arXiv:2602. 06033v2 Announce Type: replace Abstract: Pre-trained vision language models do not have good intuitions about the physical world.
By Luca M. Schulze Buschoff, Konstantinos Voudouris, Can Demircan, Eric Schulz
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.
By Sean Memery, Kartic Subr
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
arXiv:2512. 19799v2 Announce Type: replace Abstract: Advances in LLM reasoning and tool use have enabled agentic science, yet frontier theoretical and computational physics remains challenging because research requires deep domain expertise, long-horizon reasoning, and reliable numerical computation.
By Tingjia Miao, Wenkai Jin, Jinxin Tan, Muhua Zhang, Xianghe Pang, Zexi Liu, Yuwen Du, Tian Jin, Tu Guo, Zhengliang Zhang, Jingkun Liu, Yuelin Hu, Jiejun Zhang, Yunjie Huang, Yuhan Wang, Wenbo Li, Yinuo Gao, Shuo Chen, Rui Ye, Yuzhi Zhang, Linfeng Zhang, Kun Chen, Wei Wang, Weinan E, Siheng Chen
arXiv:2604. 07366v2 Announce Type: replace Abstract: Partial differential equations (PDEs) govern nearly every physical process in science and engineering, but solving them at scale remains prohibitively expensive.
By Yilong Dai, Shengyu Chen, Xiaowei Jia, Runlong Yu
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