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World models

Name variants world model · 世界模型

Here's a question straight out of sci-fi: What if AI could predict what you were going to do before you decided to, even in a completely new situation?

This explanation quotes or summarizes the cited source record. Saving creates a bookmark on this device, with no automatic account sync.

First observed in our collection
Oct 9, 2026, 3:45 PM UTC
Earliest cited publication
Jan 12, 2026, 4:42 PM UTC
Last observed
Nov 5, 2026, 3:41 PM UTC

Evidence in the last complete seven days

Source mentions
8
Previous seven days 9
Distinct sources
3
Previous 3
Comparable growth
Not comparable
Ranking appearances
0

Collection observations and undated records are separate from dated publication counts. Observed in 7d:7 · Undated records:0

Collection coverage · Limited historyCurrent / previous observed days 7 / 3

Counts use complete UTC days. First observed means first seen by this collection, not when a term was coined.

Current window:2026-10-29 → 2026-11-05 (end excluded) · History 11 days

Observed source coverage by seven-day window
SourceCurrent daysPrevious daysLast success
arXiv63Nov 5, 2026, 10:00 AM UTC
arXiv72Nov 5, 2026, 10:01 AM UTC
Ask HN73Nov 5, 2026, 3:00 PM UTC
Ask HN73Nov 5, 2026, 2:30 PM UTC
Ask HN73Nov 5, 2026, 3:00 PM UTC
Ask HN73Nov 5, 2026, 3:00 PM UTC
GitHub73Nov 5, 2026, 12:15 PM UTC
GitHub Issues73Nov 5, 2026, 10:01 AM UTC
GitHub Releases71Nov 5, 2026, 12:00 PM UTC
GitHub73Nov 5, 2026, 2:30 PM UTC
GitHub Issues73Nov 5, 2026, 10:01 AM UTC
GitHub Releases73Nov 5, 2026, 3:14 PM UTC
GitHub73Nov 5, 2026, 2:30 PM UTC
GitHub Issues73Nov 5, 2026, 10:01 AM UTC
GitHub Releases73Nov 5, 2026, 3:14 PM UTC
GitHub Discovery00Nov 5, 2026, 3:13 PM UTC
GitHub Trending73Nov 5, 2026, 3:14 PM UTC
Google72Nov 5, 2026, 10:00 AM UTC
Hugging Face72Nov 5, 2026, 8:15 AM UTC
Hugging Face Spaces00Nov 5, 2026, 3:13 PM UTC
Hacker News73Nov 5, 2026, 12:45 PM UTC
Hacker News73Nov 5, 2026, 3:30 PM UTC
Hacker News73Nov 5, 2026, 3:30 PM UTC
Hacker News00Nov 5, 2026, 3:13 PM UTC
Show HN00Nov 5, 2026, 3:13 PM UTC
MIT News72Nov 5, 2026, 10:01 AM UTC
OpenAI73Nov 5, 2026, 11:30 AM UTC
Product Hunt73Nov 5, 2026, 12:45 PM UTC
量子位73Nov 4, 2026, 10:15 AM UTC
OpenRouter00Nov 5, 2026, 3:41 PM UTC
Hugging Face Models00Nov 5, 2026, 3:41 PM UTC
HF Daily Papers00Nov 5, 2026, 3:41 PM UTC
OpenRouter00Nov 5, 2026, 3:41 PM UTC
SEC EDGAR73Nov 5, 2026, 10:30 AM UTC
Sequoia Capital72Nov 5, 2026, 10:00 AM UTC
Simon Willison00Nov 5, 2026, 3:13 PM UTC
TechCrunch73Nov 5, 2026, 2:30 PM UTC
Contracts Finder73Nov 5, 2026, 10:00 AM UTC
V2EX73Nov 5, 2026, 2:30 PM UTC
  • Counts describe monitored public sources, not the whole web or search demand.
  • Both weeks need adequate, comparable collection coverage.

Daily history · up to 90 days

Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.

2026-10-092026-11-05

Dotted markers indicate days with no record. They are not counted as zero mentions.

View accessible daily data
Daily source mentions and collection observations
UTC dateMentionsSourcesObservationsRanking appearances
2026-10-091110
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Source reference for 42AI comes out of stealth with $62.5M and a world model that can ‘predict human behavior’. Screenshot not yet available.
Source record · preview pending · techcrunch.comOpen the source for the full page.

Source evidence

Sources in the evidence shown:TechCrunch 4 · arXiv 7 · 量子位 14 · HF Daily Papers 1 · Hacker News 1

  1. TechCrunch · Published Nov 2, 2026

    42AI comes out of stealth with $62.5M and a world model that can ‘predict human behavior’

    Here's a question straight out of sci-fi: What if AI could predict what you were going to do before you decided to, even in a completely new situation?

    View source record →
  2. arXiv · Published Nov 2, 2026

    Beyond Plausible Prediction: Safety Sufficiency in Web World Models

    arXiv:2610.40798v1 Announce Type: new Abstract: Web world models increasingly predict the consequences of candidate actions before agents act, yet a plausible predicted future does not necessarily support the correct safety decision. In a matched human evaluation of 3,000 predictions from ten frozen open models, 86.1% are judged semantically plausible. Howev

    View source record →
  3. 量子位 · Published Nov 1, 2026

    三年前布局,今年爆火丨西湖大学成立新中心

    西湖世界模型研究中心成立

    View source record →
  4. 量子位 · Published Nov 1, 2026

    清华姚期智团队新作:代数世界模型,让AI像牛顿一样思考

    让AI像牛顿一样思考

    View source record →
  5. 量子位 · Published Oct 31, 2026

    世界模型首次指导设计芯片!超大核NPU700TOPS,99%算力可用

    AI算力利用“九九成”

    View source record →
  6. HF Daily Papers · Published Oct 31, 2026

    GameVerse: A Minute-Scale Gameplay Dataset for Long-Horizon Interactive World Modeling

    Recent advances in interactive world models have made it possible to generate controllable, dynamic environments from action inputs. Yet current models struggle to maintain coherence beyond roughly one minute of rollout, a limitation rooted not in model capacity but in the scarcity of long-horizon training data. Existing gameplay video datasets typically con

  7. 量子位 · Published Oct 30, 2026

    买车送终身AI?智己LS8纯电版25万起!全系线控底盘宁德时代标配

    Momenta世界模型上车

    View source record →
  8. TechCrunch · Published Oct 29, 2026

    Ex-Palantir execs launch Dream with $17M from a16z to build AI world models for construction

    Dream is grounded in real physics and construction standards. Just as LLMs learn from text, Dream's world model is trained on more than 100 million square feet of building data gathered from permit archives across the United States.

    View source record →
  9. TechCrunch · Published Oct 27, 2026

    Google’s Genie 4 world model adds audio, more realistic worlds, and real-time steering

    Genie 4 can maintain a consistent scene for up to two minutes, double Genie 3's limit, and offers better physics and more humanlike characters.

    View source record →
  10. arXiv · Published Oct 27, 2026

    $\Psi$-0.6: Adaptive Streams of Thought in a Unified Vision-Language World Model

    arXiv:2610.32177v1 Announce Type: new Abstract: Humans reason about the physical world by flexibly combining different forms of inference as a problem unfolds. Achieving this flexibility is a major open challenge in AI: capabilities often remain separated across specialized models, limiting how they can be composed and jointly adapted to a task. We introduce

    View source record →
  11. arXiv · Published Oct 27, 2026

    The Social World Model Evaluation Gap

    arXiv:2610.33014v1 Announce Type: new Abstract: A negotiation assistant can reach a good deal without accurately predicting the other person's response. We ask whether evaluations of social world models test that prediction directly. We audit 113 works published from 1999 to 2026. 9 primary evaluations measure a real person's response to an action selected b

    View source record →
  12. 量子位 · Published Oct 23, 2026

    企业AI需要两个世界模型

    特赞想让企业Agent真正懂业务

    View source record →
  13. 量子位 · Published Oct 23, 2026

    一张5090跑起34B实时世界模型,国产玩家拿下英伟达大奖!

    房间秒变草原,抬手就能下雪

    View source record →
  14. arXiv · Published Oct 23, 2026

    From Single-Actor Environments to Multi-Actor Event Worlds: Can LLMs Be Event World Models?

    arXiv:2610.30488v1 Announce Type: new Abstract: World models help agents assess the consequences of their actions before acting. Many real systems, however, involve multiple actors who make their own decisions, can evolve even when the agent takes no action, and are only partially observed through event logs. We formalize these systems as event worlds and as

    View source record →
  15. arXiv · Published Oct 23, 2026

    Anticipating Social Consequences with Distributional World Models

    arXiv:2610.30657v1 Announce Type: new Abstract: Social world models predict how interactions develop after an action, helping agents negotiate, cooperate and mediate. In these situations, one action can lead to several plausible futures, hence predicting only one future can overlook consequential alternatives, while exploring them through repeated simulation

    View source record →
  16. TechCrunch · Published Oct 22, 2026

    Rivian buys world model startup to accelerate self-driving ambitions

    Third Dimension's technology uses AI to match the physical world, which when combined with Rivian's reams of data could turn into a valuable simulator.

    View source record →
  17. arXiv · Published Oct 22, 2026

    Self-Improving Agents with World Model Predictive Paths

    arXiv:2610.28945v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly expected to interact with environments and improve their decisions during deployment. However, how anticipating future outcomes and drawing on accumulated interaction experience can jointly support decision-making remains underexplored. We propose a test-time f

    View source record →
  18. 量子位 · Published Oct 21, 2026

    生数科技Motubrain团队将直播Physical AI训练全流程,持续半个月

    10月21日,生数科技世界模型团队正式开启Motubrain 2.0训练全流程公开直播

    View source record →
  19. 量子位 · Published Oct 21, 2026

    LeCun押注的世界模型,中国一家清华系公司已经做进了机器人

    世界模型赛道新看点:千诀隐变量可辨识性方向的研究,比LeCun相近工作早一年,不堆画面预测,主打物理规律与跨本体复用。

    View source record →
  20. 量子位 · Published Oct 19, 2026

    2026物理AI与世界模型大会医疗健康世界模型分会场在南京举行

    View source record →
  21. 量子位 · Published Oct 18, 2026

    VAST开源实时世界模型EdenLive!单消费级显卡、720P超24FPS

    View source record →
  22. 量子位 · Published Oct 16, 2026

    清华特奖得主AI创业再+1:GeoPT一作入局物理世界模型

    View source record →
  23. 量子位 · Published Oct 16, 2026

    从“看懂世界”到“预演下一步”:世界模型与 VLA,如何让机器人拥有真正的“具身大脑”?

    View source record →
  24. 量子位 · Published Oct 15, 2026

    全球首款 !中国团队把游戏世界模型塞进一张5090,还开源了

    View source record →
  25. 量子位 · Published Oct 14, 2026

    国内最年轻AI教授创业:要让世界模型走向全身全模态

    View source record →
  26. arXiv · Published Oct 9, 2026

    LLM-IDEA: Identifiability-Driven Experimental Agent for Autonomous Discovery of Mechanistic World Models

    View source record →
  27. Hacker News · Published Jan 12, 2026

    1X World Model – From Video to Action: A New Way Robots Learn

    View source record →

Review priority 74 / 100

This score orders research candidates. It is not search volume, a forecast or verified commercial demand.

  • Evidence

    25.4 / 30

    Distinct dated articles or first observations; repeat crawling adds nothing.

  • Publisher diversity

    24 / 25

    Conservative owner grouping; syndicated copies count once.

  • Comparable change

    0 / 20

    Comparable complete weeks only; no percentage from a zero baseline.

  • Recent evidence

    15 / 15

    Latest publication or first observation, not repeated fetch time.

  • Name specificity

    9.7 / 10

    Explicit entities and technical concepts outrank ambiguous acronyms.

Limitations and interpretation

  • First observation is a collection milestone, not proof that a word or product is new to the world.
  • Counts describe monitored public sources, not the whole web or search demand.
  • First observed here is not the invention date of a name or idea.
  • Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
  • Owner grouping is conservative; undisclosed syndication may remain.
  • Repeat ranking appearances are observations, not new articles or independent endorsements.
  • An established concept or common product name retained as a baseline, not a newly invented term.
  • Both weeks need adequate, comparable collection coverage.
  • Sources behind this term changed or lack a comparable baseline.
  • Recent evidence includes collection over three days after publication.

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