# Can LLMs Uncover Hidden Physical Laws through Free Exploration? [Canonical reading](https://escapemeridian.com/readings/can-llms-uncover-hidden-physical-laws-through-free-exploration-ee3f5f7f) Zhen Yang, Wen Wang, Xander Xu and Ying-Cong Chen introduce LongChang, a framework for testing whether LLMs can investigate unexpected behavior and uncover altered physical laws without being told what to investigate. LongChang creates simulated worlds where one or more laws differ from ordinary physics. The LLMs explore by manipulating objects and building tools, while separate agents generate and review scenes, tools and reports. The authors report that GPT-6-Astra achieved a discovery rate of 16.7%; the other 11 evaluated LLMs scored 0%. They also report that exploration records contained anomalies the LLMs had not investigated and only partial identification of the altered laws. The findings describe both discoveries and limits in this setup: exploration yielded discoveries, but clues went unpursued and the altered laws were only partly identified. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Oct 27, 2026 (2026-10-27). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-14T03:31:43.210Z. First observed: 2026-10-28T07:04:37.586Z. Retrieved: 2026-10-30T23:02:28.675Z. - [Can LLMs Uncover Hidden Physical Laws through Free Exploration?](https://arxiv.org/abs/2610.37649) ## Limits The reported evaluation uses simulated worlds with altered physics; the abstract does not establish whether the results extend beyond this setting. The authors report uninvestigated anomalies and only partial identification of altered laws, so the reported discoveries do not amount to complete identification. ## Open question Which altered physical laws were identified, and what counted as a discovery in the evaluation? ## Source-stated The authors introduce LongChang to evaluate whether LLMs can discover altered physical laws through exploration without being given research tasks or hints. > we introduce LongChang, an automated framework that creates simulated worlds with altered physics - [Supporting source](https://arxiv.org/abs/2610.37649) ## Source-stated In LongChang, LLMs investigate by manipulating objects and building tools, while separate agents generate and review scenes, tools and reports. > The evaluated LLMs act as explorers, manipulating objects and building tools to investigate their own questions - [Supporting source](https://arxiv.org/abs/2610.37649) ## Source-stated Of the 12 evaluated LLMs, GPT-6-Astra had a discovery rate of 16.7%; the other 11 scored 0%. > Among 12 LLMs, GPT-6-Astra achieves a discovery rate of 16.7%, while the other 11 score 0%. - [Supporting source](https://arxiv.org/abs/2610.37649) ## Source-stated The authors report that exploration records contained anomalies the LLMs had not investigated and only partial identification of altered laws. > The exploration records reveal uninvestigated anomalies and only partial identification of the altered physical laws. - [Supporting source](https://arxiv.org/abs/2610.37649) ## Source and contributor context Yang, Zhen, Wang, Wen, Xu, Xander, Chen, Ying-Cong · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 26546021-f224-4293-87c3-6dacfe0f824a. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-14T03:31:43.210Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: d47c0d118aa6b32fc745c1db5c98b0bba37c285649416cbf3bf89e083b76cb08. Source text hash: 2655ab539982baefc82a84b577d5deb24b87fe9eff7562ce84de0b04a68353f3. ## Revision and source-check history ### Version 1 — 2026-11-14T03:31:43.210Z Zhen Yang, Wen Wang, Xander Xu and Ying-Cong Chen introduce LongChang, a framework for testing whether LLMs can investigate unexpected behavior and uncover altered physical laws without being told what to investigate. LongChang creates simulated worlds where one or more laws differ from ordinary physics. The LLMs explore by manipulating objects and building tools, while separate agents generate and review scenes, tools and reports. The authors report that GPT-6-Astra achieved a discovery rate of 16.7%; the other 11 evaluated LLMs scored 0%. They also report that exploration records contained anomalies the LLMs had not investigated and only partial identification of the altered laws. The findings describe both discoveries and limits in this setup: exploration yielded discoveries, but clues went unpursued and the altered laws were only partly identified. ## Original source arXiv cs.AI · Article Original source date: Oct 27, 2026 (2026-10-27). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-14T03:31:43.210Z. First observed: 2026-10-28T07:04:37.586Z. Retrieved: 2026-10-30T23:02:28.675Z. - [Can LLMs Uncover Hidden Physical Laws through Free Exploration?](https://arxiv.org/abs/2610.37649) ## Limits The reported evaluation uses simulated worlds with altered physics; the abstract does not establish whether the results extend beyond this setting. The authors report uninvestigated anomalies and only partial identification of altered laws, so the reported discoveries do not amount to complete identification. ## Open question Which altered physical laws were identified, and what counted as a discovery in the evaluation? ## Source-stated The authors introduce LongChang to evaluate whether LLMs can discover altered physical laws through exploration without being given research tasks or hints. > we introduce LongChang, an automated framework that creates simulated worlds with altered physics - [Supporting source](https://arxiv.org/abs/2610.37649) ## Source-stated In LongChang, LLMs investigate by manipulating objects and building tools, while separate agents generate and review scenes, tools and reports. > The evaluated LLMs act as explorers, manipulating objects and building tools to investigate their own questions - [Supporting source](https://arxiv.org/abs/2610.37649) ## Source-stated Of the 12 evaluated LLMs, GPT-6-Astra had a discovery rate of 16.7%; the other 11 scored 0%. > Among 12 LLMs, GPT-6-Astra achieves a discovery rate of 16.7%, while the other 11 score 0%. - [Supporting source](https://arxiv.org/abs/2610.37649) ## Source-stated The authors report that exploration records contained anomalies the LLMs had not investigated and only partial identification of altered laws. > The exploration records reveal uninvestigated anomalies and only partial identification of the altered physical laws. - [Supporting source](https://arxiv.org/abs/2610.37649) ## Source and contributor context Yang, Zhen, Wang, Wen, Xu, Xander, Chen, Ying-Cong · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 26546021-f224-4293-87c3-6dacfe0f824a. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-14T03:31:43.210Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: d47c0d118aa6b32fc745c1db5c98b0bba37c285649416cbf3bf89e083b76cb08. Source text hash: 2655ab539982baefc82a84b577d5deb24b87fe9eff7562ce84de0b04a68353f3. ### passed — 2026-11-14T03:31:43.210Z — anthropic/claude-opus-5.5 Publication: 26546021-f224-4293-87c3-6dacfe0f824a attribution — pass: The draft names the four listed authors, presents LongChang as their framework and attributes the results with phrasing such as 'The authors report.' It does not present the source's claims as established fact or as Escape Meridian's view. > Authors:Zhen Yang, Wen Wang, Xander Xu, Ying-Cong Chen summary — pass: The reading leads with the actors, the framework and the open-ended setup, then gives the headline result and the stated limits. The question about what counted as a discovery is a real gap in the abstract. Optional repairs: spell out large language models once, add the abstract's 'heavier objects falling more slowly' example, and say that LLM agents judged report correctness. The closing line 'exploration yielded discoveries' is accurate, but it should keep in view that only one of 12 models scored above 0%. > can large language models (LLMs) notice unexpected behavior, such as heavier objects falling more slowly, and discover the law through experiments without being told what to investigate? date — pass: evidence.originalPublished is 2026-10-27. This matches the arXiv v1 submission date. The feed date of 2026-10-29 is later and is correctly not used. > \[Submitted on 27 Oct 2026\] support — pass: Each claim matches the abstract verbatim in substance: the framework, the explorer role, the 16.7% versus 0% result and the uninvestigated anomalies. The limitations stay within the evidence and do not turn a simulated benchmark score into real-world scientific ability. No benefits, costs or forecasts are invented. > Among 12 LLMs, GPT-6-Astra achieves a discovery rate of 16.7%, while the other 11 score 0%. media — pass: The selected media ID is the verified direct link to the original arXiv abstract page, which is the source itself. No better source media was found. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/ee3f5f7fad902b1967ef2492fb631be5847ebedbc50dd928c2c7609b89b7dd70/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/can-llms-uncover-hidden-physical-laws-through-free-exploration-ee3f5f7f.json)