World Models

Definition

A world model is a learned model of an environment's dynamics: given a state and an action, it predicts what happens next. The term is used in this wiki for two things that share a name and little else, and the distinction is the page's reason to exist:

• Generative world models — a video (and increasingly audio) model made interactive, so the generated stream responds to inputs as it runs. GWM Worlds 2 is this.
• Predictive world models for agents — a model an agent queries before acting, to anticipate the result of a candidate action without paying for it. Agent-Editing World Model: Rethinking World Modeling for LLM Agents is this.

Both were in this wiki's intake on the same day, from unrelated publishers, and neither cites the other's tradition.

Why It Matters

The two readings imply different tests, and conflating them means a release gets credit on the wrong one. A generative world model is judged on whether the stream stays coherent under control; a predictive one is judged on whether acting on its prediction raises task success. A video model that looks right has not thereby been shown to know what happens next, and a state predictor that raises success rates need not render anything at all.

This wiki accumulated world-model material for months without a page holding it: Cosmos-H-Dreams and Qwen-Drive-1.0-4B both carry the term in comparison tables, Embodied Agents uses it, and the two papers below arrived with GWM Worlds 2 in one run. The page is created here rather than left as scattered mentions.

State of the Art (as of 2026-09-26)

A world model shipped as a playable stream (2026-09-03)

Runway's GWM Worlds 2 generates continuous 720p video at 24 fps with 48 kHz audio, not restricted to a fixed duration, under an input format Runway calls WorldPrompt — the first frame and other elements are fixed up front, then timestamped events steer characters, cameras and environment, and events can be issued while generation runs. Stated recipe: fine-tune a base video model to the format, post-train for autoregressive generation, distil for real-time speed (source).

No benchmark accompanies it, which is the state of the art's honest summary for this branch: the demonstration is the evidence, and Runway itself states that real-time generation "trades fidelity for speed" without quantifying the trade.

Object permanence is measurable, and video models mostly lack it (2026-09-23)

WROP (Training Object Permanence in World Models, arXiv 2609.28654, 155 upvotes) asks whether video generation models have emerged object permanence and solidity, builds 150 hand-designed tasks across six cognitive categories with Blender generators randomising speed, lighting and camera angle while preserving each task's structure, and releases a 1.5M-sample training corpus and a 300-question exam. It evaluates 14 video models — 3 reference-to-video, 7 edit, 4 continuation — including its own 16B PWM-WROP, which in a blind pairwise Elo study ranks first among continuation models and third overall (source).

The framing matters more than the rank: it treats a cognitive prior as a trainable target with a held-out exam, where the branch above has no exam at all.

For agents, predicting the tool response turned out to be the wrong target (2026-09-23)

Agent-Editing World Model: Rethinking World Modeling for LLM Agents argues that reconstructing high-entropy, execution-dependent tool responses "offers limited value when real feedback is available", and models how reasoning and actions shape future task progress instead. Reported: 70.5% macro-F1 on its own Action Judge benchmark, 10.6 points over the strongest frontier baseline, and 3.2–6.7 points average improvement across six benchmarks and three agent backbones (source).

Domain world models continue as training substrate

Cosmos-H-Dreams (NVIDIA, surgical robotics) and Qwen-Drive-1.0-4B (Alibaba / Qwen AI Lab, driving) are the form this wiki has held longest: a world model built to train or evaluate an embodied policy, not to be watched. See Embodied Agents.

Open Problems

• Drift over an unbounded horizon. "Generates indefinitely" is the claim on GWM Worlds 2; whether the world stays consistent over that horizon is the interesting question and nothing read addresses it.
• No shared benchmark across the two readings. WROP scores video models on a cognitive exam; Agent-Editing scores an agent on task success. There is no instrument on which GWM Worlds 2 and Agent-Editing World Model: Rethinking World Modeling for LLM Agents could be compared, and none is proposed.
• Provenance. A model emitting arbitrary-length photoreal video and audio under live control has no stated watermarking on this wiki's only captured example. Contrast Content Provenance (AI output marking), where SynthID is stated for Gemini 3.8 Live's generated avatars.
• Whether fidelity and controllability trade off, and where. Asserted by Runway, unquantified by anyone.

Key Papers

• 2609.28654 — Training Object Permanence in World Models (WROP), 2026-09-23. Released data, exam, weights and training stack. No wiki page — captured here rather than as a page, per the one-off mention rule (source)
• Agent-Editing World Model: Rethinking World Modeling for LLM Agents — Agent-Editing World Model, 2026-09-23
• 2609.28466 — The Past Frames the Future: Memory for Autoregressive Video Generation, 2026-09-23, 37 upvotes. Read in the same snapshot and not promoted: bears directly on the drift problem above and no abstract figure was extracted for it this run (source)

Related Concepts

• Embodied Agents — where predictive world models are put to work
• Agents (LLM Agents) — the memory and state-tracking problems the agent branch shares
• Content Provenance (AI output marking) — the unanswered question on the generative branch