Datasets:

cy0307
/

ropedia-xperience-10m-task-suite-artifacts

@@ -85,7 +85,7 @@ retained only as stable provenance artifact locations inside the unified suite.
 - Cross-episode generalization for Qwen3-Omni has a first verified diagnostic pilot, but strong model quality is not yet shown.
 - Feature-vector reconstruction is separate from pixel depth, mesh, NeRF, or Gaussian reconstruction.
-- The final verified Qwen3-Omni diagnostic result meets the strict-JSON target, but action/subtask held-out quality remains weak and needs error analysis before larger model-quality claims.
 - Full audio-visual representation learning still needs multi-episode training; the current report includes single-episode audio/no-audio ablations.
 ## Scale-Up Gate

 - Cross-episode generalization for Qwen3-Omni has a first verified diagnostic pilot, but strong model quality is not yet shown.
 - Feature-vector reconstruction is separate from pixel depth, mesh, NeRF, or Gaussian reconstruction.
+- The final verified Qwen3-Omni diagnostic result meets the strict-JSON target, but action/subtask held-out quality remains weak and needs error analysis before presenting stronger model-quality numbers.
 - Full audio-visual representation learning still needs multi-episode training; the current report includes single-episode audio/no-audio ablations.
 ## Scale-Up Gate

GLOSSARY.md CHANGED Viewed

@@ -10,7 +10,7 @@ machine-readable form.
 | Category | What it clarifies |
 | --- | --- |
-| Dataset and scope | Which data is public, which data is gated upstream, and what each evidence line can support. |
 | Files and features | How raw sample files, derived windows, feature manifests, and public-safe artifacts relate to each other. |
 | Tasks and metrics | What a scored task row means, when a score is direct, and when a compact proxy is being used. |
 | Models and runs | How simple/NN baselines, Qwen3-Omni, Cosmos3, LoRA adapters, and full-parameter gates differ. |
@@ -21,9 +21,9 @@ machine-readable form.
 | Term | Plain meaning | In this project | Do not confuse with |
 | --- | --- | --- | --- |
 | Xperience-10M | The upstream embodied human-interaction dataset. | The source dataset behind the public sample, selected-128 features, task suite, and model diagnostics. | This repo itself; the repo only redistributes public-safe derived artifacts. |
-| Public sample episode | One officially available sample episode. | The fully inspectable Line 1 unit used for raw-file browsing, 20-frame windows, task construction, and single-episode baselines. | Multi-episode generalization. |
 | Selected 128 episodes | A public-safe selected subset of official gated episode paths. | Line 2 uses derived windows/features and keeps links back to official episode ids and gated source paths. | Redistributed raw MP4/HDF5/RRD data. |
-| 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. | Qwen run versions v1-v6, which are model-run lineage, not evidence lines. |
 | Official gated data | Upstream files that require official dataset access. | Raw Xperience-10M MP4/HDF5/RRD files and full source directories remain outside the public repo. | Public-safe metrics, derived features, figures, and manifests. |
 | Public-safe artifact | A file that can be mirrored publicly without raw gated content. | Metrics, JSON summaries, model cards, figures, derived manifests, and approved lightweight weights/adapters. | Raw dataset redistribution. |
 | Episode | One recorded interaction sequence. | The basic source unit behind windows, labels, and train/val/test splits. | A 20-frame window, which is a smaller model input slice. |

 | Category | What it clarifies |
 | --- | --- |
+| Dataset and scope | Which data is public, which data is gated upstream, and how each evidence line should be read. |
 | Files and features | How raw sample files, derived windows, feature manifests, and public-safe artifacts relate to each other. |
 | Tasks and metrics | What a scored task row means, when a score is direct, and when a compact proxy is being used. |
 | Models and runs | How simple/NN baselines, Qwen3-Omni, Cosmos3, LoRA adapters, and full-parameter gates differ. |
 | Term | Plain meaning | In this project | Do not confuse with |
 | --- | --- | --- | --- |
 | Xperience-10M | The upstream embodied human-interaction dataset. | The source dataset behind the public sample, selected-128 features, task suite, and model diagnostics. | This repo itself; the repo only redistributes public-safe derived artifacts. |
+| Public sample episode | One officially available sample episode. | The fully inspectable Line 1 unit used for raw-file browsing, 20-frame windows, task construction, and single-episode baselines. | The selected-128 comparison rows. |
 | Selected 128 episodes | A public-safe selected subset of official gated episode paths. | Line 2 uses derived windows/features and keeps links back to official episode ids and gated source paths. | Redistributed raw MP4/HDF5/RRD data. |
+| Evidence line | A reading lane for a group of results. | Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison. | Qwen run versions v1-v6, which are model-run lineage, not evidence lines. |
 | Official gated data | Upstream files that require official dataset access. | Raw Xperience-10M MP4/HDF5/RRD files and full source directories remain outside the public repo. | Public-safe metrics, derived features, figures, and manifests. |
 | Public-safe artifact | A file that can be mirrored publicly without raw gated content. | Metrics, JSON summaries, model cards, figures, derived manifests, and approved lightweight weights/adapters. | Raw dataset redistribution. |
 | Episode | One recorded interaction sequence. | The basic source unit behind windows, labels, and train/val/test splits. | A 20-frame window, which is a smaller model input slice. |

PROJECT_BRIEF.md CHANGED Viewed

@@ -11,8 +11,8 @@ The public sample is treated as a small but real research system, while the
 selected-128 line shows the first same-split scale-up comparison. The project
 does not blend those two evidence types. A reader should be able to trace one
 model input, understand each task, reproduce the public-sample results, compare
-the 128-episode method rows, and see what remains before stronger
-model-quality claims.
 ## Capability Map
@@ -58,7 +58,7 @@ metrics, visualization, and baseline code. It is not enough to measure final
 general embodied-AI model quality. Line 2 verifies the selected-128 held-out
 comparison surface and the Qwen3-Omni v6 diagnostic row; the next research
 stage is action/subtask error analysis, stronger structured-output training,
-and policy-target conversion before larger backbone claims.
 ## Best Entry Points

 selected-128 line shows the first same-split scale-up comparison. The project
 does not blend those two evidence types. A reader should be able to trace one
 model input, understand each task, reproduce the public-sample results, compare
+the 128-episode method rows, and see what remains before presenting stronger
+model-quality numbers.
 ## Capability Map
 general embodied-AI model quality. Line 2 verifies the selected-128 held-out
 comparison surface and the Qwen3-Omni v6 diagnostic row; the next research
 stage is action/subtask error analysis, stronger structured-output training,
+and policy-target conversion before broader backbone comparisons.
 ## Best Entry Points

THREE_FOUNDATION_PIPELINES.md CHANGED Viewed

@@ -1,8 +1,9 @@
 # Three Foundation Pipeline Tracks
-Xperience-10M can support the three directions shown in the presentation, but
-they should be positioned as **pipeline tracks**, not as three already solved
-model claims. The same dataset can feed all three tracks because it combines
 egocentric and multiview video, audio, depth, camera pose, hand/body motion,
 inertial signals, object/contact annotations, and language captions.
@@ -10,15 +11,15 @@ inertial signals, object/contact annotations, and language captions.
 | Track | Question | Core inputs | First public pipeline | Current maturity |
 | --- | --- | --- | --- | --- |
-| Spatial intelligence models | Can the model recover and reason over space from video? | Multiview RGB, egocentric video, depth, camera pose, calibration, object cues, language questions. | Window or episode exporter that builds scene/object memory targets, then evaluates spatial QA, object permanence, counting, retrieval, and pose-aware consistency. | Ready as a pipeline and evaluation contract; strong claims require raw depth/pose access and held-out multi-episode evaluation. |
 | Human-video world models | Can the model predict what happens next? | Observed video/audio/sensor windows, hand/body motion, object/contact state, action/subtask labels, future windows. | Future-state and future-action probes over the existing split, then Cosmos-style or latent world-model training with separate dynamics metrics. | Partially evidenced through current future-task probes and Cosmos-style branch artifacts; still needs stronger visual/latent future metrics. |
-| Vision-language-action models | Can the model turn what it sees and reads into action? | Egocentric video, language captions, hand/body motion, contacts, objects, procedure/subtask labels. | Observation-language-to-action target conversion, action-chunk scoring, policy-token baselines, then VLA/policy model fine-tuning. | Feasible but gated by action-target conversion; do not claim policy quality until action tokens, normalization, and held-out policy metrics exist. |
 ## One-Sample Training-Pair Recipes
 These recipes describe how to obtain input/output pairs from the **single public
-sample episode**. They are development contracts, not claims that the three full
-foundation models are already trained.
 | Track | Input from the one public sample | Output target from the same sample | Existing hooks |
 | --- | --- | --- | --- |
@@ -28,7 +29,7 @@ foundation models are already trained.
 The one-sample windowization is 5,821 frames, 1,161 overlapping 20-frame windows,
 5-frame stride, and about 20 FPS. Future labels or future windows must not leak
-into inputs for world-model targets. VLA/policy claims require a later action
 space converter, normalization, retargeting report, and held-out policy metrics.
 ## Published Direction Figures
@@ -87,8 +88,9 @@ First practical implementation:
 4. Add qualitative scene-memory examples only when they are backed by saved
    target files and metrics.
-What to avoid claiming now: full neural rendering, full 3D reconstruction, or
-general spatial intelligence unless those artifacts and held-out metrics exist.
 ## 2. Human-Video World Model Pipeline
@@ -116,14 +118,14 @@ First practical implementation:
 1. Keep Qwen-style structured future probes for task-level interpretability.
 2. Keep Cosmos-style branches separate because they answer dynamics and visual
    future questions, not JSON task classification.
-3. Add latent or feature-reconstruction metrics before claiming world-model
    quality.
 4. Compare future-task metrics by held-out episode, task family, and visible
    object/action family.
-What to avoid claiming now: a strong world model from low structured scores
-alone. The public result should say that the pipeline and first probes exist,
-while stronger future-state training remains the next step.
 ## 3. Vision-Language-Action Pipeline
@@ -156,9 +158,9 @@ First practical implementation:
 4. Treat OpenVLA, openpi, GR00T, Octo, and SmolVLA-style models as policy
    branches that inherit the same split, manifest, and package rules.
-What to avoid claiming now: robot policy quality. The current project can
-build the conversion and scoring pipeline; policy quality needs action-space
-evidence and held-out model results.
 ## Shared Pipeline Discipline
@@ -174,6 +176,6 @@ All three tracks should reuse the same public discipline:
 - website and model cards updated only after validators pass.
 This framing lets the project pursue all three directions at once while keeping
-the claims precise: spatial intelligence is the geometry/reasoning pipeline,
 world modeling is the future-state pipeline, and VLA is the action-conversion
 and policy pipeline.

 # Three Foundation Pipeline Tracks
+Xperience-10M can support the three directions shown in the presentation. This
+document presents them as **pipeline tracks** with data exports, training
+recipes, and evaluation gates. The same dataset can feed all three tracks
+because it combines
 egocentric and multiview video, audio, depth, camera pose, hand/body motion,
 inertial signals, object/contact annotations, and language captions.
 | Track | Question | Core inputs | First public pipeline | Current maturity |
 | --- | --- | --- | --- | --- |
+| Spatial intelligence models | Can the model recover and reason over space from video? | Multiview RGB, egocentric video, depth, camera pose, calibration, object cues, language questions. | Window or episode exporter that builds scene/object memory targets, then evaluates spatial QA, object permanence, counting, retrieval, and pose-aware consistency. | Ready as a pipeline and evaluation contract; the next readout is held-out spatial QA, pose consistency, counting, and scene-memory metrics. |
 | Human-video world models | Can the model predict what happens next? | Observed video/audio/sensor windows, hand/body motion, object/contact state, action/subtask labels, future windows. | Future-state and future-action probes over the existing split, then Cosmos-style or latent world-model training with separate dynamics metrics. | Partially evidenced through current future-task probes and Cosmos-style branch artifacts; still needs stronger visual/latent future metrics. |
+| Vision-language-action models | Can the model turn what it sees and reads into action? | Egocentric video, language captions, hand/body motion, contacts, objects, procedure/subtask labels. | Observation-language-to-action target conversion, action-chunk scoring, policy-token baselines, then VLA/policy model fine-tuning. | Feasible after action-target conversion; present policy quality once action tokens, normalization, and held-out policy metrics exist. |
 ## One-Sample Training-Pair Recipes
 These recipes describe how to obtain input/output pairs from the **single public
+sample episode**. They are development contracts for building the three tracks,
+not finished foundation-model rows.
 | Track | Input from the one public sample | Output target from the same sample | Existing hooks |
 | --- | --- | --- | --- |
 The one-sample windowization is 5,821 frames, 1,161 overlapping 20-frame windows,
 5-frame stride, and about 20 FPS. Future labels or future windows must not leak
+into inputs for world-model targets. VLA/policy readouts require a later action
 space converter, normalization, retargeting report, and held-out policy metrics.
 ## Published Direction Figures
 4. Add qualitative scene-memory examples only when they are backed by saved
    target files and metrics.
+Next readout before stronger positioning: held-out spatial QA, pose
+consistency, object-counting, and scene-memory metrics. Full neural rendering
+and full 3D reconstruction should be separate follow-up artifacts.
 ## 2. Human-Video World Model Pipeline
 1. Keep Qwen-style structured future probes for task-level interpretability.
 2. Keep Cosmos-style branches separate because they answer dynamics and visual
    future questions, not JSON task classification.
+3. Add latent or feature-reconstruction metrics before presenting world-model
    quality.
 4. Compare future-task metrics by held-out episode, task family, and visible
    object/action family.
+Next readout before stronger positioning: latent or visual future metrics,
+per-episode future-task breakdowns, and qualitative examples backed by saved
+targets. The current public result shows the pipeline and first probes.
 ## 3. Vision-Language-Action Pipeline
 4. Treat OpenVLA, openpi, GR00T, Octo, and SmolVLA-style models as policy
    branches that inherit the same split, manifest, and package rules.
+Next readout before stronger positioning: action-space conversion, normalized
+action chunks, retargeting notes, and held-out policy metrics. The current
+project can build the conversion and scoring pipeline.
 ## Shared Pipeline Discipline
 - website and model cards updated only after validators pass.
 This framing lets the project pursue all three directions at once while keeping
+the readout precise: spatial intelligence is the geometry/reasoning pipeline,
 world modeling is the future-state pipeline, and VLA is the action-conversion
 and policy pipeline.

TWO_EVIDENCE_LINES.md CHANGED Viewed

@@ -6,10 +6,10 @@ The public Xperience-10M suite has two evidence lines. Read them separately.
 Score formula: 2 single-episode methods x 20 tasks = 40 records; 7 selected-128 methods x 20 tasks = 140 records; total public matrix = 180/180 scored records.
-| Line | Data unit | Score statement | Valid claim | Do not claim |
 | --- | --- | --- | --- | --- |
-| 1 sample episode | One public sample episode; 5,821 frames; 1,161 aligned 20-frame windows; 8,546 feature dimensions. | 40/40 direct scores from Minimal and Neural MLP heads. | Task construction, raw-file inspection, local reproducibility, and controlled single-episode baselines. | Multi-episode generalization. |
-| 128 selected episodes | Selected held-out 96/16/16 split; 34,269 exported windows; public-safe processed features linked to official gated episode paths. | 140/140 selected-128 scores: 134 direct + 6 compact-proxy. | Same-split method comparison, Qwen3-Omni v6 LoRA diagnostics, Cosmos3-Super/Cosmos3-Nano diagnostics, and scale-up planning. | Reading compact-proxy cells as direct raw-target measurements. |
 ## Result Ledger
@@ -45,9 +45,9 @@ Qwen3 run v1-v6 is a LoRA/evaluation lineage inside the 128-episode line, not th
 ## Interpretation Rule
-Use the 1-episode line for task construction and reproducibility claims.
-Use the 128-episode line for held-out same-split comparison and model-diagnostic claims.
-Do not mix those claims without naming the evidence line.
 ## Reading Order

 Score formula: 2 single-episode methods x 20 tasks = 40 records; 7 selected-128 methods x 20 tasks = 140 records; total public matrix = 180/180 scored records.
+| Line | Data unit | Score statement | Best use | Read separately from |
 | --- | --- | --- | --- | --- |
+| 1 sample episode | One public sample episode; 5,821 frames; 1,161 aligned 20-frame windows; 8,546 feature dimensions. | 40/40 direct scores from Minimal and Neural MLP heads. | Inspect the raw sample, understand file organization, reproduce the 20 task targets, and compare Minimal vs Neural MLP behavior inside one episode. | The selected-128 comparison rows and broader held-out model behavior. |
+| 128 selected episodes | Selected held-out 96/16/16 split; 34,269 exported windows; public-safe processed features linked to official gated episode paths. | 140/140 selected-128 scores: 134 direct + 6 compact-proxy. | Compare same-split metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super, and Cosmos3-Nano while keeping the 6 compact-proxy cells visible. | Direct raw-target interpretation for the proxy-marked cells. |
 ## Result Ledger
 ## Interpretation Rule
+Read the 1-episode line as the inspectable task lab.
+Read the 128-episode line as the selected held-out comparison surface.
+Compare scores within the same line first, then check proxy badges before combining totals.
 ## Reading Order

TWO_EVIDENCE_LINE_RESULT_SUMMARY.md CHANGED Viewed

@@ -1,21 +1,21 @@
 # Two Evidence-Line Result Summary
-Generated: `2026-06-21T11:49:06+00:00`.
 Source matrix: [`docs/data/task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json)
-Interpretation rule: Use the 1-episode line for task construction and reproducibility claims. Use the 128-episode line for same-split metadata/raw baselines, Qwen3-Omni v6 LoRA diagnostics, and Cosmos3 diagnostics.
 ## Read This First
-The suite has two public evidence lines. Line 1 is the fully inspectable one-episode task lab. Line 2 is the 128-episode comparison surface for aligned baselines, the Qwen3-Omni series, and the Cosmos3 series. Do not mix the two when reading scores.
 Score formula: 2 single-episode methods x 20 tasks = 40 records; 7 selected-128 methods x 20 tasks = 140 records; total public matrix = 180/180 scored records.
-| Line | What the scores mean | Valid claim | Do not claim |
 | --- | --- | --- | --- |
-| 1 sample episode | 40/40 direct scores from Minimal and Neural MLP heads on the same 20 task contracts. | Supports task construction, file inspection, local reproducibility, and controlled single-episode baseline claims. | Do not use this line as evidence of multi-episode generalization. |
-| 128 selected episodes | 140/140 selected-128 scores across seven methods: 134 direct scores plus 6 documented compact-proxy scores. | Supports same-split metadata/raw baseline comparison, Qwen3-Omni v6 diagnostics, Cosmos3 diagnostics, and scale-up planning on public-safe processed artifacts. | Do not read compact-proxy cells as direct raw-target measurements. |
 ## Public Score Totals

 # Two Evidence-Line Result Summary
+Generated: `2026-06-22T09:56:30+00:00`.
 Source matrix: [`docs/data/task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json)
+Interpretation rule: Read the 1-episode line as the inspectable task lab. Read the 128-episode line as the selected comparison surface for metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super, and Cosmos3-Nano.
 ## Read This First
+The suite has two public reading lanes. Line 1 is the fully inspectable one-episode task lab. Line 2 is the 128-episode comparison surface for aligned baselines, the Qwen3-Omni series, and the Cosmos3 series. Compare scores within the same lane first.
 Score formula: 2 single-episode methods x 20 tasks = 40 records; 7 selected-128 methods x 20 tasks = 140 records; total public matrix = 180/180 scored records.
+| Line | What the scores mean | Best use | Read separately from |
 | --- | --- | --- | --- |
+| 1 sample episode | 40/40 direct scores from Minimal and Neural MLP heads on the same 20 task contracts. | Inspect the raw sample, understand file organization, reproduce the 20 task targets, and compare Minimal vs Neural MLP behavior inside one episode. | The selected-128 comparison rows and broader held-out model behavior. |
+| 128 selected episodes | 140/140 selected-128 scores across seven methods: 134 direct scores plus 6 documented compact-proxy scores. | Compare same-split metadata/raw baselines, Qwen3-Omni v6, Cosmos3-Super, and Cosmos3-Nano while keeping the 6 compact-proxy cells visible. | Direct raw-target interpretation for the proxy-marked cells. |
 ## Public Score Totals

docs/data/artifact_index.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-21T20:35:18+00:00",
   "status": "pass",
   "artifact_count": 228,
   "missing": [],
@@ -92,8 +92,8 @@
       "surface": "repo_hf",
       "shows": "Defines terminology that can be confused across data scope, task metrics, model branches, and public mirrors.",
       "exists": true,
-      "bytes": 10857,
-      "sha256": "b9b8f8695b6c211e849073e1634057f158877a61161eca05b1266769158ee83e"
     },
     {
       "id": "glossary_json",
@@ -103,8 +103,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable terminology layer for the website, artifact dataset, model mirror, and public QA checks.",
       "exists": true,
-      "bytes": 18754,
-      "sha256": "b1dc42e1f42a7c19bb4b2ebd32a0862df28bec671eaa849b09a97f103675e9eb"
     },
     {
       "id": "research_roadmap",
@@ -125,8 +125,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable research roadmap for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 14129,
-      "sha256": "a06d6525d9532b8608bf7be81eb9387deca3159b7c42bf38e107b4096953f351"
     },
     {
       "id": "foundation_model_plan",
@@ -147,8 +147,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable foundation-model selection matrix with source links, entry conditions, and evaluation additions.",
       "exists": true,
-      "bytes": 13925,
-      "sha256": "77d4b2d5918ef1f776de6d29d34d523de95ba58df9526e2b255bed567434f932"
     },
     {
       "id": "three_foundation_pipelines",
@@ -158,8 +158,8 @@
       "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": 11609,
-      "sha256": "69d4d8781015c807c58a720b3bf967ee872540a2fe2b7e7973105946c5a1cf11"
     },
     {
       "id": "three_foundation_pipelines_json",
@@ -169,8 +169,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable pipeline-track contract for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 14465,
-      "sha256": "c8c9b7a9ee8d3ecfe8662cf0336a7ca2ade6d670c5567d2a50dfb67c7241defd"
     },
     {
       "id": "spatial_intelligence_slide_diagram",
@@ -632,7 +632,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "2f3d03a1c56ebb1b220574e565a05d35705a10a22d1cb7ecdcb1452630574bc9"
     },
     {
       "id": "source_alignment_validator",
@@ -686,8 +686,8 @@
       "surface": "repo_hf",
       "shows": "Defines the window unit, chronological split, task metrics, leakage controls, and current limitations.",
       "exists": true,
-      "bytes": 8905,
-      "sha256": "f82e9b9c4a07e95776005968788e7acdaae9e322991113d79432d59057181add"
     },
     {
       "id": "evaluation_protocol_json",
@@ -697,8 +697,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable protocol generated from committed task metrics for website and HF mirrors.",
       "exists": true,
-      "bytes": 24267,
-      "sha256": "83289b52e0193fa86d63fac7dd8af98d0612199b1e1a5f5ed37014baa9168656"
     },
     {
       "id": "evaluation_protocol_builder",
@@ -708,8 +708,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the protocol from committed summary metrics and task artifacts.",
       "exists": true,
-      "bytes": 19845,
-      "sha256": "b5d888fd67ba6b8150b24960aa0858db50f16a5c7b59a8ce274401e384f0a2a3"
     },
     {
       "id": "task_suite_20",
@@ -1182,7 +1182,7 @@
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
       "bytes": 8640,
-      "sha256": "87c26049df761996430f1ca6ef25ee53400387f907f356dcea6b3fcc7d6dac82"
     },
     {
       "id": "public_surface_qa",
@@ -1201,7 +1201,7 @@
       "path": "PUBLIC_READER_MAP.md",
       "kind": "project_path",
       "surface": "repo_hf",
-      "shows": "Provides the first-pass navigation layer for GitHub, GitHub Pages, Hugging Face mirrors, Qwen3-Omni/Cosmos3 repos, evidence lines, and claim boundaries.",
       "exists": true,
       "bytes": 5746,
       "sha256": "99ede76679880537bcf65d9503a848c9cd6ca464b3cd92c48eaa62ec5a8655eb"
@@ -1351,8 +1351,8 @@
       "surface": "repo_hf",
       "shows": "Generates the selective artifact catalog from local files.",
       "exists": true,
-      "bytes": 68407,
-      "sha256": "9f5fc1af7c4c23083975066691bf6d75aa99280961198e2c47be88754c142418"
     },
     {
       "id": "publication_audit",
@@ -1387,7 +1387,7 @@
       "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": 1420751,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1399,7 +1399,7 @@
       "volatile": true,
       "shows": "Confirms local website links, anchors, JSON data files, and referenced images resolve.",
       "exists": true,
-      "bytes": 20178,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1564,8 +1564,8 @@
       "surface": "website_hf",
       "shows": "Presents the task suite and sample modality thumbnails with metrics generated from committed files.",
       "exists": true,
-      "bytes": 1897278,
-      "sha256": "71b1ab150e952cf902488226c65b3822d8016974f63d111204c1eb1a7745faad"
     },
     {
       "id": "modality_atlas",

 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
+  "generated_at_utc": "2026-06-22T09:57:42+00:00",
   "status": "pass",
   "artifact_count": 228,
   "missing": [],
       "surface": "repo_hf",
       "shows": "Defines terminology that can be confused across data scope, task metrics, model branches, and public mirrors.",
       "exists": true,
+      "bytes": 10861,
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docs/data/evaluation_protocol.json CHANGED Viewed

@@ -2,7 +2,7 @@
   "title": "Ropedia Xperience-10M Task Suite Evaluation Protocol",
   "status": "pass",
   "version": "2026-06-01",
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@@ -509,7 +509,7 @@
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     "Feature-vector reconstruction is separate from pixel depth, mesh, NeRF, or Gaussian reconstruction.",
-    "The final verified Qwen3-Omni diagnostic result meets the strict-JSON target, but action/subtask held-out quality remains weak and needs error analysis before larger model-quality claims.",
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   ],
   "scale_up_gate": {

   "title": "Ropedia Xperience-10M Task Suite Evaluation Protocol",
   "status": "pass",
   "version": "2026-06-01",
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   "source_files": [
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     "Cross-episode generalization for Qwen3-Omni has a first verified diagnostic pilot, but strong model quality is not yet shown.",
     "Feature-vector reconstruction is separate from pixel depth, mesh, NeRF, or Gaussian reconstruction.",
+    "The final verified Qwen3-Omni diagnostic result meets the strict-JSON target, but action/subtask held-out quality remains weak and needs error analysis before presenting stronger model-quality numbers.",
     "Full audio-visual representation learning still needs multi-episode training; the current report includes single-episode audio/no-audio ablations."
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docs/data/foundation_model_plan.json CHANGED Viewed

@@ -5,7 +5,7 @@
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   "pipeline_tracks": {
     "source_document": "THREE_FOUNDATION_PIPELINES.md",
     "data": "docs/data/three_foundation_pipelines.json",
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       "human_video_world_models",

docs/data/glossary.json CHANGED Viewed

@@ -6,7 +6,7 @@
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@@ -43,7 +43,7 @@
       "category": "dataset_scope",
       "plain_meaning": "One officially available sample episode.",
       "project_usage": "The fully inspectable Line 1 unit used for raw-file browsing, 20-frame windows, task construction, and single-episode baselines.",
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@@ -57,7 +57,7 @@
     {
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       "project_usage": "Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison.",
       "do_not_confuse_with": "Qwen run versions v1-v6, which are model-run lineage.",
       "primary_files": ["TWO_EVIDENCE_LINES.md", "docs/data/two_evidence_lines.json"]

     {
       "id": "dataset_scope",
       "label": "Dataset and scope",
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       "category": "dataset_scope",
       "plain_meaning": "One officially available sample episode.",
       "project_usage": "The fully inspectable Line 1 unit used for raw-file browsing, 20-frame windows, task construction, and single-episode baselines.",
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       "primary_files": ["docs/data/raw_sample_files.json", "docs/single_episode_explorer.html"]
     },
     {
     {
       "term": "Evidence line",
       "category": "dataset_scope",
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       "project_usage": "Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison.",
       "do_not_confuse_with": "Qwen run versions v1-v6, which are model-run lineage.",
       "primary_files": ["TWO_EVIDENCE_LINES.md", "docs/data/two_evidence_lines.json"]

docs/data/mirror_parity.json CHANGED Viewed

@@ -1,6 +1,6 @@
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@@ -383,45 +383,45 @@
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@@ -972,44 +972,44 @@
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@@ -1021,44 +1021,44 @@
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           "path": "hf_artifacts:PROJECT_BRIEF.md",
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@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
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   "checks": [
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@@ -18,7 +18,7 @@
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@@ -28,27 +28,27 @@
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           "exists": true,
           "status": "pass",
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@@ -96,9 +96,9 @@
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         "128-episode pilot": 1
       }
     },

 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
+  "generated_at_utc": "2026-06-22T10:20:26+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
         "website_integrity": {
           "exists": true,
           "status": "pass",
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         },
         "rendered_site_check": {
           "exists": true,
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
+          "generated_at_utc": "2026-06-22T10:10:38+00:00"
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         "source_alignment": {
           "exists": true,
           "status": "pass",
+          "generated_at_utc": "2026-06-22T10:10:38+00:00"
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         "scale_up_status": {
           "exists": true,
           "status": "pass",
+          "generated_at_utc": "2026-06-21T20:58:21+00:00"
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         "publication_package": {
           "exists": true,
           "status": "pass",
+          "generated_at_utc": "2026-06-22T10:19:22+00:00"
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         "mirror_parity": {
           "exists": true,
           "status": "pass",
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       "reason": "Public copy should consistently present the project as Ropedia Xperience-10M, with the Qwen3-Omni scale-up status.",
       "marker_counts": {
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+        "Xperience-10M": 174,
+        "20-task": 110,
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         "128-episode pilot": 1
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docs/data/publication_audit.json CHANGED Viewed

@@ -1,6 +1,6 @@
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   "checks": [
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@@ -257,7 +257,7 @@
     "hf_artifact_bundle": {
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       "exists": true,
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@@ -268,7 +268,7 @@
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 {
   "status": "pass",
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   "checks": [
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       "name": "required_publication_assets_present",
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
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       "text_file_count": 1328,
       "largest_file": {
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     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
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@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-21T20:35:18+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
   "automated_gates": [
     {

 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
+  "generated_at_utc": "2026-06-22T10:20:26+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
   "automated_gates": [
     {

docs/data/research_roadmap.json CHANGED Viewed

@@ -142,7 +142,7 @@
                 "held-out metrics by failure type",
                 "verified public-safe package"
             ],
-            "reader_takeaway": "The next pass should improve action/subtask quality before larger model-quality claims."
         },
         {
             "id": "foundation_model_selection_matrix",

                 "held-out metrics by failure type",
                 "verified public-safe package"
             ],
+            "reader_takeaway": "The next pass should improve action/subtask quality before presenting stronger model-quality numbers."
         },
         {
             "id": "foundation_model_selection_matrix",

docs/data/research_roadmap_interactive.json CHANGED Viewed

@@ -2862,7 +2862,7 @@
     ],
     "status": "planning_artifact"
   },
-  "generated_at_utc": "2026-06-21T19:51:24+00:00",
   "omni_plan": {
     "adapter": "LoRA rank 16, alpha 32, dropout 0.05",
     "backbone": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
@@ -3013,7 +3013,7 @@
       "entry_condition": "The final diagnostic package meets strict JSON validity but has weak action/subtask held-out quality.",
       "id": "qwen3_omni_structured_output_error_analysis",
       "name": "Action/Subtask Error-Analysis Pass",
-      "reader_takeaway": "The next pass should improve action/subtask quality before larger model-quality claims.",
       "stage": "future",
       "status": "active_next_step"
     },
@@ -4197,17 +4197,12 @@
     }
   ],
   "three_foundation_pipelines": {
-    "claim_boundary": "These are supported pipeline directions, not three completed model-quality claims.",
     "source_document": "THREE_FOUNDATION_PIPELINES.md",
     "status": "pipeline_plan",
     "title": "Three Foundation Pipeline Tracks",
     "tracks": [
       {
-        "avoid_claiming_now": [
-          "full neural rendering",
-          "full 3D reconstruction",
-          "general spatial intelligence without artifact-level evidence"
-        ],
         "core_inputs": [
           "multiview RGB",
           "egocentric video",
@@ -4251,7 +4246,7 @@
               "held-out episode spatial metrics",
               "count and relation accuracy",
               "retrieval rank and consistency",
-              "saved predictions before public claim"
             ],
             "stage": "evaluate_gates"
           }
@@ -4269,8 +4264,13 @@
           "spatial QA prompts"
         ],
         "next_gate": "Raw depth and pose artifacts plus held-out multi-episode spatial metrics.",
         "one_sample_training_io": {
-          "boundary": "This yields a one-episode spatial training-pair recipe and proxy tasks; full spatial-intelligence claims require held-out multi-episode depth/pose/scene-memory metrics.",
           "existing_task_hooks": [
             "object_relevance",
             "modality_reconstruction",
@@ -4303,10 +4303,6 @@
         "website_image": "assets/foundation-pipelines/spatial-intelligence-pipeline.png"
       },
       {
-        "avoid_claiming_now": [
-          "strong world model from structured future-task scores alone",
-          "visual future quality without visual or latent future metrics"
-        ],
         "core_inputs": [
           "observed video windows",
           "audio",
@@ -4356,7 +4352,7 @@
           }
         ],
         "diagram_image": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
-        "first_pipeline": "Keep Qwen-style structured future probes for task interpretability, keep Cosmos-style dynamics branches separate, and add latent or feature-reconstruction metrics before claiming world-model quality.",
         "id": "human_video_world_models",
         "image_alt": "High-resolution slide diagram showing the Human-video world models direction for Xperience-10M.",
         "intermediate_artifacts": [
@@ -4367,8 +4363,13 @@
           "temporal consistency metadata"
         ],
         "next_gate": "Stronger future-state metrics, qualitative future examples, and held-out episode breakdowns.",
         "one_sample_training_io": {
-          "boundary": "Future labels and future windows must stay out of the input. Structured future probes are evidence for the pipeline, not a full visual world-model claim by themselves.",
           "existing_task_hooks": [
             "next_action",
             "long_horizon_next_action",
@@ -4401,10 +4402,6 @@
         "website_image": "assets/foundation-pipelines/human-video-world-model-pipeline.png"
       },
       {
-        "avoid_claiming_now": [
-          "robot policy quality",
-          "policy generalization before action-space evidence exists"
-        ],
         "core_inputs": [
           "egocentric video",
           "language captions",
@@ -4447,7 +4444,7 @@
               "held-out action metrics",
               "chunk and next-action accuracy",
               "object/contact-conditioned scores",
-              "policy card before robot-policy claim"
             ],
             "stage": "evaluate_gates"
           }
@@ -4465,8 +4462,13 @@
           "action-space model card"
         ],
         "next_gate": "Traceable action tokens, normalization, retargeting metadata, and held-out policy metrics.",
         "one_sample_training_io": {
-          "boundary": "This is a VLA/policy data-conversion recipe for the one-sample suite. Robot policy claims require a later action-space converter, normalization, retargeting report, and held-out policy metrics.",
           "existing_task_hooks": [
             "timeline_action",
             "next_action",

     ],
     "status": "planning_artifact"
   },
+  "generated_at_utc": "2026-06-22T10:07:43+00:00",
   "omni_plan": {
     "adapter": "LoRA rank 16, alpha 32, dropout 0.05",
     "backbone": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
       "entry_condition": "The final diagnostic package meets strict JSON validity but has weak action/subtask held-out quality.",
       "id": "qwen3_omni_structured_output_error_analysis",
       "name": "Action/Subtask Error-Analysis Pass",
+      "reader_takeaway": "The next pass should improve action/subtask quality before presenting stronger model-quality numbers.",
       "stage": "future",
       "status": "active_next_step"
     },
     }
   ],
   "three_foundation_pipelines": {
+    "reader_note": "These are supported pipeline directions with concrete data exports, training recipes, and evaluation gates.",
     "source_document": "THREE_FOUNDATION_PIPELINES.md",
     "status": "pipeline_plan",
     "title": "Three Foundation Pipeline Tracks",
     "tracks": [
       {
         "core_inputs": [
           "multiview RGB",
           "egocentric video",
               "held-out episode spatial metrics",
               "count and relation accuracy",
               "retrieval rank and consistency",
+              "saved predictions before public package update"
             ],
             "stage": "evaluate_gates"
           }
           "spatial QA prompts"
         ],
         "next_gate": "Raw depth and pose artifacts plus held-out multi-episode spatial metrics.",
+        "next_readout_before_stronger_positioning": [
+          "held-out spatial QA",
+          "pose consistency",
+          "object-counting and scene-memory metrics"
+        ],
         "one_sample_training_io": {
+          "boundary": "This yields a one-episode spatial training-pair recipe and proxy tasks; the next spatial-intelligence readout is held-out depth, pose, and scene-memory metrics.",
           "existing_task_hooks": [
             "object_relevance",
             "modality_reconstruction",
         "website_image": "assets/foundation-pipelines/spatial-intelligence-pipeline.png"
       },
       {
         "core_inputs": [
           "observed video windows",
           "audio",
           }
         ],
         "diagram_image": "docs/assets/foundation-pipelines/human-video-world-model-pipeline.png",
+        "first_pipeline": "Keep Qwen-style structured future probes for task interpretability, keep Cosmos-style dynamics branches separate, and add latent or feature-reconstruction metrics before presenting world-model quality.",
         "id": "human_video_world_models",
         "image_alt": "High-resolution slide diagram showing the Human-video world models direction for Xperience-10M.",
         "intermediate_artifacts": [
           "temporal consistency metadata"
         ],
         "next_gate": "Stronger future-state metrics, qualitative future examples, and held-out episode breakdowns.",
+        "next_readout_before_stronger_positioning": [
+          "latent or visual future metrics",
+          "per-episode future-task breakdowns",
+          "qualitative examples backed by saved targets"
+        ],
         "one_sample_training_io": {
+          "boundary": "Future labels and future windows must stay out of the input. Structured future probes show the pipeline, while visual world-model readouts need latent or visual future metrics.",
           "existing_task_hooks": [
             "next_action",
             "long_horizon_next_action",
         "website_image": "assets/foundation-pipelines/human-video-world-model-pipeline.png"
       },
       {
         "core_inputs": [
           "egocentric video",
           "language captions",
               "held-out action metrics",
               "chunk and next-action accuracy",
               "object/contact-conditioned scores",
+              "policy card before robot-policy quality readout"
             ],
             "stage": "evaluate_gates"
           }
           "action-space model card"
         ],
         "next_gate": "Traceable action tokens, normalization, retargeting metadata, and held-out policy metrics.",
+        "next_readout_before_stronger_positioning": [
+          "action-space conversion",
+          "normalized action chunks",
+          "held-out policy metrics"
+        ],
         "one_sample_training_io": {
+          "boundary": "This is a VLA/policy data-conversion recipe for the one-sample suite. Robot policy readouts require a later action-space converter, normalization, retargeting report, and held-out policy metrics.",
           "existing_task_hooks": [
             "timeline_action",
             "next_action",

docs/data/scope_claims_audit.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T20:58:21+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,

 {
   "status": "pass",
+  "generated_at_utc": "2026-06-22T10:20:29+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,

docs/index.html CHANGED Viewed

@@ -1132,7 +1132,7 @@
       font-size: 13px;
       line-height: 1.52;
     }
-    .line-claim {
       display: grid;
       gap: 8px;
       margin: 14px 0 0;
@@ -1141,7 +1141,7 @@
       border-radius: var(--radius);
       background: rgba(2, 5, 2, 0.38);
     }
-    .line-claim span {
       display: block;
       color: var(--green);
       font-family: var(--font-mono);
@@ -1150,7 +1150,7 @@
       letter-spacing: 0.08em;
       text-transform: uppercase;
     }
-    .line-claim p {
       margin: 2px 0 0;
       color: #d7e2d0;
       font-size: 12.5px;
@@ -4960,9 +4960,9 @@
             <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>
@@ -4975,9 +4975,9 @@
             <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>
@@ -5144,8 +5144,8 @@
               <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>
@@ -5154,16 +5154,16 @@
               <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>
@@ -5270,7 +5270,7 @@
           <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">
@@ -5423,7 +5423,7 @@
             </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>
@@ -5613,7 +5613,7 @@
           <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>
@@ -5797,7 +5797,7 @@
           <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>
@@ -6037,7 +6037,7 @@
         </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>
@@ -6205,7 +6205,7 @@
           </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">
@@ -6232,7 +6232,7 @@
           <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>
@@ -6255,7 +6255,7 @@
             <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">
@@ -6269,7 +6269,7 @@
             <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">
@@ -6400,7 +6400,7 @@
                 <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>
@@ -6731,7 +6731,7 @@
           <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>
@@ -6757,8 +6757,8 @@
                   </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>
@@ -6802,7 +6802,7 @@
           </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>
@@ -6863,7 +6863,7 @@
               <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>
@@ -6881,7 +6881,7 @@
           <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>
@@ -6907,7 +6907,7 @@
               <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>
@@ -7753,7 +7753,7 @@ python scripts/validate_publication_package.py</code></pre>
       "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.",

       font-size: 13px;
       line-height: 1.52;
     }
+    .line-readout {
       display: grid;
       gap: 8px;
       margin: 14px 0 0;
       border-radius: var(--radius);
       background: rgba(2, 5, 2, 0.38);
     }
+    .line-readout span {
       display: block;
       color: var(--green);
       font-family: var(--font-mono);
       letter-spacing: 0.08em;
       text-transform: uppercase;
     }
+    .line-readout p {
       margin: 2px 0 0;
       color: #d7e2d0;
       font-size: 12.5px;
             <small>line 1 / public sample</small>
             <h3>1 sample episode: task lab</h3>
             <p>One public episode becomes aligned windows, task targets, Minimal heads, and Neural MLP heads.</p>
+            <div class="line-readout">
+              <div><span>best use</span><p>Inspect the sample files, task targets, and local baseline runs.</p></div>
+              <div><span>read separately from</span><p>Selected-128 comparison rows and held-out model behavior.</p></div>
             </div>
             <div class="suite-line-facts">
               <span><strong>5,821</strong>frames</span>
             <small>line 2 / 128 selected episodes</small>
             <h3>128 selected episodes: comparison surface</h3>
             <p>Seven methods share the selected-episode surface and the same 20 task axes.</p>
+            <div class="line-readout">
+              <div><span>best use</span><p>Compare same-split baselines, Qwen3-Omni v6, and Cosmos3 rows.</p></div>
+              <div><span>read separately from</span><p>Direct raw-target metrics for the 6 proxy-marked cells.</p></div>
             </div>
             <div class="suite-line-facts">
               <span><strong>128</strong>selected episodes</span>
               <th>Line</th>
               <th>Data unit</th>
               <th>Score statement</th>
+              <th>Best use</th>
+              <th>Read separately from</th>
               <th>Start here</th>
             </tr>
           </thead>
               <td>1 sample episode</td>
               <td>One public Xperience-10M sample episode; 5,821 frames; 1,161 aligned 20-frame windows; 8,546-dimensional feature contract.</td>
               <td>40/40 direct scores from Minimal and Neural MLP heads.</td>
+              <td>Raw sample inspection, file organization, task definitions, local reproduction, and controlled Minimal-vs-Neural baseline behavior.</td>
+              <td>The selected-128 comparison rows and broader held-out model behavior.</td>
               <td><a href="#raw-sample">Raw browser</a><br><a href="data/single_episode_task_model_radar.json">1-episode radar 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 across metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super, Cosmos3-Nano, and scale-up decisions.</td>
+              <td>Direct raw-target interpretation for the proxy-marked cells.</td>
               <td><a href="data/episode128_task_model_radar.json">128-episode radar 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>
           <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 how to read each result family.</p>
             <a href="#overview">Read overview</a>
           </article>
           <article class="reader-step">
             </article>
             <article class="brief-card">
               <strong>Evaluation discipline</strong>
+              <p>Keeps chronological splits, predictions, confusion matrices, leakage notes, and single-episode limits visible before moving to broader model-quality reads.</p>
             </article>
             <article class="brief-card">
               <strong>Scale-up readiness</strong>
           <article class="roadmap-card" data-status="active_next_step">
             <span class="roadmap-status">active next step</span>
             <h3>Action/Subtask Error-Analysis Pass</h3>
+            <p>Keep the 96/16/16 split, tighten JSON decoding or target formatting, and analyze action/subtask failures before presenting stronger model-quality numbers.</p>
             <div class="roadmap-meta">
               <strong>Entry</strong><p>The final diagnostic package is verified, meets strict JSON validity, and exposes weak action/subtask quality.</p>
               <strong>Evidence</strong><p>Updated quality-target report, error-analysis tables, held-out metrics, and public-safe package.</p>
           <article class="artifact"><h3>Leakage controls</h3><p>Scalers fit on train windows only; future labels, target-side signals, caption/object labels, and contact labels stay on the target side unless explicitly queried.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_evaluation_protocol.py">builder script</a></article>
           <article class="artifact"><h3>Audio ablation</h3><p>Audio and no-audio variants are evaluated across the walkthrough-backed task contracts under the same chronological split.</p><a href="data/audio_ablation_summary.json">audio summary</a></article>
           <article class="artifact"><h3>Foundation track selection</h3><p>Qwen3-Omni is the first trainable baseline, Cosmos 3 is the world-model track with a camera-pose proxy forward-dynamics contract ready for trainer work, policy models wait for robot-compatible action targets, and Xperience-native pretraining remains a later full-corpus goal.</p><a href="data/foundation_model_plan.json">backbone plan</a></article>
+          <article class="artifact"><h3>Next evaluation stage</h3><p>This public-sample run covers single-episode task development. The selected multi-episode Qwen3-Omni final diagnostic result is verified and meets the JSON-validity target; Cosmos3-Nano has a verified future-window compatibility package; and Cosmos3-Super has a verified base-weight JSON-task evaluation plus a fine-tuned forward-dynamics LoRA branch. The next stage is action/subtask error analysis, stronger held-out metrics, and policy-target conversion.</p><a href="data/omni_model_comparison.json">result comparison</a></article>
           <article class="artifact"><h3>Selected-128 next stressor</h3><p>Before adding episodes, the suite should try `multiscale_20s10_40s20_80s40`, hierarchical action/subtask targets, label-normalized scoring, and compact raw-feature shards for unsupported tasks.</p><a href="data/task_suite_enhancement_128.json">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>
         <div class="artifact-grid">
           <article class="artifact primary-artifact"><div><h3>Official dataset</h3><p>Xperience-10M is a gated large-scale egocentric multimodal dataset for embodied AI, robotics, spatial intelligence, and world modeling.</p></div><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">official HF dataset</a></article>
+          <article class="artifact"><h3>Line 1 public sample</h3><p>The one-episode line builds the inspectable 20-task lab. Use Line 2 for selected-128 held-out comparison.</p><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">sample dataset</a></article>
           <article class="artifact"><h3>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>
           <article class="figure-brief-card">
             <h3>What the figure represents</h3>
+            <p>It documents the selected 128-episode final diagnostic result and the action/subtask improvement path for stronger held-out metrics.</p>
           </article>
         </div>
         <img class="pipeline-image lora-pipeline-image" src="assets/qwen3_omni_lora_pipeline.png?v=qwen3-lora-v1" alt="Detailed Qwen3-Omni LoRA training pipeline from raw Xperience-10M episodes to adapter outputs, predictions, metrics, and reports">
           <article class="result-reading-step">
             <span>01</span>
             <strong>Choose the line</strong>
+            <p>Use 1 episode for the task lab. Use 128 episodes for the selected comparison surface.</p>
           </article>
           <article class="result-reading-step">
             <span>02</span>
             <small>1 episode results</small>
             <h3>Task-lab evidence</h3>
             <p>Minimal and Neural MLP heads are both scored on all 20 public-sample task contracts. All 40 scores are direct task-target metrics.</p>
+            <div class="line-readout">
               <div><span>best read as</span><p>A reproducible public task suite and baseline behavior check.</p></div>
             </div>
             <div class="suite-line-facts">
             <small>128 episode results</small>
             <h3>Scale-up evidence</h3>
             <p>Metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window use the aligned 128-episode surface. It has 134 direct scores plus 6 compact-proxy scores.</p>
+            <div class="line-readout">
               <div><span>best read as</span><p>A same-split comparison table with explicit source and proxy status.</p></div>
             </div>
             <div class="suite-line-facts">
                 <thead>
                   <tr>
                     <th><span class="term-with-help"><span>Method</span><span class="term-help" tabindex="0" aria-label="Method row: One named method family in the matrix." title="One named method family in the matrix.">i<span class="term-tooltip" role="tooltip">One named method family in the matrix, such as Minimal, 128ep Raw NN, Qwen3-Omni v6, or Cosmos3-Super.</span></span></span></th>
+                    <th><span class="term-with-help"><span>Line</span><span class="term-help" tabindex="0" aria-label="Evidence line: A reading lane for a group of results." title="A reading lane for a group of results.">i<span class="term-tooltip" role="tooltip">A reading lane for a group of results: Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison.</span></span></span></th>
                     <th><span class="term-with-help"><span>Records</span><span class="term-help" tabindex="0" aria-label="Task-method record: One method evaluated on one task." title="One method evaluated on one task.">i<span class="term-tooltip" role="tooltip">One method evaluated on one task. 9 methods x 20 tasks gives 180 public result records.</span></span></span></th>
                     <th><span class="term-with-help"><span>Direct</span><span class="term-help" tabindex="0" aria-label="Direct score: A metric computed against the task target directly." title="A metric computed against the task target directly.">i<span class="term-tooltip" role="tooltip">A metric computed against the task target directly. This is the preferred score type in the 20-task matrix.</span></span></span></th>
                     <th><span class="term-with-help"><span>Proxy</span><span class="term-help" tabindex="0" aria-label="Compact-proxy score: A bounded proxy metric when a direct raw target is not publicly available." title="A bounded proxy metric when a direct raw target is not publicly available.">i<span class="term-tooltip" role="tooltip">A bounded proxy metric when a direct raw target is not publicly available. It stays explicit so readers do not over-read it.</span></span></span></th>
           <div class="glossary-summary" aria-label="Glossary categories">
             <article>
               <small>scope</small>
+              <strong>Separate data, result lanes, 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>
                   </tr>
                 </thead>
                 <tbody>
+                  <tr><td>Evidence line</td><td>A reading lane for a group of 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>The selected-128 comparison rows.</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>
           </article>
           <article class="resource-mode">
             <small>verify</small>
+            <strong>Check result 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>
               <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 result-reading lanes.</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>
           <section class="artifact-group tabbed-panel" id="artifact-panel-scale-up" role="tabpanel" aria-labelledby="artifact-tab-scale-up" hidden>
             <div class="artifact-group-head">
               <div><span>Scale-up path</span><h3>Verified diagnostic pilot</h3></div>
+              <p>The multi-episode Qwen3-Omni path is documented, scripted, and verified as a validation-monitored held-out pilot. The next stronger metrics come from structured-output and error-analysis improvements.</p>
             </div>
             <div class="artifact-grid">
               <article class="artifact primary-artifact"><div><h3>Two-line model comparison</h3><p>Groups Line 1 task-head baselines and Line 2 selected-128 methods: metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Nano Future Window, and Cosmos3-Super Reasoner.</p></div><a href="data/omni_model_comparison.json">result comparison</a></article>
               <article class="artifact"><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 source facts from local/public project inventory and derived artifacts.</p><a href="data/source_alignment_audit.json">source alignment</a></article>
               <article class="artifact"><h3>Task surface validation</h3><p>Checks task-count, task-contract, and result-matrix consistency across generated public data files.</p><a href="data/task_surface_integrity.json">task surface</a></article>
               <article class="artifact"><h3>Quality gates</h3><p>Summarizes build, validation, mirror, and publication gates that should pass before readers rely on the release.</p><a href="data/quality_gates.json">quality gates</a></article>
             </div>
       "normalized radar value": "A 0-1 plotting value used only to draw comparable radar polygons across metrics with different scales.",
       "task-method record": "One method evaluated on one task. 9 methods x 20 tasks gives 180 public result records.",
       "unified 20-task suite": "The current task surface: all 20 task contracts presented together and scored across methods where real artifacts exist.",
+      "evidence line": "A reading lane for a group of results: Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison.",
       "public sample episode": "The fully inspectable Line 1 unit used for raw-file browsing, task construction, and single-episode baselines.",
       "selected 128 episodes": "Line 2 uses derived windows/features and keeps links back to official gated episode ids and source paths.",
       "minimal baseline": "A simple non-neural task head used as a low-complexity comparison on the public sample episode.",

index.html CHANGED Viewed

@@ -1132,7 +1132,7 @@
       font-size: 13px;
       line-height: 1.52;
     }
-    .line-claim {
       display: grid;
       gap: 8px;
       margin: 14px 0 0;
@@ -1141,7 +1141,7 @@
       border-radius: var(--radius);
       background: rgba(2, 5, 2, 0.38);
     }
-    .line-claim span {
       display: block;
       color: var(--green);
       font-family: var(--font-mono);
@@ -1150,7 +1150,7 @@
       letter-spacing: 0.08em;
       text-transform: uppercase;
     }
-    .line-claim p {
       margin: 2px 0 0;
       color: #d7e2d0;
       font-size: 12.5px;
@@ -4960,9 +4960,9 @@
             <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>
@@ -4975,9 +4975,9 @@
             <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>
@@ -5144,8 +5144,8 @@
               <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>
@@ -5154,16 +5154,16 @@
               <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>
@@ -5270,7 +5270,7 @@
           <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">
@@ -5423,7 +5423,7 @@
             </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>
@@ -5613,7 +5613,7 @@
           <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>
@@ -5797,7 +5797,7 @@
           <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>
@@ -6037,7 +6037,7 @@
         </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>
@@ -6205,7 +6205,7 @@
           </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">
@@ -6232,7 +6232,7 @@
           <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>
@@ -6255,7 +6255,7 @@
             <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">
@@ -6269,7 +6269,7 @@
             <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">
@@ -6400,7 +6400,7 @@
                 <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>
@@ -6731,7 +6731,7 @@
           <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>
@@ -6757,8 +6757,8 @@
                   </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>
@@ -6802,7 +6802,7 @@
           </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>
@@ -6863,7 +6863,7 @@
               <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>
@@ -6881,7 +6881,7 @@
           <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>
@@ -6907,7 +6907,7 @@
               <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>
@@ -7753,7 +7753,7 @@ python scripts/validate_publication_package.py</code></pre>
       "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.",

       font-size: 13px;
       line-height: 1.52;
     }
+    .line-readout {
       display: grid;
       gap: 8px;
       margin: 14px 0 0;
       border-radius: var(--radius);
       background: rgba(2, 5, 2, 0.38);
     }
+    .line-readout span {
       display: block;
       color: var(--green);
       font-family: var(--font-mono);
       letter-spacing: 0.08em;
       text-transform: uppercase;
     }
+    .line-readout p {
       margin: 2px 0 0;
       color: #d7e2d0;
       font-size: 12.5px;
             <small>line 1 / public sample</small>
             <h3>1 sample episode: task lab</h3>
             <p>One public episode becomes aligned windows, task targets, Minimal heads, and Neural MLP heads.</p>
+            <div class="line-readout">
+              <div><span>best use</span><p>Inspect the sample files, task targets, and local baseline runs.</p></div>
+              <div><span>read separately from</span><p>Selected-128 comparison rows and held-out model behavior.</p></div>
             </div>
             <div class="suite-line-facts">
               <span><strong>5,821</strong>frames</span>
             <small>line 2 / 128 selected episodes</small>
             <h3>128 selected episodes: comparison surface</h3>
             <p>Seven methods share the selected-episode surface and the same 20 task axes.</p>
+            <div class="line-readout">
+              <div><span>best use</span><p>Compare same-split baselines, Qwen3-Omni v6, and Cosmos3 rows.</p></div>
+              <div><span>read separately from</span><p>Direct raw-target metrics for the 6 proxy-marked cells.</p></div>
             </div>
             <div class="suite-line-facts">
               <span><strong>128</strong>selected episodes</span>
               <th>Line</th>
               <th>Data unit</th>
               <th>Score statement</th>
+              <th>Best use</th>
+              <th>Read separately from</th>
               <th>Start here</th>
             </tr>
           </thead>
               <td>1 sample episode</td>
               <td>One public Xperience-10M sample episode; 5,821 frames; 1,161 aligned 20-frame windows; 8,546-dimensional feature contract.</td>
               <td>40/40 direct scores from Minimal and Neural MLP heads.</td>
+              <td>Raw sample inspection, file organization, task definitions, local reproduction, and controlled Minimal-vs-Neural baseline behavior.</td>
+              <td>The selected-128 comparison rows and broader held-out model behavior.</td>
               <td><a href="#raw-sample">Raw browser</a><br><a href="data/single_episode_task_model_radar.json">1-episode radar 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 across metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super, Cosmos3-Nano, and scale-up decisions.</td>
+              <td>Direct raw-target interpretation for the proxy-marked cells.</td>
               <td><a href="data/episode128_task_model_radar.json">128-episode radar 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>
           <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 how to read each result family.</p>
             <a href="#overview">Read overview</a>
           </article>
           <article class="reader-step">
             </article>
             <article class="brief-card">
               <strong>Evaluation discipline</strong>
+              <p>Keeps chronological splits, predictions, confusion matrices, leakage notes, and single-episode limits visible before moving to broader model-quality reads.</p>
             </article>
             <article class="brief-card">
               <strong>Scale-up readiness</strong>
           <article class="roadmap-card" data-status="active_next_step">
             <span class="roadmap-status">active next step</span>
             <h3>Action/Subtask Error-Analysis Pass</h3>
+            <p>Keep the 96/16/16 split, tighten JSON decoding or target formatting, and analyze action/subtask failures before presenting stronger model-quality numbers.</p>
             <div class="roadmap-meta">
               <strong>Entry</strong><p>The final diagnostic package is verified, meets strict JSON validity, and exposes weak action/subtask quality.</p>
               <strong>Evidence</strong><p>Updated quality-target report, error-analysis tables, held-out metrics, and public-safe package.</p>
           <article class="artifact"><h3>Leakage controls</h3><p>Scalers fit on train windows only; future labels, target-side signals, caption/object labels, and contact labels stay on the target side unless explicitly queried.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_evaluation_protocol.py">builder script</a></article>
           <article class="artifact"><h3>Audio ablation</h3><p>Audio and no-audio variants are evaluated across the walkthrough-backed task contracts under the same chronological split.</p><a href="data/audio_ablation_summary.json">audio summary</a></article>
           <article class="artifact"><h3>Foundation track selection</h3><p>Qwen3-Omni is the first trainable baseline, Cosmos 3 is the world-model track with a camera-pose proxy forward-dynamics contract ready for trainer work, policy models wait for robot-compatible action targets, and Xperience-native pretraining remains a later full-corpus goal.</p><a href="data/foundation_model_plan.json">backbone plan</a></article>
+          <article class="artifact"><h3>Next evaluation stage</h3><p>This public-sample run covers single-episode task development. The selected multi-episode Qwen3-Omni final diagnostic result is verified and meets the JSON-validity target; Cosmos3-Nano has a verified future-window compatibility package; and Cosmos3-Super has a verified base-weight JSON-task evaluation plus a fine-tuned forward-dynamics LoRA branch. The next stage is action/subtask error analysis, stronger held-out metrics, and policy-target conversion.</p><a href="data/omni_model_comparison.json">result comparison</a></article>
           <article class="artifact"><h3>Selected-128 next stressor</h3><p>Before adding episodes, the suite should try `multiscale_20s10_40s20_80s40`, hierarchical action/subtask targets, label-normalized scoring, and compact raw-feature shards for unsupported tasks.</p><a href="data/task_suite_enhancement_128.json">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>
         <div class="artifact-grid">
           <article class="artifact primary-artifact"><div><h3>Official dataset</h3><p>Xperience-10M is a gated large-scale egocentric multimodal dataset for embodied AI, robotics, spatial intelligence, and world modeling.</p></div><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">official HF dataset</a></article>
+          <article class="artifact"><h3>Line 1 public sample</h3><p>The one-episode line builds the inspectable 20-task lab. Use Line 2 for selected-128 held-out comparison.</p><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">sample dataset</a></article>
           <article class="artifact"><h3>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>
           <article class="figure-brief-card">
             <h3>What the figure represents</h3>
+            <p>It documents the selected 128-episode final diagnostic result and the action/subtask improvement path for stronger held-out metrics.</p>
           </article>
         </div>
         <img class="pipeline-image lora-pipeline-image" src="assets/qwen3_omni_lora_pipeline.png?v=qwen3-lora-v1" alt="Detailed Qwen3-Omni LoRA training pipeline from raw Xperience-10M episodes to adapter outputs, predictions, metrics, and reports">
           <article class="result-reading-step">
             <span>01</span>
             <strong>Choose the line</strong>
+            <p>Use 1 episode for the task lab. Use 128 episodes for the selected comparison surface.</p>
           </article>
           <article class="result-reading-step">
             <span>02</span>
             <small>1 episode results</small>
             <h3>Task-lab evidence</h3>
             <p>Minimal and Neural MLP heads are both scored on all 20 public-sample task contracts. All 40 scores are direct task-target metrics.</p>
+            <div class="line-readout">
               <div><span>best read as</span><p>A reproducible public task suite and baseline behavior check.</p></div>
             </div>
             <div class="suite-line-facts">
             <small>128 episode results</small>
             <h3>Scale-up evidence</h3>
             <p>Metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window use the aligned 128-episode surface. It has 134 direct scores plus 6 compact-proxy scores.</p>
+            <div class="line-readout">
               <div><span>best read as</span><p>A same-split comparison table with explicit source and proxy status.</p></div>
             </div>
             <div class="suite-line-facts">
                 <thead>
                   <tr>
                     <th><span class="term-with-help"><span>Method</span><span class="term-help" tabindex="0" aria-label="Method row: One named method family in the matrix." title="One named method family in the matrix.">i<span class="term-tooltip" role="tooltip">One named method family in the matrix, such as Minimal, 128ep Raw NN, Qwen3-Omni v6, or Cosmos3-Super.</span></span></span></th>
+                    <th><span class="term-with-help"><span>Line</span><span class="term-help" tabindex="0" aria-label="Evidence line: A reading lane for a group of results." title="A reading lane for a group of results.">i<span class="term-tooltip" role="tooltip">A reading lane for a group of results: Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison.</span></span></span></th>
                     <th><span class="term-with-help"><span>Records</span><span class="term-help" tabindex="0" aria-label="Task-method record: One method evaluated on one task." title="One method evaluated on one task.">i<span class="term-tooltip" role="tooltip">One method evaluated on one task. 9 methods x 20 tasks gives 180 public result records.</span></span></span></th>
                     <th><span class="term-with-help"><span>Direct</span><span class="term-help" tabindex="0" aria-label="Direct score: A metric computed against the task target directly." title="A metric computed against the task target directly.">i<span class="term-tooltip" role="tooltip">A metric computed against the task target directly. This is the preferred score type in the 20-task matrix.</span></span></span></th>
                     <th><span class="term-with-help"><span>Proxy</span><span class="term-help" tabindex="0" aria-label="Compact-proxy score: A bounded proxy metric when a direct raw target is not publicly available." title="A bounded proxy metric when a direct raw target is not publicly available.">i<span class="term-tooltip" role="tooltip">A bounded proxy metric when a direct raw target is not publicly available. It stays explicit so readers do not over-read it.</span></span></span></th>
           <div class="glossary-summary" aria-label="Glossary categories">
             <article>
               <small>scope</small>
+              <strong>Separate data, result lanes, 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>
                   </tr>
                 </thead>
                 <tbody>
+                  <tr><td>Evidence line</td><td>A reading lane for a group of 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>The selected-128 comparison rows.</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>
           </article>
           <article class="resource-mode">
             <small>verify</small>
+            <strong>Check result 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>
               <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 result-reading lanes.</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>
           <section class="artifact-group tabbed-panel" id="artifact-panel-scale-up" role="tabpanel" aria-labelledby="artifact-tab-scale-up" hidden>
             <div class="artifact-group-head">
               <div><span>Scale-up path</span><h3>Verified diagnostic pilot</h3></div>
+              <p>The multi-episode Qwen3-Omni path is documented, scripted, and verified as a validation-monitored held-out pilot. The next stronger metrics come from structured-output and error-analysis improvements.</p>
             </div>
             <div class="artifact-grid">
               <article class="artifact primary-artifact"><div><h3>Two-line model comparison</h3><p>Groups Line 1 task-head baselines and Line 2 selected-128 methods: metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Nano Future Window, and Cosmos3-Super Reasoner.</p></div><a href="data/omni_model_comparison.json">result comparison</a></article>
               <article class="artifact"><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 source facts from local/public project inventory and derived artifacts.</p><a href="data/source_alignment_audit.json">source alignment</a></article>
               <article class="artifact"><h3>Task surface validation</h3><p>Checks task-count, task-contract, and result-matrix consistency across generated public data files.</p><a href="data/task_surface_integrity.json">task surface</a></article>
               <article class="artifact"><h3>Quality gates</h3><p>Summarizes build, validation, mirror, and publication gates that should pass before readers rely on the release.</p><a href="data/quality_gates.json">quality gates</a></article>
             </div>
       "normalized radar value": "A 0-1 plotting value used only to draw comparable radar polygons across metrics with different scales.",
       "task-method record": "One method evaluated on one task. 9 methods x 20 tasks gives 180 public result records.",
       "unified 20-task suite": "The current task surface: all 20 task contracts presented together and scored across methods where real artifacts exist.",
+      "evidence line": "A reading lane for a group of results: Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison.",
       "public sample episode": "The fully inspectable Line 1 unit used for raw-file browsing, task construction, and single-episode baselines.",
       "selected 128 episodes": "Line 2 uses derived windows/features and keeps links back to official gated episode ids and source paths.",
       "minimal baseline": "A simple non-neural task head used as a low-complexity comparison on the public sample episode.",