arXiv Machine Learning
Jun 2
Can Vision Language Models Learn Intuitive Physics from Interaction?
arXiv:2602. 06033v2 Announce Type: replace Abstract: Pre-trained vision language models do not have good intuitions about the physical world.
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.
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arXiv AI
Sep 4
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.
arXiv Computation and Language
Sep 15
PhysMent: An Interactive Approach For LLM Reasoning In Physics Problems
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...
arXiv AI
Aug 14
PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research
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.
arXiv Machine Learning
Jun 3
Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing
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.
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.
arXiv AI
Sep 28
PhysElite: How Far Are LLMs from Solving Olympiad-Level Physics Problems?
PhysElite is a new bilingual multimodal benchmark designed to evaluate large language models on Olympiad-level physics problems. It contains 11,586 problems, each paired with visual diagrams, step-by-step bilingual Chinese‑English solution derivations, and the final answer. Benchmarking 18 models revealed that even the best reaches only 33.7% accuracy, and a step‑level analysis highlights where models falter in reasoning.
arXiv Machine Learning
Jun 4
PE-MHL: Physics-Encoded Modular Hybrid Layers for Scalable Learning of Complex Systems
arXiv:2606. 04290v1 Announce Type: new Abstract: Hybrid models that combine physics-based and data-driven components have shown strong potential for achieving accuracy and interpretability in control applications.
arXiv AI
Jul 7
Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models
arXiv:2607. 05199v1 Announce Type: new Abstract: Physics reasoning fails structurally in small language models: an error at any step propagates forward, corrupting every inference that follows.
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.
arXiv AI
4d ago
pi-Net: Empirical Latent Representations for Differentiable Physics Heads in Data-Scarce Systems
arXiv:2610.16504v1 Announce Type: cross Abstract: Developing reliable predictive models for multi-physics systems often forces a compromise between computationally demanding physical simulators and d...
arXiv Machine Learning
Jul 30
EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks
arXiv:2607. 26490v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs), yet their performance heavily relies on the manual, trial-and-error engineering of neural representations, loss formulations, and optimization dynamics.