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cs.AI2026

What Papers Don't Tell You: Recovering Tacit Knowledge for Automated Paper Reproduction

Lehui Li, Ruining Wang, Haochen Song +8

Automated paper reproduction -- generating executable code from academic papers -- is bottlenecked not by information retrieval but by the tacit knowledge that papers inevitably le…

cs.AI2026

PhysProver: Advancing Automatic Theorem Proving for Physics

Hanning Zhang, Ruida Wang, Rui Pan +3

The combination of verifiable languages and LLMs has significantly influenced both the mathematical and computer science communities because it provides a rigorous foundation for t…

cs.AI2025

Lean4Physics: Comprehensive Reasoning Framework for College-level Physics in Lean4

Yuxin Li, Minghao Liu, Ruida Wang +6

We present **Lean4PHYS**, a comprehensive reasoning framework for college-level physics problems in Lean4. **Lean4PHYS** includes *LeanPhysBench*, a college-level benchmark for for…

cs.AI2025

ERA: Transforming VLMs into Embodied Agents via Embodied Prior Learning and Online Reinforcement Learning

Hanyang Chen, Mark Zhao, Rui Yang +15

Recent advances in embodied AI highlight the potential of vision language models (VLMs) as agents capable of perception, reasoning, and interaction in complex environments. However…

cs.AI2025

Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability

Ruida Wang, Yuxin Li, Yi R. Fung +1

Enhancing the mathematical reasoning capabilities of LLMs has garnered significant attention in both the mathematical and computer science communities. Recent works have made subst…