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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,
.reader-step 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; }
#raw-sample { order: 5; }
#suite { order: 6; }
#pipeline { order: 7; }
#protocol { order: 8; }
#takeaways { order: 9; }
#models { order: 10; }
#neural { order: 11; }
#directions { order: 12; }
#extensions { order: 13; }
#architectures { order: 14; }
#walkthroughs { order: 15; }
#tasks { order: 16; }
#features { order: 17; }
#diagnostics { order: 18; }
#evidence { order: 19; }
#glossary { order: 20; }
#artifacts { order: 21; }
#omni-scale-up { order: 22; }
#run { order: 23; }
#suite { padding: 62px 0 76px; }
#suite .wrap { width: min(1680px, calc(100% - 48px)); }
#suite .section-head { max-width: var(--max); margin-inline: auto; }
.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: min(88vh, 1080px);
min-height: clamp(700px, 58vw, 980px);
padding: 8px;
}
#suite .split-radar-card img {
min-height: clamp(560px, 45vw, 860px);
}
.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;
}
.split-radar-grid {
display: grid;
grid-template-columns: minmax(0, 1fr);
gap: 24px;
margin: 26px 0 34px;
}
.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%;
min-height: clamp(420px, 34vw, 660px);
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-claim {
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-claim 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-claim 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);
}
.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;
}
.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-body {
padding: 22px 24px 24px;
}
.result-matrix-legend {
display: flex;
flex-wrap: wrap;
gap: 8px;
margin-bottom: 14px;
}
.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);
}
.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;
}
.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(5, minmax(0, 1fr));
gap: 14px;
align-items: stretch;
}
.roadmap-card {
position: relative;
border: 1px solid var(--line);
border-radius: var(--radius);
padding: 18px;
min-height: 330px;
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); }
.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;
}
.reader-journey {
display: grid;
grid-template-columns: repeat(4, minmax(0, 1fr));
gap: 12px;
margin-bottom: 22px;
}
.reader-step {
display: grid;
gap: 11px;
align-content: start;
min-height: 184px;
border: 1px solid var(--soft-line);
border-radius: var(--radius);
padding: 18px;
background:
linear-gradient(180deg, rgba(204, 255, 160, 0.08), rgba(2, 5, 2, 0.42)),
rgba(7, 18, 7, 0.58);
}
.reader-step strong,
.resource-mode strong {
color: var(--ink);
font-family: var(--font-ui);
font-size: 19px;
line-height: 1.1;
}
.reader-step p,
.resource-mode p {
margin: 0;
color: var(--muted);
font-size: 13px;
line-height: 1.52;
}
.reader-step a,
.resource-mode a {
justify-self: start;
margin-top: auto;
color: var(--cyan);
font-weight: 760;
text-decoration: none;
}
.reader-step a:hover,
.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: minmax(260px, 0.88fr) minmax(0, 1.42fr);
gap: 18px;
align-items: start;
}
.glossary-summary {
display: grid;
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-table-wrap {
overflow-x: auto;
border: 1px solid var(--line);
border-radius: var(--card-radius);
background: rgba(2, 5, 2, 0.58);
}
.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;
background: rgba(204, 255, 160, 0.07);
}
.glossary-table td:first-child {
width: 18%;
color: var(--ink);
font-family: var(--font-ui);
font-size: 15px;
font-weight: 750;
}
.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-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: 324px;
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-card-media {
overflow: hidden;
border: 1px solid rgba(204, 255, 160, 0.18);
border-radius: 7px;
background: #020502;
}
.task-card-media img {
display: block;
width: 100%;
aspect-ratio: 16 / 8.5;
object-fit: cover;
transform: scale(1.01);
}
.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-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;
}
.metric-row {
display: grid;
grid-template-columns: repeat(2, minmax(0, 1fr));
gap: 8px;
font-size: 12px;
}
.metric-row span {
display: block;
border: 1px solid var(--soft-line);
border-radius: 6px;
padding: 9px;
color: var(--muted);
min-width: 0;
}
.metric-row strong {
display: block;
color: var(--ink);
font-family: var(--font-mono);
font-size: 18px;
line-height: 1.1;
font-variant-numeric: tabular-nums;
word-break: break-word;
}
.mini-bar { height: 7px; background: rgba(204, 255, 160, 0.14); border-radius: 999px; overflow: hidden; }
.mini-bar span { display: block; height: 100%; width: var(--w); background: var(--c); }
.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;
}
.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);
}
.player-badge strong {
display: block;
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;
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;
}
.selector-button strong {
display: block;
color: var(--ink);
font-family: var(--font-ui);
font-size: 14px;
line-height: 1.15;
}
.selector-button span {
display: block;
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);
}
.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);
}
.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,
.metric-row span,
.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,
.mini-bar,
.player-progress {
background: rgba(204, 255, 160, 0.16);
}
.track > span,
.roadmap-card::before,
.mini-bar span,
.player-progress span,
.bar span {
background: linear-gradient(90deg, 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,
.reader-step strong,
.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,
.reader-step 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-v13-modality-xl") 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;
}
.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(4, minmax(0, 1fr));
gap: 6px;
margin-top: 2px;
}
.hero-task-strip span {
min-width: 0;
border: 1px solid rgba(245, 247, 240, 0.10);
border-radius: 5px;
background: rgba(255, 255, 255, 0.04);
padding: 6px 7px;
color: rgba(245, 247, 240, 0.80);
font-size: 10px;
line-height: 1.15;
overflow-wrap: anywhere;
}
.hero-radar-links {
display: flex;
flex-wrap: wrap;
gap: 8px;
margin-top: auto;
}
.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);
}
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,
.mini-bar > span {
transform-origin: left center;
}
.motion-ready .track > span,
.motion-ready .mini-bar > 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: center;
min-height: min(880px, calc(100vh - 24px));
padding: 92px 0 58px;
}
.hero-inner > div:first-child {
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;
}
.hero-stats {
grid-template-columns: repeat(2, minmax(0, 1fr));
max-width: 520px;
margin-top: 28px;
}
.hero-radar-panel {
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(4, minmax(0, 1fr));
}
.hero-task-strip span {
font-size: 9px;
padding: 5px 6px;
}
.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;
}
.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-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, .reader-journey, .resource-mode-grid, .suite-lines, .result-reading-order { grid-template-columns: repeat(2, 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)); }
.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; }
.section-head { display: block; }
.section-head p { margin-top: 14px; }
}
@media (max-width: 1120px) {
.nav-links { display: none; }
}
@media (max-width: 640px) {
:root {
--nav-height: 104px;
--tab-stack-offset: 120px;
}
.site-nav {
min-height: var(--nav-height);
}
.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: relative;
height: 64px;
}
.brand {
font-size: 16px;
gap: 9px;
}
.brand span {
display: none;
}
.brand-logo {
width: 38px;
height: 38px;
}
.site-language {
min-height: 38px;
padding: 0 8px;
font-size: 12px;
width: 58px;
max-width: 58px;
justify-content: center;
position: absolute;
left: 62px;
right: auto;
top: 13px;
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: 6px;
padding: 0;
border: 0;
border-radius: 0;
background: transparent;
box-shadow: none;
flex: 0 0 auto;
width: auto;
margin-left: 0;
margin-right: 0;
justify-content: flex-end;
overflow: visible;
position: static;
}
.nav-external-actions {
padding: 0;
flex: 0 0 auto;
position: absolute;
left: 62px;
right: auto;
top: 66px;
transform: none;
}
.nav-action {
min-width: 42px;
height: 32px;
padding: 0 10px;
font-size: 11.5px;
gap: 5px;
}
.nav-action::before {
width: 22px;
height: 22px;
font-size: 9px;
}
.nav-action-repo {
display: inline-flex;
min-width: 42px;
padding: 0 10px;
}
.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;
}
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, .reader-journey, .resource-mode-grid, .suite-lines, .suite-line-facts, .result-reading-order { 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;
}
.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-v13-modality-xl") center / cover no-repeat;
}
.hero-panel { grid-template-columns: 1fr; }
.hero-path {
min-height: 0;
}
.reader-step,
.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: 560px; min-height: 560px; }
.split-radar-card img { min-height: 500px; }
.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 7px;
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 7px;
}
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<span>Ropedia Xperience-10M</span>
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<a href="#overview">Overview</a>
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<div>
<div class="eyebrow">two evidence lines / 180 scored records</div>
<h1>Ropedia Xperience-10M Task Suite.</h1>
<p class="hero-copy">
The public suite has two evidence lines. Line 1 uses one public sample
episode to make the 20-task lab inspectable and reproducible. Line 2
uses 128 selected episodes to compare aligned baselines, Qwen3-Omni
v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window. The public matrix is complete at 180/180 scored
method-task records, with six compact-proxy cells explicitly marked.
</p>
<div class="hero-actions">
<a class="button primary" href="#reader-map">Start guided path</a>
<a class="button" href="#takeaways">See current results</a>
<a class="button" href="#suite">Inspect 20 tasks</a>
<a class="button" href="single_episode_explorer.html">Open explorer</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-claim">
<div><span>valid claim</span><p>Task construction, file inspection, and local reproducibility.</p></div>
<div><span>do not claim</span><p>Multi-episode generalization.</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-claim">
<div><span>valid claim</span><p>Same-split method comparison and scale-up planning.</p></div>
<div><span>do not claim</span><p>Proxy cells as direct raw-target measurements.</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 reader path">
<a class="hero-path" href="#overview">
<small>Understand</small>
<strong>Project in 5 minutes</strong>
<span>Brief, scope boundary, public sample, and what is complete today.</span>
</a>
<a class="hero-path" href="#suite">
<small>Inspect</small>
<strong>Tasks and results</strong>
<span>20 task contracts, radar views, method rows, and source audits.</span>
</a>
<a class="hero-path" href="#run">
<small>Reproduce</small>
<strong>Run the public pipeline</strong>
<span>Commands, validators, packaged mirrors, and release checks.</span>
</a>
<a class="hero-path" href="#directions">
<small>Extend</small>
<strong>Plan next training tracks</strong>
<span>Spatial intelligence, human-video world models, VLA, and scale-up plans.</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="Unified 20-task radar comparison">
<div class="hero-radar-top">
<strong>home radar comparison</strong>
<span>4 grouped panels / 180 scored records / 174 direct + 6 compact-proxy</span>
</div>
<div class="hero-radar-layout">
<a class="hero-radar-frame" href="#suite" aria-label="Open full unified 20-task model radar">
<img src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v7" alt="Unified 20-task grouped radar board with method-family panels, task key, score counts, and raw128 proxy notes">
</a>
<div class="hero-radar-copy">
<h2>Model comparison is grouped by method family.</h2>
<p>The full SVG names every task axis, separates the nine methods into readable panels, and keeps source artifacts plus proxy notes attached to the same comparison view.</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 class="hero-method-list" aria-label="Method families shown in the radar">
<div class="hero-method" style="--method-color:#67e8d1"><strong>Panel 1: Minimal + Neural MLP</strong><span>Single public-sample episode; 40/40 direct task scores.</span></div>
<div class="hero-method" style="--method-color:#ffd166"><strong>Panel 2: 128ep metadata/text</strong><span>Aligned JSONL and staged-target baselines; 40/40 scored with proxy flags retained.</span></div>
<div class="hero-method" style="--method-color:#22d3ee"><strong>Panel 3: 128ep raw features</strong><span>Sensor-block simple/NN heads; 40/40 scored with task 15/19 compact proxies marked.</span></div>
<div class="hero-method" style="--method-color:#9bb8ff"><strong>Panel 4: Qwen3 + Cosmos3</strong><span>Qwen3-Omni v6 LoRA, Cosmos3-Super, and Cosmos3-Nano; 60/60 scored from verified artifacts.</span></div>
</div>
<div class="hero-task-strip" aria-label="Radar task axis examples">
<span>01 Action Recognition</span>
<span>05 Hand Trajectory Forecasting</span>
<span>08 Language Grounding</span>
<span>12 Multimodal Sync Detection</span>
<span>15 Interaction Text Prediction</span>
<span>18 IMU-to-Hand Pose Reconstruction</span>
<span>19 Camera-View Sync Retrieval</span>
<span>20 Time-to-Next-Transition Regression</span>
</div>
<div class="hero-radar-links">
<a href="#suite">Open full radar</a>
<a href="assets/charts/unified_task_model_radar.svg">Open SVG</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">Open radar JSON</a>
<a href="#result-matrix-table">Open 180-result table</a>
<a href="data/task_method_20_result_matrix.json">Open 20-result matrix</a>
<a href="data/task_method_20_gap_audit.json">Open score/proxy audit</a>
</div>
</div>
</div>
</div>
</div>
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<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 roadmap development-directions reading-path">
<strong>Start</strong>
<span>project overview and roadmap</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 suite walkthroughs tasks" 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 architectures features" tabindex="-1">
<strong>Method</strong>
<span>pipeline and model design</span>
</button>
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<strong>Results</strong>
<span>takeaways and baselines</span>
</button>
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<strong>Directions</strong>
<span>four tracks and probes</span>
</button>
<button type="button" class="project-tab" id="tab-resources" role="tab" data-tab-key="resources" data-default-section="artifacts" aria-selected="false" aria-pressed="false" aria-controls="evidence glossary artifacts omni-scale-up run" tabindex="-1">
<strong>Resources</strong>
<span>research artifacts and scale-up</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 recommended entry paths.</span>
<a href="#reader-map">Reader 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>Two evidence lines: 1 episode and 128 episodes.</h2>
<p>Read the suite as two lines. Line 1 proves the task lab is inspectable and reproducible. Line 2 compares selected-128 metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window. Keep the lines separate when interpreting scores.</p>
</div>
<div class="about-identity" aria-label="About Ropedia Xperience-10M Task Suite">
<img src="assets/brand/xperience10m-logo-mark-192.png" alt="Ropedia Xperience-10M project logo" width="92" height="92" loading="eager" decoding="async">
<div>
<span>About this public surface</span>
<strong>Ropedia Xperience-10M Task Suite</strong>
<p>The mark identifies the shared public package across the GitHub repository, GitHub Pages dashboard, Hugging Face Space, artifact dataset, model mirrors, and social preview. Use this area as the project identity checkpoint before reading the 1-episode and selected-128 evidence lines.</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>
<figure class="line-map-figure">
<img src="assets/charts/two_evidence_line_map.svg?v=two-line-map-v1" 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>Valid claim</th>
<th>Do not claim</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 reproducibility, and controlled baseline behavior.</td>
<td>Multi-episode generalization.</td>
<td><a href="#raw-sample">Raw browser</a><br><a href="data/single_episode_task_model_radar.json">1-episode radar JSON</a><br><a href="data/two_evidence_line_result_summary.json">result summary JSON</a><br><a href="data/two_evidence_lines.json">line JSON</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, Qwen3-Omni v6 LoRA diagnostics, Cosmos3-Super/Cosmos3-Nano diagnostics, and scale-up decisions.</td>
<td>Reading compact-proxy cells as direct raw-target measurements.</td>
<td><a href="data/episode128_task_model_radar.json">128-episode radar JSON</a><br><a href="data/xperience10m_128_episode_feature_index.json">feature index JSON</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 JSON</a><br><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TWO_EVIDENCE_LINES.md">line doc</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: <a href="data/qwen3_omni_run_lineage.json">qwen3_omni_run_lineage.json</a> and <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/QWEN3_OMNI_RUN_LINEAGE.md">QWEN3_OMNI_RUN_LINEAGE.md</a>.</p>
<div class="reader-journey" aria-label="Recommended reader journeys">
<article class="reader-step">
<small>01 understand</small>
<strong>Start with scope and status</strong>
<p>Use this route if you need the project story, what is public, and what claims are safe.</p>
<a href="#overview">Read overview</a>
</article>
<article class="reader-step">
<small>02 inspect</small>
<strong>Follow the task evidence</strong>
<p>Use the 20-task suite, radar, matrix, and source audit to compare methods without losing metric provenance.</p>
<a href="#suite">Open task suite</a>
</article>
<article class="reader-step">
<small>03 reproduce</small>
<strong>Run or verify the release</strong>
<p>Use scripts, validators, mirrors, and checks when you want to rerun or trust the public package.</p>
<a href="#run">Open reproduce path</a>
</article>
<article class="reader-step">
<small>04 extend</small>
<strong>Choose the next model track</strong>
<p>Use directions and scale-up resources for spatial, world-model, VLA, Qwen3-Omni, and Cosmos3 follow-up work.</p>
<a href="#directions">Open directions</a>
</article>
</div>
<div class="brief-panel reader-map-panel" id="reader-map">
<div class="brief-panel-head">
<div>
<span>Public reader map</span>
<h3>Choose the right entry point without losing the evidence trail.</h3>
</div>
<p>The project keeps source code, visual explanation, derived artifacts, model outputs, and release checks on different public surfaces. This map shows what each surface is responsible for before you dive into the full file set.</p>
</div>
<div class="brief-grid" aria-label="Public reader paths">
<article class="brief-card">
<small>overview</small>
<strong>Understand the project quickly</strong>
<p>Start with the brief and status files, then use the dashboard for the visual story.</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">status JSON</a>
</div>
</article>
<article class="brief-card">
<small>benchmark</small>
<strong>Inspect the 20-task suite</strong>
<p>Use the task contract, protocol, walkthroughs, and radar matrix to follow each scored axis.</p>
<div class="reading-links">
<a href="#suite">20 tasks</a>
<a href="data/task_suite_20.json">task JSON</a>
<a href="data/task_method_20_result_matrix.json">matrix</a>
</div>
</article>
<article class="brief-card">
<small>sample data</small>
<strong>Understand one data sample</strong>
<p>Open the sample explorer, raw-file manifest, and feature manifest before reading model scores.</p>
<div class="reading-links">
<a href="single_episode_explorer.html">explorer</a>
<a href="data/raw_sample_files.json">files</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="brief-card">
<small>terms</small>
<strong>Decode terminology</strong>
<p>Use the glossary when evidence lines, direct/proxy scores, Qwen v1-v6, Cosmos branches, or HF surfaces are unclear.</p>
<div class="reading-links">
<a href="#glossary">glossary</a>
<a href="data/glossary.json">glossary JSON</a>
<a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/GLOSSARY.md">Markdown</a>
</div>
</article>
<article class="brief-card">
<small>results</small>
<strong>Compare methods cleanly</strong>
<p>Single-episode baselines, 128-episode aligned baselines, Qwen3-Omni v6 LoRA, and Cosmos3-Super/Nano diagnostics stay separated by evidence type.</p>
<div class="reading-links">
<a href="#takeaways">takeaways</a>
<a href="data/unified_task_model_radar.json">radar data</a>
<a href="data/omni_model_comparison.json">model comparison</a>
</div>
</article>
<article class="brief-card">
<small>directions</small>
<strong>Read the three foundation pipelines</strong>
<p>Spatial intelligence, human-video world modeling, and vision-language-action are documented as trainable directions with task mappings.</p>
<div class="reading-links">
<a href="#directions">directions</a>
<a href="data/three_foundation_pipelines.json">pipeline JSON</a>
</div>
</article>
<article class="brief-card">
<small>release health</small>
<strong>Verify public copies</strong>
<p>Publication checks validate source alignment, package contents, mirror parity, and live URL/hash status.</p>
<div class="reading-links">
<a href="data/public_reader_map.json">reader map JSON</a>
<a href="data/public_surface_qa.json">surface QA</a>
<a href="data/live_publication_status.json">live status</a>
</div>
</article>
</div>
<div class="surface-map" aria-label="Public surface 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 JSON, 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 full reader map</a>
<a href="data/public_reader_map.json">Open reader-map JSON</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 limitations explicit before claiming broader model quality.</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="research_roadmap.html">Open interactive roadmap</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="#roadmap">Roadmap summary</a>
<a href="#reading-path">Reader path</a>
<a href="#takeaways">Current takeaways</a>
</div>
</div>
<div class="split-radar-grid" aria-label="Homepage split 20-task radar comparisons">
<article class="split-radar-card">
<h3>1-Episode 20-Task Radar</h3>
<p>Minimal and Neural MLP baselines over the original public-sample episode, with 40/40 scored method-task records.</p>
<img src="assets/charts/single_episode_task_model_radar.svg?v=xperience10m-split-radar-v2" 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">Open SVG</a>
<a href="data/single_episode_task_model_radar.json">Open JSON</a>
</div>
</article>
<article class="split-radar-card">
<h3>128-Episode 20-Task Radar</h3>
<p>Metadata, raw-feature, Qwen3-Omni, and Cosmos3 methods on the aligned 128-episode surface, with all 140 rows scored and proxy/evidence notes kept explicit.</p>
<img src="assets/charts/episode128_task_model_radar.svg?v=xperience10m-split-radar-v2" alt="128-episode grouped 20-task radar comparing metadata baselines, raw-feature baselines, Qwen3-Omni, and Cosmos3 series with explicit score counts">
<div class="split-radar-links">
<a href="assets/charts/episode128_task_model_radar.svg">Open SVG</a>
<a href="data/episode128_task_model_radar.json">Open JSON</a>
</div>
</article>
</div>
<div class="snapshot-grid">
<article class="snapshot-card">
<span class="status-pill">featured</span>
<h3>Interactive research roadmap</h3>
<p>Use this as the front door for the project: it 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="research_roadmap.html">Open roadmap</a>
<a href="data/research_roadmap_interactive.json">Roadmap structure</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 dataset card, including modalities, scale, access, and current project coverage. The source snapshot records 31.9 TB on the HF 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 data/source_alignment_audit.json.</p>
<div class="snapshot-meta">
<span>full dataset <strong>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>reader path <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 file <strong>task_suite_enhancement_128.json</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">Project summary</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">Current status</a>
<a href="data/research_roadmap.json">Roadmap summary</a>
<a href="data/project_packet.json">Reader path</a>
<a href="#artifacts">Project materials</a>
</div>
</div>
</section>
<section id="roadmap" data-project-tab="start" role="tabpanel" aria-labelledby="tab-start" 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-grid" 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>TASK_SUITE_ENHANCEMENT_128.md, task_suite_enhancement_128.json, 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 larger model-quality claims.</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-links">
<a href="research_roadmap.html">interactive roadmap</a>
<a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/RESEARCH_ROADMAP.md">roadmap document</a>
<a href="data/research_roadmap.json">roadmap stages</a>
<a href="data/foundation_model_plan.json">foundation model plan</a>
<a href="data/three_foundation_pipelines.json">three foundation pipelines</a>
<a href="data/additional_development_directions.json">additional directions</a>
<a href="data/task_suite_enhancement_128.json">task_suite_enhancement_128.json</a>
<a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/THREE_FOUNDATION_PIPELINES.md">pipeline track note</a>
<a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">native pretraining plan</a>
<a href="data/research_roadmap_interactive.json">interactive map</a>
<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/PROJECT_STATUS.md">project status</a>
</div>
</div>
</section>
<section id="development-directions" data-project-tab="start" role="tabpanel" aria-labelledby="tab-start" tabindex="-1">
<div class="wrap">
<div class="section-head">
<h2>Additional development directions.</h2>
<p>Beyond the current task heads, Qwen3-Omni fine-tuning path, Cosmos/world-model track, and future native pretraining goal, Xperience-10M can support three foundation pipeline tracks plus several concrete research-development tracks.</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 JSON</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">Track JSON</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">Track contract</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">direction data</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">direction 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">JSON 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>
<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 so readers can see the exact data unit, split, task targets, leakage controls, and current limitations before comparing scores.</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_suite_20.json</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 model-quality runs, 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">task_suite_enhancement_128.json</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="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>Aligned Xperience-10M sample windows</h3>
<p>5,821 frames become 1,161 synchronized 20-frame windows with an 8,546-dimensional representation.</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>
</div>
</article>
<article class="evidence-card">
<span class="status-pill">verified</span>
<h3>20 task contracts + 180 public results</h3>
<p>The current release reports nine method families over the unified 20-task axes, with minimal, neural, 128-episode, Qwen3, Cosmos3, and proxy-scored rows kept source-linked.</p>
<div class="evidence-links">
<a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite">task artifacts</a>
<a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/neural_mlp">neural MLP outputs</a>
</div>
</article>
<article class="evidence-card">
<span class="status-pill">verified</span>
<h3>Audio contribution is measured task by task</h3>
<p>Audio variants improve the primary metric on 6 walkthrough-backed task contracts in this single-episode setting.</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>
</div>
</article>
<article class="evidence-card">
<span class="status-pill">verified</span>
<h3>Four research directions are mapped by evidence type</h3>
<p>The Ropedia directions are labeled as direct, proxy, or diagnostic coverage, plus one coded extension probe per direction.</p>
<div class="evidence-links">
<a href="data/research_directions.json">direction map</a>
<a href="data/research_direction_extensions.json">extension probes</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 stays first for held-out LoRA; Cosmos 3 is the world-model track with camera-pose proxy forward-dynamics targets ready for trainer work; OpenVLA/openpi/GR00T are policy candidates after robot-compatible action conversion; Xperience-native pretraining is 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 series</h3>
<p>The selected 96/16/16 episode split now has a verified Qwen3-Omni v6 package with 4,032 held-out test predictions and 99.90% JSON validity. Cosmos3-Nano has 378 held-out future-window predictions, Cosmos3-Super Reasoner has 448 held-out base-weight JSON-task predictions, and Cosmos3-Super Forward-Dynamics LoRA has 448 held-out loss records.</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>
</div>
</article>
<article class="evidence-card">
<span class="status-pill">current plan</span>
<h3>Dense selected-128 export path</h3>
<p>The current 3,808-window export can be expanded 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/task_suite_enhancement_128.json">task_suite_enhancement_128.json</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>Multi-episode pilot status is explicit</h3>
<p>The Qwen3-Omni notes separate earlier diagnostic packages, the final 128-episode LoRA result, and the next action/subtask error-analysis pass.</p>
<div class="evidence-links">
<a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_ACCESS_STATUS.md">training status</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">verified</span>
<h3>Public pages are connected</h3>
<p>The website, GitHub repo, Hugging Face Space, artifact dataset, baseline model repo, consolidated weights/results repo, and collection point to the same research project.</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/cy0307/ropedia-xperience-10m-task-baselines">baseline models</a>
<a href="https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results">weights + results</a>
</div>
</article>
<article class="evidence-card">
<span class="status-pill">verified</span>
<h3>Figures are indexed</h3>
<p>The visual set includes the logo, raw-sample stream thumbnails, task-suite figure, unified 20-task model radar, model-architecture figure, provenance baseline chart, and Qwen3-Omni LoRA training-flow figure.</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/task_suite_infographic.png">task-suite figure</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>
</div>
</article>
<article class="evidence-card">
<span class="status-pill">verified</span>
<h3>Brand assets are packaged consistently</h3>
<p>The project logo is used consistently in the website header, favicon, README/HF cards, and social preview.</p>
<div class="evidence-links">
<a href="assets/brand/xperience10m-logo-social-card.png">logo card</a>
<a href="assets/brand/xperience10m-logo-mark-512.png">logo mark</a>
</div>
</article>
<article class="evidence-card">
<span class="status-pill">verified</span>
<h3>Raw dataset files are not redistributed</h3>
<p>The public project shares derived task artifacts, figures, reports, and lightweight baseline files. Raw Xperience-10M videos, HDF5 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://huggingface.co/datasets/ropedia-ai/xperience-10m">official dataset</a>
</div>
</article>
<article class="evidence-card">
<span class="status-pill">verified</span>
<h3>The dashboard is designed as the visual entry point</h3>
<p>Tabs organize the sample data, 20 tasks, model method, results, research directions, and next-stage resources.</p>
<div class="evidence-links">
<a href="#dataset-card">dataset</a>
<a href="#tasks">tasks</a>
<a href="#directions">directions</a>
</div>
</article>
<article class="evidence-card">
<span class="status-pill">verified</span>
<h3>Reproduction path is documented</h3>
<p>The reproduction guide lists the public sample setup, task-suite rebuild, neural heads, figure generation, and expected outputs.</p>
<div class="evidence-links">
<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>
</div>
</article>
<article class="evidence-card">
<span class="status-pill">verified</span>
<h3>Official dataset source is linked</h3>
<p>The project keeps the official Xperience-10M dataset, public sample, dataset website, and HOMIE toolkit visible so readers can trace the data source.</p>
<div class="evidence-links">
<a href="https://ropedia.com/dataset">dataset website</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>
</div>
</div>
</section>
<section id="reading-path" data-project-tab="start" role="tabpanel" aria-labelledby="tab-start" 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">reader path</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">task_suite_enhancement_128.json</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="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, limited diversity, and data/source_alignment_audit.json 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. It is not evidence of multi-episode generalization.</p><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">sample dataset</a></article>
<article class="artifact"><h3>Sample streams</h3><p>The raw browser is the canonical place to inspect synchronized video, embedded audio, HDF5 annotation groups, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals.</p><a href="#raw-sample">open raw browser</a><a href="data/modality_atlas.json">stream metadata</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>Raw sample browser</h3><p>The Data tab now exposes the official public sample files directly, including playable MP4 video streams and the audio track embedded in fisheye_cam0.mp4.</p><a href="#raw-sample">open raw browser</a><a href="data/raw_sample_files.json">raw manifest</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>Current project subset</h3><p>One public sample episode, 5,821 frames, 1,161 aligned windows, 8,546-dimensional task inputs, and direct links to the official raw sample files.</p><a href="#suite">task suite</a><a href="data/raw_sample_files.json">raw manifest</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="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-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>Sensor, labels, pose, mocap, IMU, depth, calibration, and captions.</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>Optional Rerun viewer recording. Download/open with Rerun 0.29.0.</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 <a href="data/modality_atlas.json">modality_atlas.json</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>Ropedia Xperience-10M Unified 20-Task Suite.</h2>
<p>The suite connects synchronized multimodal windows to 20 task contracts in one table, one radar surface, and one source-linked result matrix. Historical filenames remain only for stable artifact links.</p>
</div>
<div class="figure-pan" id="task-suite-map">
<img class="task-suite-image" src="assets/task_suite_infographic.png?v=xperience10m-taskfirst-v13-modality-xl" alt="Infographic showing Ropedia Xperience-10M task families with enlarged full-width modality cards">
</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 JSON 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>
<img class="chart radar-chart unified-radar-chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v7" 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-grid" aria-label="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-v2" 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">Open SVG</a>
<a href="data/single_episode_task_model_radar.json">Open JSON</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 SVG and JSON.</p>
<img src="assets/charts/episode128_task_model_radar.svg?v=xperience10m-split-radar-v2" 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">Open SVG</a>
<a href="data/episode128_task_model_radar.json">Open JSON</a>
<a href="data/task_method_20_gap_audit.json">Gap audit</a>
</div>
</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>
<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 needed for stronger model-quality numbers.</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="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>
<div class="result-reading-order" aria-label="How to read the result evidence">
<article class="result-reading-step">
<span>01</span>
<strong>Choose the line</strong>
<p>Use 1 episode for task-lab claims. Use 128 episodes for comparison claims.</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-claim">
<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-claim">
<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 JSON</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 JSON</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 JSON</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 JSON</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 JSON</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 JSON</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 <code>task_method_20_result_matrix.json</code> 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 JSON</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">Markdown table</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-body">
<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 readers do not over-read it.</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>
<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 claim boundary for a group of results." title="A claim boundary for a group of results.">i<span class="term-tooltip" role="tooltip">A claim boundary 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 readers do not over-read it.</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>
</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="models" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1">
<div class="wrap">
<div class="section-head">
<h2>Small baselines, no hidden machinery.</h2>
<p>Motion-only and current all-feature classifiers use lightweight heads so the comparison stays readable on a laptop and easy to inspect. The neural run keeps the same features and splits, then swaps in PyTorch MLP heads.</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>
</section>
<section id="neural" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1">
<div class="wrap">
<div class="section-head">
<h2>Neural MLP heads, same task contracts.</h2>
<p>The neural baseline uses small PyTorch MLP classifiers/regressors on the same 8,546-dimensional windows, chronological splits, and leakage filters. This isolates the value of a nonlinear head before moving to heavier Qwen/Omni experiments.</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>
</section>
<section id="directions" data-project-tab="directions" role="tabpanel" aria-labelledby="tab-directions" tabindex="-1">
<div class="wrap">
<div class="section-head">
<h2>The walkthrough-backed tasks organized into four research directions.</h2>
<p>Each task is mapped as direct, proxy, or diagnostic evidence for the Ropedia research tracks. The mapping uses two current baselines: minimal interpretable heads and neural MLP heads over the same feature contract.</p>
</div>
<div class="direction-grid">
<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">proxy tasks only</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">early proxy tasks</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">
<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>
</section>
<section id="extensions" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1">
<div class="wrap">
<div class="section-head">
<h2>Unified 20-task evidence and provenance.</h2>
<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 20-task JSON, per-task metrics, prediction/rank files, Markdown summaries, radar charts, and the 180-record method-task matrix.</p>
<p><a href="data/task_suite_20.json">Open unified 20-task JSON</a> · <a href="data/task_method_20_result_matrix.json">Open 180-record matrix</a> · <a href="assets/charts/unified_task_model_radar.svg">Open 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 JSON</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, JSON metrics, a Markdown summary, and a website chart 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>
</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 Ropedia Xperience-10M task heads">
</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">
<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="tasks" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1">
<div class="wrap">
<div class="section-head">
<h2>Task cards and metrics.</h2>
<p>All 20 task contracts are shown together with readable research names, representative modality thumbnails, explicit input-process-output contracts, and verified minimal versus neural scores. Rich interactive walkthroughs are available for the first 12 task cards; the remaining cards use the same unified task JSON contract.</p>
</div>
<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>
</section>
<section id="features" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1">
<div class="wrap">
<div class="section-head">
<h2>Every model input has a source.</h2>
<p>The point is not hidden complexity. Every input group maps back to a source modality and a manifest entry.</p>
</div>
<img class="chart" src="assets/charts/feature_blocks.svg" alt="All modality source chart">
</div>
</section>
<section id="diagnostics" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1">
<div class="wrap">
<div class="section-head">
<h2>Diagnostics separate memorization from signal.</h2>
<p>The charts make the main lesson visible: within-episode supervised labels are 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>
</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 overloaded terms.</h2>
<p>These are the terms readers most often confuse when moving between the repo, website, Hugging Face mirrors, result matrices, and model-package cards. The full glossary is mirrored as Markdown and JSON.</p>
</div>
<div class="glossary-panel">
<div class="glossary-summary" aria-label="Glossary categories">
<article>
<small>scope</small>
<strong>Separate data, claims, and mirrors</strong>
<p>Evidence lines, public-safe artifacts, and gated upstream data are different objects. The glossary keeps those boundaries visible.</p>
</article>
<article>
<small>results</small>
<strong>Read scores by source type</strong>
<p>Direct scores, compact-proxy scores, gap audits, and task-method records should not be interpreted as the same kind of evidence.</p>
</article>
<article>
<small>models</small>
<strong>Keep branches distinct</strong>
<p>Minimal/NN heads, metadata/raw baselines, Qwen3-Omni v1-v6, Cosmos3-Super, Cosmos3-Nano, LoRA adapters, and full-parameter gates each mean something specific.</p>
</article>
</div>
<div>
<div class="glossary-table-wrap">
<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>
<tr><td>Evidence line</td><td>A claim boundary for results.</td><td>Line 1 is the public sample episode; Line 2 is selected-128 held-out comparison.</td><td>Qwen v1-v6 run versions.</td></tr>
<tr><td>Public sample episode</td><td>The one fully inspectable official sample episode.</td><td>Raw-file browsing, task construction, single-episode baselines.</td><td>Multi-episode generalization.</td></tr>
<tr><td>Selected 128 episodes</td><td>Public-safe derived features linked to official gated episode paths.</td><td>Same-split Line 2 baseline/model comparisons.</td><td>Redistributed raw MP4/HDF5/RRD files.</td></tr>
<tr><td>20-frame window</td><td>A fixed short clip slice used as a model input unit.</td><td>Feature rows, labels, tasks, and many baseline heads.</td><td>A full episode.</td></tr>
<tr><td>Task-method record</td><td>One method evaluated on one task.</td><td>The 9 x 20 public matrix, now 180 scored records.</td><td>A single prediction row.</td></tr>
<tr><td>Direct score</td><td>A metric computed against the task target directly.</td><td>Primary interpretation in the result matrix.</td><td>Compact-proxy score.</td></tr>
<tr><td>Compact-proxy score</td><td>A bounded proxy when the direct raw target is not public.</td><td>Explicitly marked cells in the gap audit and matrix.</td><td>A direct target measurement.</td></tr>
<tr><td>Raw metric value</td><td>The original value emitted by the runner or verified package.</td><td>The value to cite from the 180-result table.</td><td>Normalized radar value.</td></tr>
<tr><td>Normalized radar value</td><td>A 0-1 plotting value used only for comparable radar polygons.</td><td>Visual comparison across metrics with different scales.</td><td>The raw metric value to cite.</td></tr>
<tr><td>Minimal baseline</td><td>A simple non-neural task head; the "minimum" reference row in casual wording.</td><td>Single-episode lower-complexity comparison.</td><td>Selected-128 Simple baseline rows.</td></tr>
<tr><td>Simple baseline</td><td>A non-neural selected-128 baseline family.</td><td>Metadata/text and raw-feature 128-episode comparisons before NN/foundation rows.</td><td>The single-episode Minimal baseline.</td></tr>
<tr><td>Qwen3-Omni v6</td><td>The current public Qwen 20-task row.</td><td>Qwen3-Omni LoRA plus task-specific probes.</td><td>All Qwen v1-v6 experiments.</td></tr>
<tr><td>Cosmos3-Super</td><td>The larger Cosmos-style branch.</td><td>Reasoner diagnostics and a verified forward-dynamics LoRA branch.</td><td>Cosmos3-Nano Future Window.</td></tr>
<tr><td>LoRA adapter</td><td>Lightweight trainable adapter weights.</td><td>Public model-branch artifacts when verified.</td><td>Full base-model weights.</td></tr>
<tr><td>HF artifact dataset</td><td>Hugging Face dataset repo for derived evidence.</td><td>Reports, metrics, website JSON, sanitized result packages.</td><td>The upstream Xperience-10M dataset.</td></tr>
<tr><td>Mirror parity</td><td>A check that public copies match source files.</td><td>Verifying GitHub, website, and HF mirrors.</td><td>A model-quality metric.</td></tr>
</tbody>
</table>
</div>
<div class="glossary-links">
<a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/GLOSSARY.md">Open full glossary</a>
<a href="data/glossary.json">Open glossary JSON</a>
<a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PUBLIC_READER_MAP.md">Reader map</a>
<a href="data/task_method_20_gap_audit.json">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>Research artifacts for the next experiments.</h2>
<p>Metrics, predictions, manifests, lightweight model weights, and derived window artifacts are organized so the project can be inspected, extended, and scaled before rerunning the full pipeline. 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>download</small>
<strong>Find the right public surface</strong>
<p>Open GitHub, HF Space, artifact dataset, baseline models, or consolidated weights/results without guessing.</p>
<a href="#artifact-panel-public-surfaces">Open public surfaces</a>
</article>
<article class="resource-mode">
<small>verify</small>
<strong>Check claims and parity</strong>
<p>Use validators, source alignment, mirror parity, and live URL/hash checks before trusting a number.</p>
<a href="#artifact-panel-checks">Open checks</a>
</article>
<article class="resource-mode">
<small>reproduce</small>
<strong>Run the task pipeline</strong>
<p>Start from scripts, windows, feature manifests, task contracts, and minimal/neural result outputs.</p>
<a href="#run">Open commands</a>
</article>
<article class="resource-mode">
<small>scale</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>Public Surfaces</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>validators 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>Public surfaces</span><h3>Project map, mirrors, and runnable code</h3></div>
<p>Use these files to navigate the whole project, open the published mirrors, or reproduce the public-sample pipeline.</p>
</div>
<div class="artifact-grid">
<article class="artifact primary-artifact"><div><h3>Public reader map</h3><p>Single navigation view for GitHub, GitHub Pages, HF Space, artifact dataset, baseline model repo, Qwen3-Omni/Cosmos3 repos, and public claim boundaries.</p></div><a href="data/public_reader_map.json">reader map</a></article>
<article class="artifact primary-artifact"><div><h3>Glossary</h3><p>Definitions for evidence lines, windows, direct/proxy scores, Qwen v1-v6, Cosmos3 branches, adapters, and public mirror terms.</p></div><a href="data/glossary.json">glossary JSON</a><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/GLOSSARY.md">Markdown</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 through the project for readers who want the shortest path from scope to results after choosing a surface.</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 diagnostic held-out pilot. Stronger model-quality metrics require 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">task_suite_enhancement_128.json</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>This tab now does one job: show the audit files that prove the public pages, mirrors, and package contents are internally consistent.</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, data JSONs, 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>Public surface QA</h3><p>Records public-surface readiness, reader-map links, live status pointers, and cross-repo publication checks.</p><a href="data/public_surface_qa.json">surface 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 dataset claims 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 readers rely 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">task_suite_enhancement_128.json</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="run" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1">
<div class="wrap">
<div class="section-head">
<h2>Reproduce the suite.</h2>
<p>Raw Xperience-10M data is not redistributed here. The reproduction guide states the commands, expected outputs, exact-match reproduction record, and multi-episode requirements.</p>
</div>
<div class="artifact-grid">
<article class="artifact"><h3>Reproducibility guide</h3><p>Human-readable commands, expected artifacts, and current scope for the public single-episode pipeline.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/REPRODUCIBILITY.md">reproducibility guide</a></article>
<article class="artifact"><h3>Reproducibility matrix</h3><p>Machine-readable command matrix covering sample download, baselines, the unified 20-task suite, figures, and validation.</p><a href="data/reproducibility_matrix.json">reproducibility matrix</a></article>
<article class="artifact"><h3>Exact-match reproduction record</h3><p>The last metric rebuild reproduced the public-sample outputs from a fresh cache and matched the committed metrics.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/notes/reproducibility_audit.md">reproduction audit</a></article>
<article class="artifact"><h3>Project dashboard</h3><p>The website organizes the dataset sample, tasks, methods, results, directions, and scale-up path in one tabbed reader flow.</p><a href="#artifacts">project materials</a></article>
<article class="artifact"><h3>Line 2 model status</h3><p>The comparison JSON groups selected-128 baselines, Qwen3-Omni v6 LoRA, Cosmos3-Nano Future Window, and Cosmos3-Super Reasoner. Full Qwen v1-v6 detail stays in a separate lineage audit.</p><a href="data/omni_model_comparison.json">comparison</a><a href="data/qwen3_omni_run_lineage.json">Qwen v1-v6</a></article>
</div>
<p class="repro-note">Minimal path: install the toolkit dependencies, download the official sample, run the task suite with neural heads, regenerate the historical provenance bundle, build the unified 20-task index, regenerate visualizations, then rebuild the supporting project reports.</p>
<pre class="code-panel"><button type="button" data-copy="setup">Copy</button><code id="setup">git clone https://github.com/Ropedia/HOMIE-toolkit.git
python3.12 -m venv .venv
source .venv/bin/activate
pip install -r HOMIE-toolkit/requirements.txt huggingface_hub hf_xet
git clone https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite.git
pip install -r ropedia-xperience-10m-task-suite/requirements.txt
pip install torch
hf download ropedia-ai/xperience-10m-sample \
--repo-type dataset \
--local-dir data/sample/xperience-10m-sample
cd ropedia-xperience-10m-task-suite
export WORKSPACE=/path/to/workspace
python scripts/episode_task_suite.py --workspace "$WORKSPACE" --include-neural
python scripts/research_direction_extension_tasks.py
python scripts/tier2_task_suite.py --workspace "$WORKSPACE"
python scripts/build_unified_task_suite.py
python scripts/task_walkthroughs.py
python scripts/build_evaluation_protocol.py
python scripts/generate_visualizations.py
python scripts/render_overview_figures.py
python scripts/render_task_suite_infographic.py
python scripts/export_modality_atlas_assets.py
python scripts/validate_website_integrity.py
python scripts/validate_scope_claims.py
python scripts/build_artifact_index.py
python scripts/validate_mirror_parity.py
python scripts/validate_publication_package.py</code></pre>
</div>
</section>
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observeXperienceReveal(document);
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initPageMotion();
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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 familyAccent = {
supervised: "#9bdfff",
forecast: "#ccffa0",
retrieval: "#7ae5c3",
diagnostic: "#d8f4a5"
};
let taskEntries = [];
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
},
{
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
}
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const sectionOrientation = document.getElementById("sectionOrientation");
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{ id: "roadmap", label: "Roadmap" },
{ id: "reading-path", label: "Reading Path" },
{ id: "dataset-card", label: "Dataset Card" },
{ id: "raw-sample", label: "Raw Sample Browser" },
{ id: "suite", label: "20-Task Suite" },
{ id: "walkthroughs", label: "Interactive Walkthrough" },
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{ id: "pipeline", label: "Pipeline" },
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{ id: "extensions", label: "Unified Task Evidence" },
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{ id: "artifacts", label: "Research Artifacts" },
{ id: "omni-scale-up", label: "Omni Scale-Up" },
{ id: "run", label: "Reproduce" }
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const sectionLabels = Object.fromEntries(sectionConfig.map((section) => [section.id, section.label]));
const sectionDescriptions = {
overview: "Best for first-time readers: scope, public boundaries, and recommended entry paths.",
roadmap: "Best for planning: what has shipped and what the next experiments should do.",
"reading-path": "Best for choosing an order through the repo, website, and HF surfaces.",
"dataset-card": "Best for source alignment and public-sample boundaries.",
"raw-sample": "Best for inspecting the sample files, media previews, and file relationships.",
suite: "Best for the unified 20-task contracts, radar, and score matrix.",
walkthroughs: "Best for case-study style task explanations.",
tasks: "Best for task-by-task input, output, and metric cards.",
pipeline: "Best for understanding how raw episode data becomes features and results.",
protocol: "Best for splits, leakage controls, metrics, and evaluation rules.",
architectures: "Best for how task heads and model tracks are organized.",
features: "Best for modality and feature provenance.",
takeaways: "Best for the fastest read on what the current metrics mean.",
models: "Best for minimal baseline evidence.",
neural: "Best for neural MLP task-head comparisons.",
directions: "Best for mapping tasks to spatial, world-model, and VLA directions.",
extensions: "Best for historical provenance links inside the unified 20-task surface.",
diagnostics: "Best for charts and error-analysis evidence.",
glossary: "Best for terms that can be confused across data, tasks, models, metrics, and public mirrors.",
artifacts: "Best for finding files, mirrors, weights, scripts, and checks.",
evidence: "Best for current experiment status and milestones.",
"omni-scale-up": "Best for Qwen3-Omni and Cosmos3 status.",
run: "Best for reproduction commands."
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const tabLabels = Object.fromEntries(
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tabButtons.map((button) => [button.dataset.tabKey, button.dataset.defaultSection])
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function orderedSectionsForTab(tabKey) {
const configured = sectionConfig
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const configuredIds = new Set(configured.map((section) => section.id));
const unconfigured = tabSections.filter((section) => (
section.dataset.projectTab === tabKey && !configuredIds.has(section.id)
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function labelForSection(section) {
return sectionLabels[section.id] ||
section.querySelector("h2")?.textContent?.replace(/\.$/, "") ||
section.id.replaceAll("-", " ");
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function updateSectionOrientation(tabKey, sectionId) {
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const tabLabel = tabLabels[tabKey] || tabKey;
const sectionLabel = sectionLabels[sectionId] || labelForSection(document.getElementById(sectionId));
const description = sectionDescriptions[sectionId] || "Evidence, files, and source links for this part of the suite.";
sectionOrientation.innerHTML = `
<strong>${tabLabel} / ${sectionLabel}</strong>
<span>${description}</span>
<a href="#reader-map">Reader map</a>
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}
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
});
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button.addEventListener("keydown", (event) => {
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pushHash: true,
scroll: true,
smooth: true
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const requestedId = options.targetId || tabDefaultSections[fallbackTab] || "overview";
const targetId = document.getElementById(requestedId)
? requestedId
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tabSections.forEach((section) => {
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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);
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history.pushState(null, "", `#${targetId}`);
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requestAnimationFrame(() => {
document.getElementById(targetId)?.scrollIntoView({
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block: "start"
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function activateTabForHash(options = {}) {
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setProjectTab(tabKey, { targetId, scroll: options.scroll, smooth: options.smooth });
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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;
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nextButton.focus();
setProjectTab(nextButton.dataset.tabKey, {
targetId: nextButton.dataset.defaultSection,
pushHash: true,
scroll: true,
smooth: true
});
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tabButtons.forEach((button, index) => {
button.addEventListener("click", () => {
setProjectTab(button.dataset.tabKey, {
targetId: button.dataset.defaultSection,
pushHash: true,
scroll: true,
smooth: true
});
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button.addEventListener("keydown", (event) => {
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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();
});
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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)} ${units[unitIndex]}`;
}
function setRawSampleFile(file) {
const video = document.getElementById("rawVideo");
const audio = document.getElementById("rawAudio");
const videoFrame = document.getElementById("rawVideoFrame");
const audioFrame = document.getElementById("rawAudioFrame");
const sourceLink = document.getElementById("rawOpenSource");
const previewLink = document.getElementById("rawOpenPreview");
const previewNote = document.getElementById("rawPreviewNote");
const mediaUrl = file.previewUrl || file.url;
document.getElementById("rawFileTitle").textContent = file.name;
document.getElementById("rawFileDescription").textContent = file.description;
document.getElementById("rawKindPill").textContent = `${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;
}
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();
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 readers do not over-read it.",
"proxy": "A bounded proxy metric when a direct raw target is not publicly available. It stays explicit so readers do not over-read it.",
"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 claim boundary 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 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>${infoIcon(term, termDefinition(term))}</span>`;
}
function methodGlossaryTerms(method) {
const text = `${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, glossaryResponse] = await Promise.all([
fetch("data/task_method_20_result_matrix.json", { cache: "no-cache" }),
fetch("data/glossary.json", { cache: "no-cache" }).catch(() => null)
]);
if (!response.ok) throw new Error(`matrix ${response.status}`);
if (glossaryResponse?.ok) registerGlossaryTerms(await glossaryResponse.json());
renderResultScoreTable(await response.json());
} catch (error) {
table.querySelector("tbody").innerHTML = '<tr><td colspan="2">Result matrix could not be loaded. Open the JSON link above.</td></tr>';
}
}
const metricBarWidth = (task) => {
const value = Number(task.metric?.neural_mlp ?? task.metric?.minimal ?? 0);
if (!Number.isFinite(value)) return 4;
if (task.metric?.name === "R2") return Math.max(4, Math.min(100, (value + 1) * 50));
if (task.metric?.direction === "lower") return Math.max(4, Math.min(100, 100 / (1 + Math.max(value, 0))));
return Math.max(4, Math.min(100, value * 100));
};
const normalizeWalkthroughTasks = (payload) => Object.values(payload.tasks || {});
const normalizeSuiteTasks = (payload) => Array.isArray(payload.tasks) ? payload.tasks : Object.values(payload.tasks || {});
const familyFromSuiteTask = (task) => {
const id = task.task_id || "";
const text = `${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 || ""} ${task.task_id || ""}`.toLowerCase();
const modalities = [];
if (text.includes("video") || text.includes("camera") || text.includes("view") || text.includes("fisheye")) modalities.push("video");
if (text.includes("audio")) modalities.push("audio");
if (text.includes("depth")) modalities.push("depth");
if (text.includes("pose") || text.includes("slam")) modalities.push("pose_slam");
if (text.includes("hand") || text.includes("body") || text.includes("mocap")) modalities.push("motion_capture");
if (text.includes("imu") || text.includes("inertial") || text.includes("gyroscope")) modalities.push("inertial");
if (text.includes("language") || text.includes("caption") || text.includes("text") || text.includes("interaction")) modalities.push("language");
return modalities.length ? Array.from(new Set(modalities)) : ["video", "motion_capture", "inertial"];
};
const synthesizeTaskCard = (task) => {
const modalities = modalitiesFromSuiteTask(task);
const family = familyFromSuiteTask(task);
return {
display_name: task.task_display_name || task.task_id || "Task",
research_name: `${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 JSON 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: ${(task.artifact_sources && (task.artifact_sources.minimal_metrics || task.artifact_sources.walkthrough || task.artifact_sources.legacy_result_directory)) || "docs/data/task_suite_20.json"}.`
],
metric: {
name: task.metric_name || task.metric_key || "metric",
direction: task.metric_direction || "higher",
minimal: task.minimal_primary_metric,
neural_mlp: task.neural_primary_metric
},
failure_mode: "single-episode chronological split; read the 180-record matrix for selected-128 method comparisons"
};
};
const mergeUnifiedTaskCards = (walkthroughPayload, suitePayload) => {
const walkthroughTasks = normalizeWalkthroughTasks(walkthroughPayload);
const walkthroughById = Object.fromEntries(
walkthroughTasks.map((task) => [task.task_id || task.artifact_id || task.display_name, task])
);
const suiteTasks = normalizeSuiteTasks(suitePayload);
return suiteTasks.map((task) => {
const rich = walkthroughById[task.task_id];
if (rich) {
return {
...rich,
display_name: task.task_display_name || rich.display_name,
research_name: `${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: ${task.failure_mode}`
];
}
function renderTaskCards() {
const grid = document.getElementById("taskGrid");
grid.innerHTML = taskEntries.map((task, index) => {
const family = task.task_family;
const poster = modalityMeta[task.poster_modality] || modalityMeta.video;
const minimal = formatMetric(task.metric?.minimal);
const neural = formatMetric(task.metric?.neural_mlp);
return `
<button type="button" class="task-card" data-kind="${escapeHtml(family)}" data-index="${index}" aria-pressed="false">
<span class="task-card-media" aria-hidden="true">
<img src="${poster.src}" alt="">
</span>
<div class="task-top">
<span>
<span class="task-name">${escapeHtml(task.display_name)}</span>
<span class="task-research-name">${escapeHtml(task.research_name)}</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="metric-row">
<span><strong>${minimal}</strong>minimal ${escapeHtml(task.metric?.name)}</span>
<span><strong>${neural}</strong>neural MLP</span>
</div>
<div class="mini-bar"><span style="--w:${metricBarWidth(task).toFixed(1)}%;--c:${familyAccent[family] || "#ccffa0"}"></span></div>
</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) => `
<button type="button" class="selector-button" data-index="${index}" aria-pressed="false">
<strong>${escapeHtml(task.display_name)}</strong>
<span>${escapeHtml(task.task_family)}</span>
</button>
`).join("");
selector.querySelectorAll(".selector-button").forEach((button) => {
button.addEventListener("click", () => {
pausePlayer();
setActiveTask(Number(button.dataset.index));
});
});
}
function renderPlayer(task, index) {
const poster = modalityMeta[task.poster_modality] || modalityMeta.video;
document.getElementById("playerPoster").src = poster.src;
document.getElementById("playerPoster").alt = `${task.display_name} representative ${poster.label} modality`;
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").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] = await Promise.all([
fetch("data/task_walkthroughs.json", { cache: "no-cache" }),
fetch("data/task_suite_20.json", { cache: "no-cache" })
]);
if (!walkthroughResponse.ok) throw new Error(`walkthrough data ${walkthroughResponse.status}`);
if (!suiteResponse.ok) throw new Error(`suite data ${suiteResponse.status}`);
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();
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>
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