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Assessing Adaptive World Models in Machines with Novel Games (with 13 co-authors)

François Chollet · 17 Jul 2025

Read the paper on arxiv.org ↗

5 korrents from this paper ↓

In plain words

People adapt fast by quickly building mental models of how new places work, yet AI checks only look at fixed knowledge from big training sets. This opens concrete tests of whether machines can figure out brand-new rules through limited play the way people do. It is a perspective proposing suites of games with genuine hidden novelty as benchmarks, drawing on cognitive science and pushing past static model evaluations.

Our summary of the paper, not the authors' words — written to be readable without the field's vocabulary, from the stored copy of the paper and nothing else. Drafted with xai:grok-4.5 and checked by a person. The authors' own sentences are the quotes below.

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François Chollet did not write this page.

Every claim below was made in this piece, quoted word for word and numbered in the order the piece makes them, so you can read it there rather than take our word for it. The sentence above each quote is our reading of the claim, not their wording. Each quote was checked against a stored copy of the page at build time; where the two differ, the quote is the fact.

  1. Our reading

    Human adaptability is fundamentally linked to the efficient construction and refinement of internal world models

    Their wordsWe argue that this profound adaptability is fundamentally linked to the efficient construction and refinement of internal representations of the environment, commonly referred to as world models, and we refer to this adaptation mechanism as world model induction.

  2. Our reading

    Current AI evaluation of world models is too narrow, favoring static representations learned from massive datasets over efficient learning through interaction

    Their wordsHowever, current understanding and evaluation of world models in artificial intelligence (AI) remains narrow, often focusing on static representations learned from training on massive corpora of data, instead of the efficiency and efficacy in learning these representations through interaction and exploration within a novel environment.

  3. Our reading

    Rapid world model induction will be critical for achieving robust, general AI that functions in complex, fast-changing real and human worlds

    Their wordsWe expect that building and evaluating AI systems capable of this kind of rapid world model induction will be critical for achieving robust, general AI capable of functioning effectively in the complex and fast-changing real world, and especially in human worlds - the environments that human beings have evolved in, created, and are continually changing and re-creating.

  4. Our reading

    An adaptive agent's world model cannot be a static representation learned once and fixed

    Their wordsFor adaptation to complex and changing environments, an agent's world models cannot be static representations learned once and fixed.

  5. Our reading

    Games are particularly rich and controlled environments uniquely well-suited for evaluating rapid world model induction in AI

    Their wordsWe contend that games provide particularly rich and controlled environments uniquely well-suited for systematically evaluating rapid model adaptation and the process of world model induction.

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