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world-models.io
The Knowledge Hub for AI World Models

world-models.io - The Knowledge Hub for AI World Models

A structured discovery layer for world models across robotics, model-based reinforcement learning, simulation engines, embodied AI, and autonomous systems.

robotics model-based-rl simulation embodied-ai

Quick Answer

Short extractable summary preserved directly in static HTML.

  • world-models.io is a structured knowledge hub for understanding, comparing, and tracking AI world models across robotics, model-based reinforcement learning, simulation, and embodied AI.
  • Use it to find relevant models quickly, understand how the field is organized, and move from high-level discovery to model pages, comparisons, guides, and research topics.
  • The site is organized so readers can move from a high-level category or guide into concrete model records, then into comparisons, research context, and methodology pages without losing the thread of the topic.
  • That structure is intended to reduce search friction and make the field legible both to human readers and to systems that need stable, extractable answers.
  • The homepage is optimized to answer three kinds of questions quickly: which entities matter, which options differ in practice, and which sources or methodology pages should be checked before reusing a claim.
  • The broader purpose of the homepage is to separate overview, comparison, methodology, and source validation so the field can be navigated without collapsing very different systems into one vague category.

Editorial Trust Signals

Editorial provenance and refresh policy preserved directly in static HTML.

Published by world-models.io editorial board.

Lead editor Bernard Grenat.

world-models.io publishes structured definitions, model records, comparisons, and research explainers designed for citation, retrieval, and verification.

Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-06-21.

Pages are refreshed when a new paper, benchmark, release, architecture update, or stronger primary source materially changes the answer a reader or AI system should retrieve.

Each page links back to relevant primary sources and keeps a stable canonical URL so readers can verify claims, trace context, and reference the most up-to-date version. See the editorial policy.

Primary sources onlyLast reviewed date visibleMethodology documentedSource links included

External Sources

Primary references and official sources surfaced directly in static HTML for crawlers and no-JS readers.