The paper investigates whether large language models can autonomously discover altered physical laws in simulated environments. Using the LongChang framework, 12 LLMs explore worlds with modified physics, manipulate objects, and build tools without external guidance. Only GPT‑6‑Astra achieved a 16.7% discovery rate, while the others found none, revealing that current models struggle to fully identify hidden laws.
arXiv:2605.26087v2 Announce Type: replace-cross
Abstract: Frontier LLMs now perform strongly across a wide range of physics evaluations, but it is hard to disentangle genuine reasoning from recall of...
By Matt L. Wiemann, Lindsay M. Smith, Peter Melchior, Siddharth Mishra-Sharma, Andrew Gordon Wilson, Pavel Izmailov, Carolina Cuesta-L\'azaro
The paper presents an explore‑then‑commit protocol that uses a large language model to generate hypotheses, a programmatic planner to collect measurements, and a fresh prompt to synthesize a scientific law from fixed observations. In 576 NewtonBench trials across 12 physics modules, the protocol—especially when interpreter‑enabled planners are used—reduces the number of measurements needed and improves root‑mean‑squared logarithmic error for both GPT‑4.1‑mini and GPT‑4.1. The study demonstrates measurement savings in every module, though it notes that the causal components and generalization beyond noiseless direct‑equation tasks remain unresolved.
By Kautik Mandve, Dileepa Fernando
arXiv:2607. 05682v1 Announce Type: new Abstract: LLM systems for scientific discovery increasingly assist with ideation, literature synthesis, experiment planning, and report generation, but the first research question they propose can remain difficult to audit: it may sound plausible without exposing the mechanism, falsifier, or assumption that a scientist should inspect.
By Yufeng Wang
arXiv:2607. 00276v1 Announce Type: cross Abstract: Current large-language-model (LLM) physics benchmarks are usually scored by answer accuracy, which cannot distinguish genuine reasoning from recall of familiar problem patterns and reveals little about where a model's reasoning breaks down.
By Dong Zhang
arXiv:2609.01526v1 Announce Type: new
Abstract: Scientific agents must learn not only how to reason, but also what to believe. However, existing LLM agents typically express scientific hypotheses in...
By Qing Zhao, Haowei Li, Weijian Deng, Pengxu Wei, Liang Lin
The paper introduces LLM-IDEA, an identifiability‑driven experimental agent that guides large language model scientists in closed‑loop discovery of mechanistic world models. It incorporates an identifiability engine that classifies experimental plateaus as capability limits, resolvable within the design class, or certified exhausted, and demonstrates its effectiveness on benchmarks such as ODEBench, DiscoverPhysics, and a custom Alien Universe testbed. The agent’s ability to distinguish when further experimentation is futile versus when new designs are needed improves the efficiency and reliability of autonomous scientific discovery.
By Surya Shetty, Ulisses Braga-Neto
arXiv:2609.13152v1 Announce Type: new
Abstract: Large language models (LLMs) perform strongly on static science benchmarks, yet their ability to reason about the physical world through active experim...
By Joseph Chan, Utkarsh Jha, Xiyin Yang, Abhinav Jarajapu, Anik Sahai, Eddie Hu, Robin Jeshua Deepak, Stefano Saravalle, Aditya Shah
LLM-IDEA is an identifiability‑driven experimental agent that guides large language model scientists in autonomous discovery of mechanistic world models. It evaluates whether a plateau in learning is due to the agent’s capability limits, the model’s resolvability within the design class, or a certified exhaustion of the experiment space. The system demonstrates its effectiveness across benchmarks such as ODEBench, DiscoverPhysics, and a custom Alien Universe testbed, consistently identifying when additional experiments are futile and when new experimental designs are required.
SciGame is a new benchmark that transforms scientific literature into structured cards, each providing both an Original and a Counterfactual environment for the same task. It evaluates language‑model agents in identification and discovery scenarios under Prior‑only, Fixed, and Adaptive protocols, using historical canonical laws and a live temporal collection. Experiments with five advanced models reveal that prior knowledge can hurt performance when relations change, adaptive experimentation benefits strong models but not weak ones, and accurate explanations may mask quantitative errors, highlighting the difficulty of recovering exact symbolic laws.
By Yusheng Zhao, Bowei He, Xue Liu, Mingxiao Li
arXiv:2604. 12198v2 Announce Type: replace-cross Abstract: Autonomous LLM agents now produce complete research artifacts in machine-learning sandboxes, but real computational physics is harder: experiments are first-principles calculations against re-runnable physical ground truth, and meaningful new work almost always builds on a key existing paper.
By Haonan Huang
arXiv:2607. 07379v1 Announce Type: new Abstract: In agentic scientific machine learning (SciML), large language model (LLM) agents can discover surrogate models and select one by an automated score, typically an error metric.
By Diab W. Abueidda, Bilal Ahmed, Panos Pantidis, Mostafa E. Mobasher