name, | |
| research_name: `${task.suite_label || ""} · ${rich.research_name || task.research_name || ""}`.replace(/^ · /, ""), | |
| metric: { | |
| ...(rich.metric || {}), | |
| name: task.metric_name || rich.metric?.name, | |
| direction: task.metric_direction || rich.metric?.direction, | |
| minimal: task.minimal_primary_metric ?? rich.metric?.minimal, | |
| neural_mlp: task.neural_primary_metric ?? rich.metric?.neural_mlp | |
| } | |
| }; | |
| } | |
| return synthesizeTaskCard(task); | |
| }); | |
| }; | |
| const modalityLabels = (task) => (task.modalities || []) | |
| .map((key) => modalityMeta[key]?.label) | |
| .filter(Boolean) | |
| .join(", "); | |
| function stageNarration(task) { | |
| const minimal = formatMetric(task.metric?.minimal); | |
| const neural = formatMetric(task.metric?.neural_mlp); | |
| const modules = (task.middle_modules || []).slice(0, 2).join(" "); | |
| return [ | |
| `Input: ${task.input_short}. Evidence shown here comes from ${modalityLabels(task)}.`, | |
| `Process: ${task.process_short}. ${modules}`, | |
| `Output: ${task.output_short}. Case study: ${task.case_study}`, | |
| `Evaluate: ${task.metric.name}, ${task.metric.direction} is better. Minimal ${minimal}; neural MLP ${neural}. Limitation: ${task.failure_mode}` | |
| ]; | |
| } | |
| function renderTaskCards() { | |
| const grid = document.getElementById("taskGrid"); | |
| grid.innerHTML = taskEntries.map((task, index) => { | |
| const family = task.task_family; | |
| const icon = taskIconFor(task); | |
| const modalityChips = (task.modalities || []) | |
| .map((key) => modalityMeta[key]?.label || key) | |
| .filter(Boolean) | |
| .slice(0, 5) | |
| .map((label) => `<span>${escapeHtml(label)}</span>`) | |
| .join(""); | |
| return ` | |
| <button type="button" class="task-card" data-kind="${escapeHtml(family)}" data-index="${index}" aria-pressed="false"> | |
| <div class="task-top"> | |
| <span class="task-title-wrap"> | |
| <img class="task-card-icon" src="${escapeHtml(icon.src)}" alt="${escapeHtml(icon.alt)}" loading="lazy"> | |
| <span> | |
| <span class="task-name">${escapeHtml(task.display_name)}</span> | |
| <span class="task-research-name">${escapeHtml(task.research_name)}</span> | |
| </span> | |
| </span> | |
| <span class="tag ${escapeHtml(family)}">${escapeHtml(family)}</span> | |
| </div> | |
| <p>${escapeHtml(task.card_blurb)}</p> | |
| <div class="task-contract"> | |
| <span><strong>Input</strong>${escapeHtml(task.input_short)}</span> | |
| <span><strong>Middle</strong>${escapeHtml(task.process_short)}</span> | |
| <span><strong>Output</strong>${escapeHtml(task.output_short)}</span> | |
| </div> | |
| <div class="task-modality-chips" aria-label="Modalities used by ${escapeHtml(task.display_name)}">${modalityChips}</div> | |
| ${renderTaskMethodResults(task, index)} | |
| </button> | |
| `; | |
| }).join(""); | |
| grid.querySelectorAll(".task-card").forEach((card) => { | |
| card.addEventListener("click", () => { | |
| pausePlayer(); | |
| setActiveTask(Number(card.dataset.index)); | |
| document.getElementById("walkthroughs").scrollIntoView({ behavior: "smooth", block: "start" }); | |
| }); | |
| }); | |
| applyTaskFilter(activeFilter); | |
| window.observeXperienceReveal?.(grid); | |
| } | |
| function renderSelector() { | |
| const selector = document.getElementById("walkthroughSelector"); | |
| selector.innerHTML = taskEntries.map((task, index) => { | |
| const icon = taskIconFor(task); | |
| return ` | |
| <button type="button" class="selector-button" data-index="${index}" aria-pressed="false"> | |
| <img class="selector-task-icon" src="${escapeHtml(icon.src)}" alt="" loading="lazy" aria-hidden="true"> | |
| <strong>${escapeHtml(task.display_name)}</strong> | |
| <span>${escapeHtml(task.task_family)}</span> | |
| </button> | |
| `; | |
| }).join(""); | |
| selector.querySelectorAll(".selector-button").forEach((button) => { | |
| button.addEventListener("click", () => { | |
| pausePlayer(); | |
| setActiveTask(Number(button.dataset.index)); | |
| }); | |
| }); | |
| } | |
| function renderPlayer(task, index) { | |
| const poster = modalityMeta[task.poster_modality] || modalityMeta.video; | |
| const icon = taskIconFor(task); | |
| document.getElementById("playerPoster").src = poster.src; | |
| document.getElementById("playerPoster").alt = `${task.display_name} representative ${poster.label} modality`; | |
| document.getElementById("playerTaskIcon").src = icon.src; | |
| document.getElementById("playerTaskIcon").alt = icon.alt; | |
| document.getElementById("playerBadgeTitle").textContent = task.display_name; | |
| document.getElementById("playerBadgeMeta").textContent = task.research_name; | |
| document.getElementById("playerFamily").className = `tag ${task.task_family}`; | |
| document.getElementById("playerFamily").textContent = task.task_family; | |
| document.getElementById("playerArchitecture").textContent = task.architecture_family; | |
| document.getElementById("playerTitle").textContent = task.display_name; | |
| document.getElementById("playerCase").textContent = task.case_study; | |
| document.getElementById("playerInput").textContent = task.input_short; | |
| document.getElementById("playerProcess").textContent = task.process_short; | |
| document.getElementById("playerOutput").textContent = task.output_short; | |
| document.getElementById("playerModules").innerHTML = task.middle_modules.map((module, moduleIndex) => ( | |
| `<li><strong>Module ${String(moduleIndex + 1).padStart(2, "0")}</strong>${escapeHtml(module)}</li>` | |
| )).join(""); | |
| document.getElementById("playerMetric").textContent = `${task.metric.name} (${task.metric.direction} is better). Minimal ${formatMetric(task.metric.minimal)}; neural MLP ${formatMetric(task.metric.neural_mlp)}.`; | |
| document.getElementById("playerLimit").textContent = `Current limitation: ${task.failure_mode}`; | |
| document.getElementById("playerScrub").max = Math.max(0, taskEntries.length - 1); | |
| document.getElementById("playerScrub").value = index; | |
| document.getElementById("playerModalities").innerHTML = task.modalities.map((key) => { | |
| const modality = modalityMeta[key] || modalityMeta.video; | |
| return `<span class="modality-tile"><img src="${modality.src}" alt="${escapeHtml(modality.label)} sample thumbnail"><span>${escapeHtml(modality.label)}</span></span>`; | |
| }).join(""); | |
| renderStageFrame(task, index); | |
| } | |
| function renderStageFrame(task, index) { | |
| const stage = storyStages[activeStageIndex] || storyStages[0]; | |
| const narration = stageNarration(task); | |
| const totalFrames = Math.max(1, taskEntries.length * storyStages.length); | |
| const currentFrame = index * storyStages.length + activeStageIndex + 1; | |
| document.getElementById("playerFrameChip").textContent = `Step ${activeStageIndex + 1} / ${storyStages.length} · ${stage.label}`; | |
| document.getElementById("playerFrameCaption").textContent = narration[activeStageIndex] || narration[0]; | |
| document.getElementById("playerCounter").textContent = `${String(index + 1).padStart(2, "0")} / ${String(taskEntries.length).padStart(2, "0")} · ${stage.label}`; | |
| document.getElementById("playerProgress").style.width = `${(currentFrame / totalFrames) * 100}%`; | |
| document.querySelectorAll("[data-stage]").forEach((button) => { | |
| const active = Number(button.dataset.stage) === activeStageIndex; | |
| button.classList.toggle("active", active); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| }); | |
| } | |
| function updateActiveMarkers() { | |
| document.querySelectorAll(".task-card").forEach((card) => { | |
| const active = Number(card.dataset.index) === activeTaskIndex; | |
| card.classList.toggle("active", active); | |
| card.setAttribute("aria-pressed", active ? "true" : "false"); | |
| }); | |
| document.querySelectorAll(".selector-button").forEach((button) => { | |
| const active = Number(button.dataset.index) === activeTaskIndex; | |
| button.classList.toggle("active", active); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| }); | |
| } | |
| function setActiveTask(index, options = {}) { | |
| if (!taskEntries.length) return; | |
| activeTaskIndex = (index + taskEntries.length) % taskEntries.length; | |
| if (options.resetStage !== false) activeStageIndex = 0; | |
| renderPlayer(taskEntries[activeTaskIndex], activeTaskIndex); | |
| updateActiveMarkers(); | |
| } | |
| function setActiveStage(index) { | |
| if (!taskEntries.length) return; | |
| activeStageIndex = (index + storyStages.length) % storyStages.length; | |
| renderStageFrame(taskEntries[activeTaskIndex], activeTaskIndex); | |
| } | |
| function advancePlayer() { | |
| if (activeStageIndex < storyStages.length - 1) { | |
| setActiveStage(activeStageIndex + 1); | |
| return; | |
| } | |
| setActiveTask(activeTaskIndex + 1); | |
| } | |
| function applyTaskFilter(filter) { | |
| activeFilter = filter; | |
| document.querySelectorAll(".filter").forEach((button) => { | |
| const active = button.dataset.filter === filter; | |
| button.classList.toggle("active", active); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| }); | |
| document.querySelectorAll(".task-card").forEach((card) => { | |
| card.classList.toggle("hide", filter !== "all" && card.dataset.kind !== filter); | |
| }); | |
| } | |
| function pausePlayer() { | |
| if (playerTimer) { | |
| window.clearInterval(playerTimer); | |
| playerTimer = null; | |
| document.getElementById("playerPlay").textContent = "Play"; | |
| } | |
| } | |
| function togglePlayer() { | |
| if (playerTimer) { | |
| pausePlayer(); | |
| return; | |
| } | |
| document.getElementById("playerPlay").textContent = "Pause"; | |
| playerTimer = window.setInterval(advancePlayer, 2600); | |
| } | |
| function labelResponsiveTables() { | |
| document.querySelectorAll(".line-table, .glossary-table").forEach((table) => { | |
| const headers = Array.from(table.querySelectorAll("thead th")).map((header) => header.textContent.trim()); | |
| table.querySelectorAll("tbody tr").forEach((row) => { | |
| Array.from(row.children).forEach((cell, index) => { | |
| if (headers[index] && !cell.dataset.label) { | |
| cell.dataset.label = headers[index]; | |
| } | |
| }); | |
| }); | |
| }); | |
| } | |
| async function initTaskSurface() { | |
| try { | |
| const [walkthroughResponse, suiteResponse, matrixPayload] = await Promise.all([ | |
| fetch("data/task_walkthroughs.json", { cache: "no-cache" }), | |
| fetch("data/task_suite_20.json", { cache: "no-cache" }), | |
| fetch("data/task_method_20_result_matrix.json", { cache: "no-cache" }) | |
| .then((response) => response.ok ? response.json() : null) | |
| .catch(() => null) | |
| ]); | |
| if (!walkthroughResponse.ok) throw new Error(`walkthrough data ${walkthroughResponse.status}`); | |
| if (!suiteResponse.ok) throw new Error(`suite data ${suiteResponse.status}`); | |
| prepareTaskResultLookup(matrixPayload); | |
| taskEntries = mergeUnifiedTaskCards(await walkthroughResponse.json(), await suiteResponse.json()); | |
| renderTaskCards(); | |
| renderSelector(); | |
| setActiveTask(0); | |
| } catch (error) { | |
| document.getElementById("taskGrid").innerHTML = '<p class="repro-note">Task walkthrough data could not be loaded.</p>'; | |
| document.getElementById("walkthroughSelector").innerHTML = ""; | |
| } | |
| } | |
| document.querySelectorAll(".filter").forEach((button) => { | |
| button.addEventListener("click", () => applyTaskFilter(button.dataset.filter)); | |
| }); | |
| document.getElementById("playerPrev").addEventListener("click", () => { pausePlayer(); setActiveTask(activeTaskIndex - 1); }); | |
| document.getElementById("playerNext").addEventListener("click", () => { pausePlayer(); setActiveTask(activeTaskIndex + 1); }); | |
| document.getElementById("playerPlay").addEventListener("click", togglePlayer); | |
| document.getElementById("playerScrub").addEventListener("input", (event) => { | |
| pausePlayer(); | |
| setActiveTask(Number(event.target.value)); | |
| }); | |
| document.querySelectorAll("[data-stage]").forEach((button) => { | |
| button.addEventListener("click", () => { | |
| pausePlayer(); | |
| setActiveStage(Number(button.dataset.stage)); | |
| }); | |
| }); | |
| labelResponsiveTables(); | |
| initTermTooltipPortal(); | |
| initGlossaryControls(); | |
| initGlossaryData(); | |
| initResultMatrixTable(); | |
| initTaskSurface(); | |
| document.querySelectorAll("[data-copy]").forEach((button) => { | |
| button.addEventListener("click", async () => { | |
| const target = document.getElementById(button.dataset.copy); | |
| try { | |
| await navigator.clipboard.writeText(target.innerText); | |
| } catch (error) { | |
| return; | |
| } | |
| const previous = button.textContent; | |
| button.textContent = "Copied"; | |
| setTimeout(() => button.textContent = previous, 1300); | |
| }); | |
| }); | |
| </script> | |
| <script src="https://translate.google.com/translate_a/element.js?cb=googleTranslateElementInit" async></script> | |
| </body> | |
| </html> | |