arXiv Computation and Language
PhysMent: An Interactive Approach For LLM Reasoning In Physics Problems
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
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.
arXiv Machine Learning
Sep 2
DiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinking
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...
arXiv AI
2d ago
Lost in Perception: Isolating Perceptual and Reasoning Failures in Multimodal Physics and Geometry Reasoning
The paper introduces a five-task diagnostic experiment that separates perceptual and reasoning failures in multimodal large language models on physics and geometry benchmarks. It finds that misinterpreting diagrams hurts performance even on text-only solvable problems, and that accuracy improves when models receive human-authored captions. The study shows that correcting captions can recover many errors, revealing distinct reasoning bottlenecks that differ by domain, while a heavily pretrained model still underperforms and often truncates reasoning traces.
Hugging Face Trending Papers
Jun 2
NewtPhys: Do Foundation Models Understand Newtonian Physics?
Previous work has evaluated physics reasoning in foundation models using synthetic or semi-synthetic scenes and visual question-answering tasks. However, these benchmarks emphasize high-level events and lack the visual fidelity required to assess true low-level Newtonian understanding.
arXiv AI
Aug 18
Large Language Models and their Awareness of Mechanics and Spatial Geometry
arXiv:2608. 14615v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well on established code-generation and mathematical-reasoning benchmarks, but their capabilities in mechanics and spatial geometry, here denoted as mechanical engineering awareness, has not been quantified systematically.
arXiv AI
Jun 30
Hierarchical Experimentalist Agents
arXiv:2606. 29315v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to take actions in the real world and support human decision-making, yet most agents rely on parametric knowledge, fixed post-training data, retrieval, or search.