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README.md

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+---
+title: World Model Registry
+emoji: 🌐
+colorFrom: blue
+colorTo: indigo
+sdk: static
+pinned: false
+---
+
+# World Model Registry
+
+### Interactive registry for world models, world action models and Physical AI
+
+**World Model Registry** is a data-driven Hugging Face Space for exploring and comparing world models across:
+
+- world-model type
+- architecture
+- modalities
+- action conditioning
+- interactivity
+- prediction target
+- planning support
+- control support
+- robotics
+- Physical AI
+- simulation
+- synthetic-data generation
+- parameter count
+- access status
+- license
+- commercial-use status
+- base model
+- associated paper
+- verification date
+
+The Space reads from:
+
+**`world-models/world-model-registry`**
+
+> **Not every predictive video model is the same kind of world model.**
+
+The registry therefore distinguishes between categories such as:
+
+```text
+World Foundation Model
+World Action Model
+Interactive World Model
+Planning World Model
+Action-Conditioned Video World Model
+Predictive Representation / World-Model Backbone
+```
+
+---
+
+## What the Space provides
+
+### Registry filters
+
+Filter by:
+
+- world-model type
+- architecture
+- modality
+- domain
+- action-conditioned
+- interactive
+- planning support
+- control support
+- robotics
+- Physical AI
+- simulation
+- access status
+- license
+- maximum parameter count
+
+### Compact detail view
+
+Each model has a focused detail panel containing:
+
+- taxonomy
+- modalities
+- domains
+- prediction targets
+- action-conditioning
+- planning and control support
+- robotics / Physical AI relevance
+- simulation support
+- parameter count
+- access status
+- license
+- commercial-use status
+- base model
+- paper
+- source links
+- verification date
+- registry notes
+
+### Direct model comparison
+
+Select two models and compare their world-model characteristics side by side.
+
+Differences are highlighted.
+
+The interface does **not** rank models and does not select a winner.
+
+---
+
+## Dataset
+
+Primary data source:
+
+`world-models/world-model-registry`
+
+The Space attempts to load the latest:
+
+`data/train.jsonl`
+
+directly from the dataset repository.
+
+A small embedded fallback snapshot is included so the Space remains usable if live loading temporarily fails.
+
+---
+
+## Classification principles
+
+```text
+USE THE MODEL CARD
+LINK THE SOURCE
+SEPARATE DOCUMENTED FACTS FROM INTERPRETATION
+MARK GAPS INSTEAD OF GUESSING
+```
+
+---
+
+## Important taxonomy note
+
+World-model terminology is not fully standardized.
+
+For example:
+
+- a world foundation model may be broad and generative;
+- a world action model may connect prediction with action generation;
+- an interactive world model may support closed-loop control;
+- a predictive representation model may model latent dynamics without acting as a full simulator.
+
+The registry makes those distinctions explicit.
+
+---
+
+## Related World Models resources
+
+- `world-models/world-models-explorer`
+- `world-models/world-model-benchmark`
+- `world-models/world-model-landscape`
+- `world-models/world-model-registry`
+
+---
+
+## Collaboration & Partnerships
+
+The **World Models** organization is open to collaboration with:
+
+- world-model research teams
+- robotics companies
+- Physical AI companies
+- autonomous-system developers
+- simulation platforms
+- research labs
+- universities
+- model developers
+- infrastructure providers
+
+Relevant areas include:
+
+- registry contributions
+- world-model taxonomy
+- World Action Models
+- interactive simulation
+- planning
+- control
+- Physical AI
+- robotics
+- benchmarks
+- dataset integration
+- model metadata
+
+### Collaboration Contact
+
+**agenten@magenta.de**
+
+---
+
+## Independence
+
+This is an independent Hugging Face community Space.
+
+It is not an official registry of Hugging Face, NVIDIA, Meta or any other model developer listed in the registry.
+
+All model names and trademarks belong to their respective owners.

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+<header class="hero">
+<div class="wrap hero-inner">
+  <div>
+    <span class="kicker">World Models · Physical AI · Robotics · Simulation</span>
+    <h1>World Model <span class="grad">Registry</span></h1>
+    <div class="subtitle">
+      Explore and compare world foundation models, world action models, interactive simulators,
+      planning world models and predictive world-model backbones.
+    </div>
+  </div>
+  <div class="sourcebox">
+    <b>Dataset source</b>
+    <span>world-models/world-model-registry</span>
+    <div class="status"><span class="dot" id="statusDot"></span><span id="statusText">Loading registry…</span></div>
+  </div>
+</div>
+</header>
+
+<main class="wrap">
+<nav class="navtabs">
+  <a href="#compare">Direct comparison</a>
+  <a href="#registry">Registry</a>
+</nav>
+
+<section class="panel section" id="compare">
+  <div class="compare-head">
+    <div>
+      <h2>Direct World Model Comparison</h2>
+      <p>Select two registry entries and inspect their taxonomy, action conditioning, planning, control, simulation and Physical AI characteristics side by side.</p>
+    </div>
+  </div>
+
+  <div class="compare-controls">
+    <div class="field"><label>Model A</label><select id="modelA"></select></div>
+    <button id="swap">⇄ Swap</button>
+    <div class="field"><label>Model B</label><select id="modelB"></select></div>
+    <button class="primary" id="copyLink">Copy comparison link</button>
+  </div>
+
+  <div class="compare-grid" id="compareGrid"></div>
+</section>
+
+<section class="detail-shell section" id="modelDetail" hidden>
+  <div class="detail-top">
+    <div>
+      <div class="detail-kicker">Compact world-model detail</div>
+      <div class="detail-title" id="detailTitle">Model</div>
+      <div class="detail-sub" id="detailSub"></div>
+    </div>
+    <div class="detail-actions">
+      <button id="detailToA">Use as Model A</button>
+      <button id="detailToB">Use as Model B</button>
+      <a id="detailRepo" class="primary-link" href="#" target="_blank" rel="noopener">Open repository</a>
+      <button id="detailClose">Close</button>
+    </div>
+  </div>
+  <div class="detail-body">
+    <div class="detail-summary" id="detailSummary"></div>
+    <div class="detail-grid" id="detailGrid"></div>
+    <div class="detail-notes">
+      <b>Registry note</b>
+      <div id="detailNotes">—</div>
+      <div class="detail-sources" id="detailSources"></div>
+    </div>
+  </div>
+</section>
+
+<section class="panel filters section" id="registry">
+  <div class="filter-grid">
+    <div class="field"><label>Search</label><input id="q" type="search" placeholder="Model, organization, type…"></div>
+    <div class="field"><label>World-model type</label><select id="type"><option value="">All types</option></select></div>
+    <div class="field"><label>Architecture</label><select id="arch"><option value="">All architectures</option></select></div>
+    <div class="field"><label>Modality</label><select id="modality"><option value="">All modalities</option></select></div>
+    <div class="field"><label>Domain</label><select id="domain"><option value="">All domains</option></select></div>
+    <div class="field"><label>Access</label><select id="access"><option value="">All access</option></select></div>
+  </div>
+
+  <div class="filter-grid-2">
+    <div class="field"><label>Action conditioned</label><select id="action"><option value="">Any</option><option value="true">Yes</option><option value="false">No</option></select></div>
+    <div class="field"><label>Interactive</label><select id="interactive"><option value="">Any</option><option value="true">Yes</option><option value="false">No</option></select></div>
+    <div class="field"><label>Planning</label><select id="planning"><option value="">Any</option><option value="true">Yes</option><option value="false">No</option></select></div>
+    <div class="field"><label>Control</label><select id="control"><option value="">Any</option><option value="true">Yes</option><option value="false">No</option></select></div>
+    <div class="field"><label>Physical AI</label><select id="physical"><option value="">Any</option><option value="true">Yes</option><option value="false">No</option></select></div>
+    <div class="field"><label>Simulation</label><select id="simulation"><option value="">Any</option><option value="true">Yes</option><option value="false">No</option></select></div>
+  </div>
+
+  <div class="range-row">
+    <div class="field"><label>Maximum parameter count (B)</label><input id="maxParams" type="number" min="0" step="1" placeholder="e.g. 5"></div>
+  </div>
+
+  <div class="controls">
+    <div class="stats"><strong id="count">0</strong> models shown · registry verified <strong id="verified">—</strong></div>
+    <button id="reset">Reset filters</button>
+  </div>
+
+  <div class="note">
+    World-model taxonomy is still evolving. Registry fields are source-linked descriptive metadata, not performance rankings.
+  </div>
+</section>
+
+<section class="registry">
+  <div class="table-wrap" id="tableWrap">
+    <table>
+      <thead>
+        <tr>
+          <th>Model</th><th>Type</th><th>Modalities</th><th>Domain</th>
+          <th>Action</th><th>Interactive</th><th>Planning</th><th>Control</th>
+          <th>Physical AI</th><th>Simulation</th><th>Params</th><th>Access</th><th>License</th><th>Verified</th><th>Details</th>
+        </tr>
+      </thead>
+      <tbody id="rows"></tbody>
+    </table>
+  </div>
+  <div class="empty" id="empty" hidden>No models match the current filters.</div>
+</section>
+</main>
+
+<footer>
+<div class="wrap">
+  <strong>World Model Registry</strong> — independent community registry.<br>
+  Collaboration: <strong>agenten@magenta.de</strong>
+</div>
+</footer>
+
+<script>
+const DATASET_URL="https://huggingface.co/datasets/world-models/world-model-registry/resolve/main/data/train.jsonl?download=true";
+const fallback=[{"repo_id": "nvidia/Cosmos-Predict2-2B-Video2World", "organization": "nvidia", "model_name": "Cosmos-Predict2-2B-Video2World", "world_model_type": "world foundation model", "architecture_family": "latent video diffusion transformer", "modalities": ["text", "image", "video"], "domain": ["physical-ai", "world-generation"], "action_conditioned": false, "interactive": false, "prediction_target": ["future video frames", "world states"], "planning_support": false, "control_support": false, "robotics": true, "physical_ai": true, "spatial_reasoning": true, "simulation": true, "synthetic_data": true, "parameter_count_b": 2.0, "weights_available": true, "access_status": "gated", "license": "nvidia-open-model-license", "commercial_use": "permitted_by_license", "base_model": null, "paper_id": null, "verification_status": "verified", "last_verified": "2026-09-24", "source_urls": ["https://huggingface.co/nvidia/Cosmos-Predict2-2B-Video2World"], "notes": "NVIDIA describes Cosmos-Predict2 as a world foundation model family for physics-aware world generation and Physical AI."}, {"repo_id": "nvidia/Cosmos-Predict2-14B-Video2World", "organization": "nvidia", "model_name": "Cosmos-Predict2-14B-Video2World", "world_model_type": "world foundation model", "architecture_family": "latent video diffusion transformer", "modalities": ["text", "image", "video"], "domain": ["physical-ai", "world-generation"], "action_conditioned": false, "interactive": false, "prediction_target": ["future video frames", "world states"], "planning_support": false, "control_support": false, "robotics": true, "physical_ai": true, "spatial_reasoning": true, "simulation": true, "synthetic_data": true, "parameter_count_b": 14.0, "weights_available": true, "access_status": "gated", "license": "nvidia-open-model-license", "commercial_use": "permitted_by_license", "base_model": null, "paper_id": null, "verification_status": "verified", "last_verified": "2026-09-24", "source_urls": ["https://huggingface.co/nvidia/Cosmos-Predict2-14B-Video2World"], "notes": "Larger Cosmos Predict-2 Video2World checkpoint for higher-fidelity world generation."}, {"repo_id": "nvidia/Cosmos-Policy-LIBERO-Predict2-2B", "organization": "nvidia", "model_name": "Cosmos-Policy-LIBERO-Predict2-2B", "world_model_type": "world action model / visuomotor policy", "architecture_family": "latent video diffusion transformer", "modalities": ["text", "multi-view image", "proprioception", "action"], "domain": ["robotics", "manipulation", "simulation"], "action_conditioned": true, "interactive": true, "prediction_target": ["actions", "future images", "future proprioception", "value"], "planning_support": true, "control_support": true, "robotics": true, "physical_ai": true, "spatial_reasoning": true, "simulation": true, "synthetic_data": false, "parameter_count_b": 2.0, "weights_available": true, "access_status": "public", "license": "nvidia-one-way-noncommercial-license-nsclv1", "commercial_use": "not_permitted_without_custom_license", "base_model": "nvidia/Cosmos-Predict2-2B-Video2World", "paper_id": "2601.16163", "verification_status": "verified", "last_verified": "2026-09-24", "source_urls": ["https://huggingface.co/nvidia/Cosmos-Policy-LIBERO-Predict2-2B"], "notes": "Jointly predicts actions, future states and values for LIBERO robot manipulation tasks."}, {"repo_id": "nvidia/Cosmos-Policy-RoboCasa-Predict2-2B", "organization": "nvidia", "model_name": "Cosmos-Policy-RoboCasa-Predict2-2B", "world_model_type": "world action model / visuomotor policy", "architecture_family": "latent video diffusion transformer", "modalities": ["text", "multi-view image", "proprioception", "action"], "domain": ["robotics", "kitchen-manipulation", "simulation"], "action_conditioned": true, "interactive": true, "prediction_target": ["actions", "future images", "future proprioception", "value"], "planning_support": true, "control_support": true, "robotics": true, "physical_ai": true, "spatial_reasoning": true, "simulation": true, "synthetic_data": false, "parameter_count_b": 2.0, "weights_available": true, "access_status": "public", "license": "nvidia-one-way-noncommercial-license-nsclv1", "commercial_use": "not_permitted_without_custom_license", "base_model": "nvidia/Cosmos-Predict2-2B-Video2World", "paper_id": "2601.16163", "verification_status": "verified", "last_verified": "2026-09-24", "source_urls": ["https://huggingface.co/nvidia/Cosmos-Policy-RoboCasa-Predict2-2B"], "notes": "Cosmos Policy checkpoint for RoboCasa kitchen manipulation, predicting actions, future state and value."}, {"repo_id": "nvidia/Cosmos-Policy-ALOHA-Planning-Model-Predict2-2B", "organization": "nvidia", "model_name": "Cosmos-Policy-ALOHA-Planning-Model-Predict2-2B", "world_model_type": "planning world model", "architecture_family": "latent video diffusion transformer", "modalities": ["text", "multi-view image", "proprioception", "action"], "domain": ["robotics", "bimanual-manipulation", "real-world"], "action_conditioned": true, "interactive": true, "prediction_target": ["future images", "future proprioception", "value"], "planning_support": true, "control_support": true, "robotics": true, "physical_ai": true, "spatial_reasoning": true, "simulation": true, "synthetic_data": false, "parameter_count_b": 2.0, "weights_available": true, "access_status": "public", "license": "nvidia-one-way-noncommercial-license-nsclv1", "commercial_use": "not_permitted_without_custom_license", "base_model": "nvidia/Cosmos-Policy-ALOHA-Predict2-2B", "paper_id": "2601.16163", "verification_status": "verified", "last_verified": "2026-09-24", "source_urls": ["https://huggingface.co/nvidia/Cosmos-Policy-ALOHA-Planning-Model-Predict2-2B"], "notes": "Refined world-model and value checkpoint for best-of-N model-based planning on real ALOHA manipulation tasks."}, {"repo_id": "nvidia/Cosmos-H-Dreams", "organization": "nvidia", "model_name": "Cosmos-H-Dreams", "world_model_type": "interactive world model", "architecture_family": "causal latent video diffusion transformer", "modalities": ["image", "robot kinematics", "video"], "domain": ["surgical-robotics", "interactive-simulation", "healthcare"], "action_conditioned": true, "interactive": true, "prediction_target": ["future video frames"], "planning_support": true, "control_support": true, "robotics": true, "physical_ai": true, "spatial_reasoning": true, "simulation": true, "synthetic_data": true, "parameter_count_b": 2.0, "weights_available": true, "access_status": "public", "license": "nvidia-open-model-license", "commercial_use": "permitted_by_license", "base_model": "nvidia/Cosmos-Predict2.5-2B-Video2World", "paper_id": "2511.00062", "verification_status": "verified", "last_verified": "2026-09-24", "source_urls": ["https://huggingface.co/nvidia/Cosmos-H-Dreams"], "notes": "Real-time action-conditioned surgical world model for interactive simulation and policy evaluation."}, {"repo_id": "sii-research/tau-0-wm", "organization": "sii-research", "model_name": "tau-0-wm", "world_model_type": "unified video-action world model", "architecture_family": "video diffusion backbone", "modalities": ["multi-view video", "language", "robot state", "action"], "domain": ["robotics", "manipulation", "real-world"], "action_conditioned": true, "interactive": true, "prediction_target": ["future visual latents", "continuous actions", "task-progress value"], "planning_support": true, "control_support": true, "robotics": true, "physical_ai": true, "spatial_reasoning": true, "simulation": true, "synthetic_data": false, "parameter_count_b": null, "weights_available": true, "access_status": "public", "license": "apache-2.0", "commercial_use": "permitted_by_license", "base_model": "Wan-2.2 video diffusion backbone", "paper_id": "2606.01027", "verification_status": "verified", "last_verified": "2026-09-24", "source_urls": ["https://huggingface.co/sii-research/tau-0-wm", "https://huggingface.co/papers/2606.01027"], "notes": "Unified Video Action Model plus action-conditioned simulator for robotic manipulation."}, {"repo_id": "Fleurrr/A2World-World-Model", "organization": "Fleurrr", "model_name": "A2World-World-Model", "world_model_type": "action-conditioned video world model", "architecture_family": "multi-view diffusion world model", "modalities": ["multi-view image", "robot action", "video"], "domain": ["robotics", "manipulation", "simulation"], "action_conditioned": true, "interactive": false, "prediction_target": ["future robot observations", "video rollout"], "planning_support": true, "control_support": false, "robotics": true, "physical_ai": true, "spatial_reasoning": true, "simulation": true, "synthetic_data": false, "parameter_count_b": 2.0, "weights_available": true, "access_status": "public", "license": "nvidia-open-model-license", "commercial_use": "subject_to_nvidia_open_model_license", "base_model": "nvidia/Cosmos-Predict2-2B-Video2World", "paper_id": "2606.29501", "verification_status": "verified", "last_verified": "2026-09-24", "source_urls": ["https://huggingface.co/Fleurrr/A2World-World-Model", "https://huggingface.co/papers/2606.29501"], "notes": "Predicts future robot observations from initial camera observations and a 20-step action chunk; supports autoregressive rollout."}, {"repo_id": "BLM-Lab/Boundless-World-Model", "organization": "BLM-Lab", "model_name": "Boundless-World-Model", "world_model_type": "action-conditioned video world model", "architecture_family": "DiT + action encoder", "modalities": ["video", "robot action"], "domain": ["robotics", "manipulation"], "action_conditioned": true, "interactive": false, "prediction_target": ["future video frames"], "planning_support": false, "control_support": false, "robotics": true, "physical_ai": true, "spatial_reasoning": true, "simulation": true, "synthetic_data": false, "parameter_count_b": 5.0, "weights_available": true, "access_status": "public", "license": "not_documented_on_hf_model_card", "commercial_use": "unknown", "base_model": "Wan-AI/Wan2.2-TI2V-5B", "paper_id": null, "verification_status": "verified_with_license_gap", "last_verified": "2026-09-24", "source_urls": ["https://huggingface.co/BLM-Lab/Boundless-World-Model"], "notes": "Physically consistent action-conditioned video world model; license was not displayed on the Hugging Face model card at verification time."}, {"repo_id": "facebook/vjepa2-vitg-fpc64-384", "organization": "facebook", "model_name": "V-JEPA 2 ViT-g 384", "world_model_type": "predictive representation / world-model backbone", "architecture_family": "joint-embedding predictive architecture", "modalities": ["video", "image"], "domain": ["video-understanding", "physical-world-representation"], "action_conditioned": false, "interactive": false, "prediction_target": ["latent video representations"], "planning_support": false, "control_support": false, "robotics": true, "physical_ai": true, "spatial_reasoning": true, "simulation": false, "synthetic_data": false, "parameter_count_b": 1.0, "weights_available": true, "access_status": "public", "license": "apache-2.0", "commercial_use": "permitted_by_license", "base_model": null, "paper_id": "2506.09985", "verification_status": "verified", "last_verified": "2026-09-24", "source_urls": ["https://huggingface.co/facebook/vjepa2-vitg-fpc64-384", "https://huggingface.co/papers/2506.09985"], "notes": "Base V-JEPA 2 checkpoint is action-free; the associated research post-trains V-JEPA 2-AC as an action-conditioned world model for robotic planning."}];
+
+let data=[];
+const $=id=>document.getElementById(id);
+const els={
+  modelA:$("modelA"),modelB:$("modelB"),swap:$("swap"),copyLink:$("copyLink"),compareGrid:$("compareGrid"),
+  q:$("q"),type:$("type"),arch:$("arch"),modality:$("modality"),domain:$("domain"),access:$("access"),
+  action:$("action"),interactive:$("interactive"),planning:$("planning"),control:$("control"),physical:$("physical"),simulation:$("simulation"),
+  maxParams:$("maxParams"),rows:$("rows"),count:$("count"),verified:$("verified"),empty:$("empty"),tableWrap:$("tableWrap"),
+  reset:$("reset"),statusDot:$("statusDot"),statusText:$("statusText"),
+  modelDetail:$("modelDetail"),detailTitle:$("detailTitle"),detailSub:$("detailSub"),detailSummary:$("detailSummary"),
+  detailGrid:$("detailGrid"),detailNotes:$("detailNotes"),detailSources:$("detailSources"),detailRepo:$("detailRepo"),
+  detailToA:$("detailToA"),detailToB:$("detailToB"),detailClose:$("detailClose")
+};
+
+function esc(v){return String(v??"—").replace(/[&<>"']/g,c=>({"&":"&amp;","<":"&lt;",">":"&gt;","\"":"&quot;","'":"&#039;"}[c]))}
+function arr(v){return Array.isArray(v)?v:(v?[v]:[])}
+function uniq(a){return [...new Set(a.filter(Boolean))].sort((a,b)=>String(a).localeCompare(String(b)))}
+function fillSelect(el,values,first=null){
+  el.innerHTML=(first!==null?`<option value="">${esc(first)}</option>`:"")+values.map(v=>`<option value="${esc(v)}">${esc(v)}</option>`).join("");
+}
+function boolBadge(v){return `<span class="badge ${v?"yes":"no"}">${v?"Yes":"No"}</span>`}
+function n(v){return v==null?"—":Number(v).toLocaleString("en-US",{maximumFractionDigits:1})}
+function pretty(v){return v==null||v===""?"—":String(v).replaceAll("_"," ")}
+function same(a,b){
+  const norm=v=>Array.isArray(v)?JSON.stringify([...v].sort()):String(v??"");
+  return norm(a)===norm(b);
+}
+
+function init(){
+  const options=data.map(x=>({value:x.repo_id,label:`${x.model_name} — ${x.organization}`}));
+  els.modelA.innerHTML=options.map(o=>`<option value="${esc(o.value)}">${esc(o.label)}</option>`).join("");
+  els.modelB.innerHTML=els.modelA.innerHTML;
+
+  const p=new URLSearchParams(location.search);
+  const qa=p.get("a"),qb=p.get("b");
+  els.modelA.value=data.some(x=>x.repo_id===qa)?qa:data[0]?.repo_id||"";
+  els.modelB.value=data.some(x=>x.repo_id===qb)?qb:data[Math.min(1,data.length-1)]?.repo_id||"";
+
+  fillSelect(els.type,uniq(data.map(x=>x.world_model_type)),"All types");
+  fillSelect(els.arch,uniq(data.map(x=>x.architecture_family)),"All architectures");
+  fillSelect(els.modality,uniq(data.flatMap(x=>arr(x.modalities))),"All modalities");
+  fillSelect(els.domain,uniq(data.flatMap(x=>arr(x.domain))),"All domains");
+  fillSelect(els.access,uniq(data.map(x=>x.access_status)),"All access");
+  els.verified.textContent=uniq(data.map(x=>x.last_verified)).slice(-1)[0]||"—";
+
+  renderCompare();
+  renderRegistry();
+}
+
+const compareFields=[
+ ["Repository","repo_id",x=>`<a href="https://huggingface.co/${encodeURI(x.repo_id)}" target="_blank" rel="noopener">${esc(x.repo_id)}</a>`],
+ ["World-model type","world_model_type",x=>esc(x.world_model_type)],
+ ["Architecture","architecture_family",x=>esc(x.architecture_family)],
+ ["Modalities","modalities",x=>arr(x.modalities).map(v=>`<span class="badge">${esc(v)}</span>`).join("")||"—"],
+ ["Domains","domain",x=>arr(x.domain).map(v=>`<span class="badge">${esc(v)}</span>`).join("")||"—"],
+ ["Prediction target","prediction_target",x=>arr(x.prediction_target).map(v=>`<span class="badge">${esc(v)}</span>`).join("")||"—"],
+ ["Action-conditioned","action_conditioned",x=>boolBadge(x.action_conditioned)],
+ ["Interactive","interactive",x=>boolBadge(x.interactive)],
+ ["Planning support","planning_support",x=>boolBadge(x.planning_support)],
+ ["Control support","control_support",x=>boolBadge(x.control_support)],
+ ["Robotics","robotics",x=>boolBadge(x.robotics)],
+ ["Physical AI","physical_ai",x=>boolBadge(x.physical_ai)],
+ ["Spatial reasoning","spatial_reasoning",x=>boolBadge(x.spatial_reasoning)],
+ ["Simulation","simulation",x=>boolBadge(x.simulation)],
+ ["Synthetic data","synthetic_data",x=>boolBadge(x.synthetic_data)],
+ ["Parameters","parameter_count_b",x=>x.parameter_count_b==null?"—":`${n(x.parameter_count_b)}B`],
+ ["Access","access_status",x=>`<span class="badge ${x.access_status==="public"?"public":"gated"}">${esc(x.access_status)}</span>`],
+ ["License","license",x=>esc(x.license)],
+ ["Commercial use","commercial_use",x=>esc(pretty(x.commercial_use))],
+ ["Base model","base_model",x=>esc(x.base_model||"—")],
+ ["Paper","paper_id",x=>x.paper_id?`<a href="https://huggingface.co/papers/${encodeURIComponent(x.paper_id)}" target="_blank" rel="noopener">${esc(x.paper_id)}</a>`:"—"],
+ ["Last verified","last_verified",x=>esc(x.last_verified)]
+];
+
+function renderCompare(){
+  const a=data.find(x=>x.repo_id===els.modelA.value)||data[0];
+  const b=data.find(x=>x.repo_id===els.modelB.value)||data[1]||data[0];
+  if(!a||!b)return;
+
+  const head=`<div class="ccell labelcell modelhead">Field</div>
+    <div class="ccell modelhead"><b>${esc(a.model_name)}</b><small>${esc(a.organization)} · ${esc(a.world_model_type)}</small></div>
+    <div class="ccell modelhead"><b>${esc(b.model_name)}</b><small>${esc(b.organization)} · ${esc(b.world_model_type)}</small></div>`;
+
+  const rows=compareFields.map(([label,key,fmt])=>{
+    const diff=!same(a[key],b[key]);
+    return `<div class="ccell labelcell">${esc(label)}</div>
+      <div class="ccell ${diff?"diff":""}">${fmt(a)}</div>
+      <div class="ccell ${diff?"diff":""}">${fmt(b)}</div>`;
+  }).join("");
+
+  els.compareGrid.innerHTML=head+rows;
+  const url=new URL(location.href);
+  url.searchParams.set("a",a.repo_id); url.searchParams.set("b",b.repo_id);
+  history.replaceState(null,"",url);
+}
+
+let detailRepoId=null;
+function openDetail(repoId,scroll=true){
+  const x=data.find(m=>m.repo_id===repoId);
+  if(!x)return;
+  detailRepoId=x.repo_id;
+  els.detailTitle.textContent=x.model_name||x.repo_id;
+  els.detailSub.textContent=`${x.organization||"—"} · ${x.world_model_type||"—"} · ${x.repo_id}`;
+  els.detailRepo.href=`https://huggingface.co/${encodeURI(x.repo_id)}`;
+
+  els.detailSummary.innerHTML=[
+    ["Type",esc(x.world_model_type)],
+    ["Action-conditioned",boolBadge(x.action_conditioned)],
+    ["Interactive",boolBadge(x.interactive)],
+    ["Physical AI",boolBadge(x.physical_ai)]
+  ].map(([k,v])=>`<div class="detail-stat"><span>${esc(k)}</span><b>${v}</b></div>`).join("");
+
+  const fields=[
+    ["Architecture",esc(x.architecture_family||"—")],
+    ["Modalities",arr(x.modalities).map(v=>`<span class="badge">${esc(v)}</span>`).join("")||"—"],
+    ["Domains",arr(x.domain).map(v=>`<span class="badge">${esc(v)}</span>`).join("")||"—"],
+    ["Prediction targets",arr(x.prediction_target).map(v=>`<span class="badge">${esc(v)}</span>`).join("")||"—"],
+    ["Planning support",boolBadge(x.planning_support)],
+    ["Control support",boolBadge(x.control_support)],
+    ["Robotics",boolBadge(x.robotics)],
+    ["Spatial reasoning",boolBadge(x.spatial_reasoning)],
+    ["Simulation",boolBadge(x.simulation)],
+    ["Synthetic data",boolBadge(x.synthetic_data)],
+    ["Parameters",x.parameter_count_b==null?"—":`${n(x.parameter_count_b)}B`],
+    ["Weights available",boolBadge(x.weights_available)],
+    ["Access",`<span class="badge ${x.access_status==="public"?"public":"gated"}">${esc(x.access_status||"—")}</span>`],
+    ["License",esc(x.license||"—")],
+    ["Commercial use",esc(pretty(x.commercial_use))],
+    ["Base model",esc(x.base_model||"—")],
+    ["Paper",x.paper_id?`<a href="https://huggingface.co/papers/${encodeURIComponent(x.paper_id)}" target="_blank" rel="noopener">${esc(x.paper_id)}</a>`:"—"],
+    ["Last verified",esc(x.last_verified||"—")]
+  ];
+  els.detailGrid.innerHTML=fields.map(([k,v])=>`<div class="detail-field"><div class="dlabel">${esc(k)}</div><div class="dvalue">${v}</div></div>`).join("");
+  els.detailNotes.textContent=x.notes||"No additional registry note.";
+  els.detailSources.innerHTML=arr(x.source_urls).map((u,j)=>`<a href="${esc(u)}" target="_blank" rel="noopener">Source ${j+1}</a>`).join("")||"<span>—</span>";
+
+  els.modelDetail.hidden=false;
+  if(scroll)els.modelDetail.scrollIntoView({behavior:"smooth",block:"start"});
+}
+function closeDetail(){els.modelDetail.hidden=true;detailRepoId=null}
+
+function boolFilter(value,selected){
+  if(selected==="")return true;
+  return String(Boolean(value))===selected;
+}
+
+function renderRegistry(){
+ const q=els.q.value.trim().toLowerCase(),type=els.type.value,arch=els.arch.value,mod=els.modality.value,
+       dom=els.domain.value,acc=els.access.value,maxParams=Number(els.maxParams.value||Infinity);
+
+ const filtered=data.filter(x=>{
+   const hay=[x.repo_id,x.organization,x.model_name,x.world_model_type,x.architecture_family,x.license,...arr(x.domain),...arr(x.modalities),...arr(x.prediction_target)].join(" ").toLowerCase();
+   const p=x.parameter_count_b==null?0:Number(x.parameter_count_b);
+   return (!q||hay.includes(q))
+     &&(!type||x.world_model_type===type)
+     &&(!arch||x.architecture_family===arch)
+     &&(!mod||arr(x.modalities).includes(mod))
+     &&(!dom||arr(x.domain).includes(dom))
+     &&(!acc||x.access_status===acc)
+     &&boolFilter(x.action_conditioned,els.action.value)
+     &&boolFilter(x.interactive,els.interactive.value)
+     &&boolFilter(x.planning_support,els.planning.value)
+     &&boolFilter(x.control_support,els.control.value)
+     &&boolFilter(x.physical_ai,els.physical.value)
+     &&boolFilter(x.simulation,els.simulation.value)
+     &&p<=maxParams;
+ });
+
+ els.count.textContent=filtered.length;
+ els.empty.hidden=filtered.length>0; els.tableWrap.hidden=filtered.length===0;
+
+ els.rows.innerHTML=filtered.map(x=>`<tr>
+   <td class="repo"><a href="https://huggingface.co/${encodeURI(x.repo_id)}" target="_blank" rel="noopener">${esc(x.model_name)}</a><small>${esc(x.organization)} · ${esc(x.architecture_family)}</small></td>
+   <td>${esc(x.world_model_type)}</td>
+   <td>${arr(x.modalities).map(v=>`<span class="badge">${esc(v)}</span>`).join("")}</td>
+   <td>${arr(x.domain).map(v=>`<span class="badge">${esc(v)}</span>`).join("")}</td>
+   <td>${boolBadge(x.action_conditioned)}</td>
+   <td>${boolBadge(x.interactive)}</td>
+   <td>${boolBadge(x.planning_support)}</td>
+   <td>${boolBadge(x.control_support)}</td>
+   <td>${boolBadge(x.physical_ai)}</td>
+   <td>${boolBadge(x.simulation)}</td>
+   <td class="num">${x.parameter_count_b==null?"—":n(x.parameter_count_b)+"B"}</td>
+   <td><span class="badge ${x.access_status==="public"?"public":"gated"}">${esc(x.access_status||"—")}</span></td>
+   <td>${esc(x.license||"—")}</td>
+   <td>${esc(x.last_verified||"—")}</td>
+   <td><button class="detail-btn" data-detail="${esc(x.repo_id)}">Details</button></td>
+ </tr>`).join("");
+
+ els.rows.querySelectorAll("[data-detail]").forEach(btn=>btn.addEventListener("click",()=>openDetail(btn.dataset.detail)));
+}
+
+async function load(){
+ try{
+   const res=await fetch(DATASET_URL,{cache:"no-store"});
+   if(!res.ok)throw new Error("HTTP "+res.status);
+   const text=await res.text();
+   const parsed=text.split(/\r?\n/).filter(Boolean).map(line=>JSON.parse(line));
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