mlsanityData to 14 Oct 2026

SimLM: Can Language Models Infer Parameters of Physical Systems?

arXiv 2312.14215 ↗First posted 21 Dec 2023cs.CL cs.AIcited by 5

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Abstract (arXiv)

Several machine learning methods aim to learn or reason about complex physical systems. A common first-step towards reasoning is to infer system parameters from observations of its behavior. In this paper, we investigate the performance of Large Language Models (LLMs) at performing parameter inference in the context of physical systems. Our experiments suggest that they are not inherently suited to this task, even for simple systems. We propose a promising direction of exploration, which involves the use of physical simulators to augment the context of LLMs. We assess and compare the performance of different LLMs on a simple example with and without access to physical simulation.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 21 Dec 2023.

Builds on

22 of 40 references.

References · 22

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10 more
  1. 2022Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoningcited by 334
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2024LLMPhy: Parameter-Identifiable Physical Reasoning Combining Large Language Models and Physics Enginescited by 30
  2. 2024A Survey on Large Language Model-empowered Autonomous Drivingcited by 4
  3. 2025Teaching Physical Awareness to LLMs through Soundscited by 4
  4. 2025CueTip: An Interactive and Explainable Physics-aware Pool Assistantcited by 3
  5. 2026Battery-Sim-Agent: Leveraging LLM-Agent for Inverse Battery Parameter Estimationcited by 0