Add files using upload-large-folder tool

assets/foundation-pipelines/prompts.md

@@ -8,9 +8,9 @@ manifests and mirrors already link here as the provenance note.
 Update on 2026-06-19: the latest supplied clean Spatial intelligence and
 Human-video world model PNGs are byte-identical to the committed source-slide
 cache and are published as 2560-pixel public images. The third uploaded image
-duplicates the Spatial intelligence PNG, so the Vision-language-action card
-continues to use the restored original presentation photo until a clean VLA
-slide PNG is supplied.
+duplicates the Spatial intelligence PNG, so the Vision-language-action card is
+published as a clean deterministic slide redraw from the original VLA
+presentation-photo content.
 
 | Track | Source | Enhanced public PNG |
 | --- | --- | --- |
@@ -25,8 +25,11 @@ Restoration is deterministic and local:
 - Autocontrast and moderate brightness/color/contrast correction.
 - Lanczos resize to a 2560-pixel public width.
 - Gentle sharpening and unsharp masking.
+- For VLA only, a deterministic clean slide redraw preserves the visible
+  presentation content from the source photo while matching the clean black and
+  lime public-slide style.
 
-The restoration script deliberately does not synthesize, redraw, or hallucinate
-slide text. Technical task/training/evaluation claims are maintained in
+The restoration script deliberately avoids hallucinated model claims or
+non-source concept art. Technical task/training/evaluation claims are maintained in
 `THREE_FOUNDATION_PIPELINES.md` and
 `docs/data/three_foundation_pipelines.json`.

data/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-18T16:39:29+00:00",
+  "generated_at_utc": "2026-06-18T17:18:54+00:00",
   "status": "pass",
   "artifact_count": 213,
   "missing": [],
@@ -136,8 +136,8 @@
       "surface": "repo_hf",
       "shows": "Frames spatial intelligence, human-video world modeling, and vision-language-action as three pipeline tracks with explicit inputs, outputs, maturity, and next evidence gates.",
       "exists": true,
-      "bytes": 8179,
-      "sha256": "ed390388e996fbe9b1b663cb98420ea658a59930b453aa7215859e9a05c2d0ef"
+      "bytes": 8234,
+      "sha256": "bad51748b2f9461d1246730131ee348714cfef146aae7080348f6d6b8d72ab22"
     },
     {
       "id": "three_foundation_pipelines_json",
@@ -147,8 +147,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable pipeline-track contract for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 10531,
-      "sha256": "30a261a912b172fee7433d81e2b36390554afa8f2bed009d5c126f3f2583b077"
+      "bytes": 10520,
+      "sha256": "be07a1a44100047181636efe4fce53248d61df6ec0b76f341652032e45ef252e"
     },
     {
       "id": "spatial_intelligence_slide_diagram",
@@ -180,8 +180,8 @@
       "surface": "website_hf",
       "shows": "High-resolution slide diagram for the vision-language-action training pipeline direction.",
       "exists": true,
-      "bytes": 2911014,
-      "sha256": "d4704ee28f747067c440845905cf2cacf6cbbf3fd5d17418ba16993f617ade29"
+      "bytes": 156793,
+      "sha256": "43f2c9f741319abb64d652aa3cf52d35d740b6890efc7be099c701ebd01b4018"
     },
     {
       "id": "spatial_intelligence_source_slide",
@@ -1017,7 +1017,7 @@
       "shows": "Catalogs public figures, charts, modality thumbnails, dimensions, hashes, roles, and source scripts.",
       "exists": true,
       "bytes": 6983,
-      "sha256": "84c4040a5a4dadeae164599fc937814cbb9d6a129593bd7c1bbd0614e36fa993"
+      "sha256": "48ea04c063df0745f2a31483d15baa71d420906b2ad7ce15fdb10760f41907e6"
     },
     {
       "id": "figure_index_json",
@@ -1027,8 +1027,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable visual asset index for website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 19441,
-      "sha256": "9b872e3f7e6c62f93cb3e38748f5918005a9b3f6ef3033eddbab9b7afa980d4a"
+      "bytes": 19440,
+      "sha256": "f9616f6254547e0483b43d498fb4256546e4d5f844f5934934ba78a8adba70bb"
     },
     {
       "id": "figure_index_builder",
@@ -1310,7 +1310,7 @@
       "volatile": true,
       "shows": "Confirms prepared GitHub/HF Space/artifact/model mirrors share the same critical data, figure, website HTML, and validator files.",
       "exists": true,
-      "bytes": 922005,
+      "bytes": 923179,
       "hash_policy": "existence_and_size_only"
     },
     {

data/figure_index.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Figure Index",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T16:36:15+00:00",
+  "generated_at_utc": "2026-06-18T17:18:54+00:00",
   "scope": "Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience-10M videos, annotations, RRD files, and Qwen weights are excluded.",
   "figure_count": 29,
   "figures": [
@@ -149,12 +149,12 @@
       "source_script": "scripts/render_foundation_pipeline_diagrams.py",
       "surface": "README, website, HF Space, artifact dataset, model card",
       "exists": true,
-      "bytes": 2911014,
-      "sha256": "d4704ee28f747067c440845905cf2cacf6cbbf3fd5d17418ba16993f617ade29",
+      "bytes": 156793,
+      "sha256": "43f2c9f741319abb64d652aa3cf52d35d740b6890efc7be099c701ebd01b4018",
       "dimensions": {
         "format": "PNG",
         "width": 2560,
-        "height": 1919
+        "height": 1920
       },
       "source_script_exists": true
     },

data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T16:39:28+00:00",
+  "generated_at_utc": "2026-06-18T17:18:53+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
@@ -43,12 +43,12 @@
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-18T16:31:49+00:00"
+          "generated_at_utc": "2026-06-18T16:39:43+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-18T15:39:07+00:00"
+          "generated_at_utc": "2026-06-18T16:41:09+00:00"
         }
       },
       "failures": {}

data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-18T16:39:43+00:00",
+  "generated_at_utc": "2026-06-18T17:19:27+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -237,8 +237,8 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 2673,
-      "text_file_count": 1146,
+      "file_count": 2674,
+      "text_file_count": 1147,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_128ep_dense_multiscale_hierarchical_v1_20260608/dense_multiscale_windows.jsonl",
         "bytes": 135591061
@@ -248,8 +248,8 @@
     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
-      "file_count": 3146,
-      "text_file_count": 1314,
+      "file_count": 3147,
+      "text_file_count": 1315,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_128ep_dense_multiscale_hierarchical_v1_20260608/dense_multiscale_windows.jsonl",
         "bytes": 135591061

data/three_foundation_pipelines.json

@@ -6,13 +6,13 @@
   "diagram_assets": {
     "status": "published_high_resolution_slide_diagrams",
     "asset_root": "docs/assets/foundation-pipelines",
-    "source": "Clean direction-slide PNGs where supplied, plus restored original presentation photos where a clean slide PNG is not yet available",
+    "source": "Clean direction-slide PNGs where supplied, plus a deterministic VLA slide redraw from the original presentation-photo source",
     "source_slide_root": "docs/assets/foundation-pipelines/source-slides",
     "source_photo_root": "docs/assets/foundation-pipelines/source-photos",
     "provenance_file": "docs/assets/foundation-pipelines/prompts.md",
     "renderer_script": "scripts/render_foundation_pipeline_diagrams.py",
     "diagram_type": "direction_slide_diagram",
-    "source_update": "2026-06-19: the latest supplied clean Spatial intelligence and Human-video world model PNGs are byte-identical to the committed source-slide cache and are published as 2560-pixel public images. The third uploaded file duplicates the Spatial intelligence slide, so Vision-language-action intentionally remains the restored original presentation-photo source until a clean VLA slide PNG is supplied.",
+    "source_update": "2026-06-19: the latest supplied clean Spatial intelligence and Human-video world model PNGs are byte-identical to the committed source-slide cache and are published as 2560-pixel public images. The third uploaded file duplicates the Spatial intelligence slide, so Vision-language-action is published as a clean 2560-pixel deterministic redraw from the original VLA presentation-photo content.",
     "note": "Images are slide-diagram communication assets for pipeline tracks. Technical claims remain governed by the Markdown/JSON contracts and verified metrics."
   },
   "shared_principles": [

data/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-18T16:38:48+00:00",
+  "generated_at_utc": "2026-06-18T17:19:13+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -81,7 +81,7 @@
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
       "overview_index": 95702,
-      "evidence_index": 132075
+      "evidence_index": 132076
     },
     {
       "name": "project_status_links_json",
@@ -160,8 +160,8 @@
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
       "overview_index": 95702,
-      "protocol_index": 128256,
-      "evidence_index": 132075
+      "protocol_index": 128257,
+      "evidence_index": 132076
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -301,7 +301,7 @@
     },
     {
       "path": "data/artifact_index.json",
-      "bytes": 116109,
+      "bytes": 116108,
       "top_level_type": "dict"
     },
     {
@@ -331,7 +331,7 @@
     },
     {
       "path": "data/figure_index.json",
-      "bytes": 19441,
+      "bytes": 19440,
       "top_level_type": "dict"
     },
     {
@@ -351,7 +351,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 922005,
+      "bytes": 923179,
       "top_level_type": "dict"
     },
     {
@@ -516,7 +516,7 @@
     },
     {
       "path": "data/three_foundation_pipelines.json",
-      "bytes": 10531,
+      "bytes": 10520,
       "top_level_type": "dict"
     },
     {
@@ -664,9 +664,9 @@
     {
       "path": "assets/foundation-pipelines/vision-language-action-pipeline.png",
       "exists": true,
-      "bytes": 2911014,
+      "bytes": 156793,
       "width": 2560,
-      "height": 1919,
+      "height": 1920,
       "format": "PNG"
     },
     {

scripts/render_foundation_pipeline_diagrams.py

@@ -4,16 +4,19 @@
 The public foundation-direction visuals intentionally use the direction-slide
 sources provided by the project owner, not generated concept art. Clean slide
 PNGs are used directly when available; older photo sources are restored only as
-fallbacks. The output asset names stay stable for the website, README, and HF
+fallbacks. The VLA clean slide is a deterministic redraw from the supplied
+presentation photo because the latest third clean PNG duplicated the Spatial
+slide. The output asset names stay stable for the website, README, and HF
 mirrors.
 """
 
 from __future__ import annotations
 
 from dataclasses import dataclass
+import math
 from pathlib import Path
 
-from PIL import Image, ImageEnhance, ImageFilter, ImageOps
+from PIL import Image, ImageDraw, ImageEnhance, ImageFilter, ImageFont, ImageOps
 
 
 ROOT = Path(__file__).resolve().parents[1]
@@ -22,6 +25,13 @@ SOURCE_DIR = OUT_DIR / "source-photos"
 SOURCE_SLIDE_DIR = OUT_DIR / "source-slides"
 
 TARGET_WIDTH = 2560
+TARGET_HEIGHT = 1920
+LIME = (142, 255, 45)
+LIME_SOFT = (190, 255, 126)
+WHITE = (246, 248, 244)
+MUTED = (205, 211, 207)
+BLUE = (71, 178, 255)
+BG = (0, 0, 0)
 
 
 @dataclass(frozen=True)
@@ -34,6 +44,7 @@ class PhotoAsset:
     contrast: float
     color: float
     sharpness: float
+    clean_vla_redraw: bool = False
 
 
 PHOTOS = [
@@ -66,11 +77,201 @@ PHOTOS = [
         contrast=1.18,
         color=1.09,
         sharpness=1.34,
+        clean_vla_redraw=True,
     ),
 ]
 
 
+def font(size: int, weight: str = "regular") -> ImageFont.FreeTypeFont:
+    candidates = {
+        "regular": [
+            "/System/Library/Fonts/Supplemental/Arial.ttf",
+            "/System/Library/Fonts/Helvetica.ttc",
+        ],
+        "bold": [
+            "/System/Library/Fonts/Supplemental/Arial Bold.ttf",
+            "/System/Library/Fonts/HelveticaNeue.ttc",
+        ],
+        "black": [
+            "/System/Library/Fonts/Supplemental/Arial Black.ttf",
+            "/System/Library/Fonts/Supplemental/Arial Bold.ttf",
+        ],
+        "mono": [
+            "/System/Library/Fonts/Menlo.ttc",
+            "/System/Library/Fonts/SFNSMono.ttf",
+            "/System/Library/Fonts/Supplemental/Arial.ttf",
+        ],
+    }[weight]
+    for candidate in candidates:
+        path = Path(candidate)
+        if path.is_file():
+            return ImageFont.truetype(str(path), size=size)
+    return ImageFont.load_default()
+
+
+def text(draw: ImageDraw.ImageDraw, xy: tuple[int, int], value: str, size: int, fill=WHITE, weight: str = "regular") -> None:
+    draw.text(xy, value, font=font(size, weight), fill=fill)
+
+
+def fitted_text(
+    draw: ImageDraw.ImageDraw,
+    xy: tuple[int, int],
+    value: str,
+    max_width: int,
+    size: int,
+    fill=WHITE,
+    weight: str = "regular",
+    min_size: int = 36,
+) -> None:
+    chosen = size
+    while chosen > min_size:
+        fnt = font(chosen, weight)
+        bbox = draw.textbbox((0, 0), value, font=fnt)
+        if bbox[2] - bbox[0] <= max_width:
+            break
+        chosen -= 2
+    draw.text(xy, value, font=font(chosen, weight), fill=fill)
+
+
+def centered_text(
+    draw: ImageDraw.ImageDraw,
+    box: tuple[int, int, int, int],
+    value: str,
+    size: int,
+    fill=WHITE,
+    weight: str = "regular",
+) -> None:
+    x0, y0, x1, y1 = box
+    fnt = font(size, weight)
+    bbox = draw.textbbox((0, 0), value, font=fnt)
+    x = x0 + (x1 - x0 - (bbox[2] - bbox[0])) / 2
+    y = y0 + (y1 - y0 - (bbox[3] - bbox[1])) / 2 - 2
+    draw.text((x, y), value, font=fnt, fill=fill)
+
+
+def arrow(draw: ImageDraw.ImageDraw, start: tuple[int, int], end: tuple[int, int], fill=LIME, width: int = 7) -> None:
+    draw.line([start, end], fill=fill, width=width)
+    sx, sy = start
+    ex, ey = end
+    angle = math.atan2(ey - sy, ex - sx)
+    head = 34
+    spread = 0.55
+    points = [
+        end,
+        (ex - head * math.cos(angle - spread), ey - head * math.sin(angle - spread)),
+        (ex - head * math.cos(angle + spread), ey - head * math.sin(angle + spread)),
+    ]
+    draw.polygon(points, fill=fill)
+
+
+def rounded(draw: ImageDraw.ImageDraw, box: tuple[int, int, int, int], outline=LIME, width: int = 3, radius: int = 22) -> None:
+    draw.rounded_rectangle(box, radius=radius, outline=outline, width=width)
+
+
+def render_ropedia_header(draw: ImageDraw.ImageDraw) -> None:
+    draw.rounded_rectangle((58, 64, 112, 118), radius=8, fill=WHITE)
+    draw.ellipse((77, 82, 93, 98), fill=BG)
+    text(draw, (132, 62), "Ropedia", 56, WHITE, "bold")
+    draw.line((58, 168, 2502, 168), fill=(190, 196, 190), width=3)
+
+
+def render_vla_clean_slide() -> Image.Image:
+    image = Image.new("RGB", (TARGET_WIDTH, TARGET_HEIGHT), BG)
+    draw = ImageDraw.Draw(image)
+    render_ropedia_header(draw)
+
+    fitted_text(draw, (58, 240), "Train Vision-Language-Action Models", 2440, 128, WHITE, "black", 84)
+    text(draw, (62, 410), "What the robot sees and reads becomes what it does.", 48, MUTED, "regular")
+
+    # Data card.
+    card = (60, 540, 790, 1394)
+    rounded(draw, card, LIME, 3, 32)
+    text(draw, (130, 618), "OUR DATA", 34, LIME, "mono")
+    text(draw, (130, 700), "Xperience-10M", 72, WHITE, "black")
+
+    rows = [
+        ("video", "Egocentric video"),
+        ("body", "Hand & body motion"),
+        ("caption", "Language captions"),
+    ]
+    y = 910
+    for kind, label in rows:
+        x = 132
+        if kind == "video":
+            draw.rounded_rectangle((x, y - 16, x + 64, y + 32), radius=8, outline=LIME, width=4)
+            draw.polygon([(x + 25, y - 6), (x + 25, y + 22), (x + 48, y + 8)], outline=LIME, fill=None)
+        elif kind == "body":
+            draw.ellipse((x + 25, y - 28, x + 43, y - 10), outline=LIME, width=4)
+            draw.line((x + 34, y - 8, x + 34, y + 30), fill=LIME, width=5)
+            draw.line((x + 10, y + 4, x + 58, y + 4), fill=LIME, width=5)
+            draw.line((x + 34, y + 30, x + 12, y + 62), fill=LIME, width=5)
+            draw.line((x + 34, y + 30, x + 58, y + 62), fill=LIME, width=5)
+        else:
+            for offset in (0, 13, 26):
+                draw.line((x, y - 14 + offset, x + 58, y - 14 + offset), fill=LIME, width=4)
+            draw.line((x, y + 26, x + 36, y + 26), fill=LIME, width=4)
+        text(draw, (260, y - 30), label, 44, WHITE, "regular")
+        y += 145
+
+    arrow(draw, (840, 960), (910, 960), LIME, 7)
+
+    # Model/action flow.
+    text(draw, (1038, 556), "VISION + LANGUAGE -> ACTION", 34, LIME, "mono")
+    vision_box = (1085, 725, 1238, 805)
+    language_box = (1085, 902, 1238, 982)
+    rounded(draw, vision_box, WHITE, 3, 12)
+    rounded(draw, language_box, WHITE, 3, 12)
+    centered_text(draw, vision_box, "Vision", 32, WHITE, "bold")
+    centered_text(draw, language_box, "Language", 32, WHITE, "bold")
+    text(draw, (1145, 832), "+", 42, WHITE, "bold")
+    arrow(draw, (1268, 848), (1355, 848), WHITE, 5)
+
+    # Robot action chunk.
+    path = [(1548, 1052), (1662, 946), (1794, 902), (1902, 934), (2010, 858)]
+    for i in range(len(path) - 1):
+        draw.line((path[i], path[i + 1]), fill=(208, 208, 208), width=3)
+    for px, py in path[:-1]:
+        draw.ellipse((px - 8, py - 8, px + 8, py + 8), fill=WHITE)
+    for px, py in [(1662, 946), (1794, 902), (1902, 934)]:
+        draw.ellipse((px - 5, py - 5, px + 5, py + 5), fill=LIME_SOFT)
+    gripper_x, gripper_y = path[-1]
+    draw.line((gripper_x - 18, gripper_y + 4, gripper_x + 18, gripper_y - 18), fill=WHITE, width=6)
+    draw.line((gripper_x - 3, gripper_y - 6, gripper_x - 3, gripper_y - 46), fill=WHITE, width=6)
+    draw.line((gripper_x - 3, gripper_y - 20, gripper_x - 24, gripper_y - 44), fill=WHITE, width=5)
+    draw.line((gripper_x - 3, gripper_y - 20, gripper_x + 20, gripper_y - 42), fill=WHITE, width=5)
+    text(draw, (1500, 1108), "Robot action chunk", 36, WHITE, "regular")
+
+    # Bottom cards.
+    left = (60, 1472, 1195, 1788)
+    right = (1230, 1472, 2502, 1788)
+    rounded(draw, left, LIME, 3, 22)
+    rounded(draw, right, LIME, 3, 22)
+    draw.ellipse((110, 1545, 230, 1665), outline=(74, 83, 76), width=2)
+    text(draw, (255, 1570), "pi_0.7", 58, WHITE, "black")
+    draw.line((600, 1528, 600, 1732), fill=(144, 150, 145), width=2)
+    text(draw, (655, 1530), "Physical intelligence", 36, WHITE, "regular")
+    text(draw, (655, 1604), "generalist", 34, MUTED, "regular")
+    text(draw, (655, 1658), "manipulation policy", 34, MUTED, "regular")
+    text(draw, (860, 1712), "arXiv:2604.15483", 34, LIME_SOFT, "regular")
+
+    draw.ellipse((1280, 1545, 1400, 1665), outline=(74, 83, 76), width=2)
+    for px, py in [(1315, 1614), (1338, 1572), (1365, 1606), (1339, 1641)]:
+        draw.ellipse((px - 9, py - 9, px + 9, py + 9), outline=LIME, width=4)
+    draw.line((1315, 1614, 1338, 1572, 1365, 1606, 1339, 1641, 1315, 1614), fill=LIME, width=3)
+    text(draw, (1448, 1572), "Qwen-VLA", 52, WHITE, "black")
+    draw.line((1860, 1528, 1860, 1732), fill=(144, 150, 145), width=2)
+    text(draw, (1918, 1530), "Alibaba Qwen", 36, WHITE, "regular")
+    text(draw, (1918, 1604), "robot + human-ego", 34, MUTED, "regular")
+    text(draw, (1918, 1664), "co-training", 34, MUTED, "regular")
+    text(draw, (2210, 1664), "arXiv:2605.30280", 34, LIME_SOFT, "regular")
+
+    return image
+
+
 def enhance(asset: PhotoAsset) -> Image.Image:
+    if asset.clean_vla_redraw:
+        return render_vla_clean_slide()
+
     if asset.slide_source:
         slide_path = SOURCE_SLIDE_DIR / asset.slide_source
         if slide_path.is_file():