ChronoPhyBench is a new benchmark and dataset for chronological physical dynamics reasoning that combines video-conditioned next-state prediction with VQA to reduce language bias in MLLM evaluation.
Ugphysics: A comprehensive benchmark for undergraduate physics reasoning with large language models
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Small 7B reasoning models were fine-tuned on synthetic and curated QFT problems using RL and SFT, yielding performance gains, error analysis, and public release of data and traces.
Entropy minimization on self-generated outputs elicits strong reasoning in pretrained LLMs, matching or exceeding supervised RL methods on benchmarks.
ViSA-R2 recovers single executable SymPy expressions for linear steady-state fields from visualizations using a self-verifying chain-of-thought that recognizes patterns, hypothesizes solution families, derives parameters, and checks consistency.
A survey of physical AI that distinguishes theoretical physics reasoning from applied understanding and synthesizes advances in symbolic reasoning, embodied systems, and generative models to advocate for physics-grounded world models.
citing papers explorer
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ChronoPhyBench: Do MLLMs Truly Understand the World or Merely Exploit Language Priors?
ChronoPhyBench is a new benchmark and dataset for chronological physical dynamics reasoning that combines video-conditioned next-state prediction with VQA to reduce language bias in MLLM evaluation.
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Fine-Tuning Small Reasoning Models for Quantum Field Theory
Small 7B reasoning models were fine-tuned on synthetic and curated QFT problems using RL and SFT, yielding performance gains, error analysis, and public release of data and traces.
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The Unreasonable Effectiveness of Entropy Minimization in LLM Reasoning
Entropy minimization on self-generated outputs elicits strong reasoning in pretrained LLMs, matching or exceeding supervised RL methods on benchmarks.
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Hidden in Plain Sight: Visual-to-Symbolic Analytical Solution Inference from Field Visualizations
ViSA-R2 recovers single executable SymPy expressions for linear steady-state fields from visualizations using a self-verifying chain-of-thought that recognizes patterns, hypothesizes solution families, derives parameters, and checks consistency.
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Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI
A survey of physical AI that distinguishes theoretical physics reasoning from applied understanding and synthesizes advances in symbolic reasoning, embodied systems, and generative models to advocate for physics-grounded world models.