arXiv AI September 4, 2026 By Sean Memery, Kartic Subr

Discovering High Level Patterns from Simulation Traces

The paper proposes an unsupervised learning approach that uses program synthesis to translate detailed simulation traces into sparse, high‑level structural patterns, making them easier for large language models to interpret. These pattern detectors can be guided by human‑provided labels such as "rigid collision" or "stretching spring" and produce transparent, explainable functions mapping system states to concise annotations. Experiments on a physics benchmark show that the annotated representations improve natural language reasoning about specific physical systems and enable natural‑language goals to be converted into reward programs for solution search.

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arXiv Computation and Language

Aug 25

Decoupled Physical Modeling and Execution for Physics Reasoning

The paper introduces a framework that separates physical modeling from execution in physics reasoning tasks. It uses a two‑stage post‑training approach: supervised fine‑tuning to build structured models and reinforcement learning with rubric‑based feedback to refine them. Experiments on PhysReason, PhyX, and SeePhys show that this explicit modeling improves reasoning performance by about 3% on average for small LLMs.

By Ye Zhang, Xuehang Guo, Rui Pan, Pengfei Yu, Denghui Zhang, Manling Li, Qingyun Wang

llms reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Aug 14

Training and Benchmarking Code Generation for Physics-Inspired Animations

arXiv:2602. 10840v2 Announce Type: replace Abstract: Large language models (LLMs) have been widely studied in areas such as mathematical reasoning, complex coding, and scientific problem solving.

By Yanan Wang, Renxi Wang, Yongxin Wang, Xuezhi Liang, Fajri Koto, Timothy Baldwin, Xiaodan Liang, Haonan Li

llms reinforcement-learning multimodal benchmarks data-engineering

arXiv AI

Jun 2

Towards a Physics Foundation Model

arXiv:2509. 13805v4 Announce Type: replace-cross Abstract: Foundation models have revolutionized natural language processing through a ``train once, deploy anywhere'' paradigm, where a single pre-trained model adapts to countless downstream tasks without retraining.

By Florian Wiesner, Zo\"e J. Gray, Matthias Wessling, Stephen Baek

llms nlp

arXiv AI

Aug 5

Uncovering Spontaneous Physics Representations in In-Context Learning

arXiv:2508. 12448v2 Announce Type: replace-cross Abstract: In-context learning (ICL) lets large language models (LLMs) solve new tasks from prompts alone, across an ever-widening range of domains, yet the mechanisms underlying this ability remain poorly understood.

By Yeongwoo Song, Jaeyong Bae, Dong-Kyum Kim, Hawoong Jeong

llms

arXiv Machine Learning

Aug 31

Aligning Agentic World Models via Knowledgeable Experience Learning

arXiv:2601.13247v2 Announce Type: replace-cross Abstract: Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural ground...

By Baochang Ren, Yunzhi Yao, Rui Sun, Shuofei Qiao, Ningyu Zhang, Huajun Chen

llms agents fine-tuning safety

arXiv Computer Vision

Aug 31

Code as Worlds: Agentic Discovery of Executable World Representations for Physical Reasoning

arXiv:2608.27549v1 Announce Type: new Abstract: Physical understanding and reasoning depend on forming compact and generalizable representations of the world. While modern vision-language models can...

By Hanyang Wang, Yimo Cai, Weiliang Chen, Jiawei Chi, Haowen Sun, Qiyu Dai, Yi-Hsin Hung, Xingzhuo Guo, Jinshan Ren, Runmao Yao, Ziwei Liu, Mingsheng Long, Yueqi Duan, Jun Gao, Jiangran Lyu, Fangfu Liu, Jialong Wu

llms agents multimodal benchmarks