collaborators

17 papers

cs.CL2026

FormalRx: Rectify and eXamine Semantic Failures in Autoformalization

Haocheng Wang, Baiyu Huang, Yingjia Wan +4

The veracious semantic alignment in autoformalization is significant for formal mathematical reasoning. However, existing evaluations provide only opaque binary verdicts or scalar…

cs.LO2026

Lean Meets Theoretical Computer Science: Scalable Synthesis of Theorem Proving Challenges in Formal-Informal Pairs

Terry Jingchen Zhang, Wenyuan Jiang, Rongchuan Liu +6

Formal theorem proving (FTP) has emerged as a critical foundation for evaluating the reasoning capabilities of large language models, enabling automated verification of mathematica…

cs.AI2026

Test of Time: Rethinking Temporal Signal of Benchmark Contamination

Terry Jingchen Zhang, Gopal Dev, Ning Wang +8

Post-cutoff performance decay of LLMs has been widely interpreted as a temporal signal for benchmark contamination, where public information released before the training cutoff may…

cs.AI2026

SeePhys Pro: Diagnosing Modality Transfer and Blind-Training Effects in Multimodal RLVR for Physics Reasoning

Kun Xiang, Terry Jingchen Zhang, Zirong Liu +15

We introduce SeePhys Pro, a fine-grained modality transfer benchmark that studies whether models preserve the same reasoning capability when critical information is progressively t…

cs.AI2026

Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI

Kun Xiang, Terry Jingchen Zhang, Yinya Huang +13

The rapid advancement of embodied intelligence and world models has intensified efforts to integrate physical laws into AI systems, yet physical perception and symbolic physics rea…

cs.LG2026

Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration

Zhicheng Yang, Zhijiang Guo, Yinya Huang +6

Reinforcement Learning with Verifiable Reward (RLVR) is a powerful method for enhancing the reasoning abilities of Large Language Models, but its full potential is limited by a lac…