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cs.AI2026

Right Answer, Wrong Method: Shortcut Hacking Misleads the Evaluation of LLM Reasoning on Frontier Science Benchmarks

Xuan Ren, Weiqi Zhai, Tianle Pu +3

Scientific reasoning benchmarks typically evaluate large language models (LLMs) using final-answer accuracy. However, a correct answer does not necessarily demonstrate the reasonin…

cs.AI2026

FeynmanBench: Benchmarking Multimodal LLMs on Diagrammatic Physics Reasoning

Zeyu Wang, Jingye Xu, Xiaogang Li +7

Current multimodal benchmarks for scientific reasoning primarily evaluate local information extraction -- models recognize symbols and values and then perform textual inference. Th…

cs.AI2026

BilliardPhys-Bench: Benchmarking Physical Reasoning and Visual Dynamics of Multimodal LLMs

Ben Wang, Xiaogang Li, Ruochen Gao +6

Current multimodal models handle static image recognition well, but intuitive physical reasoning remains a weakness. Predicting how objects will move and interact from a single ima…

cs.AI2026

Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem

Weixun Wang, XiaoXiao Xu, Wanhe An +86

Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its impo…

cs.AI2026

Benchmark Health Index: A Systematic Framework for Benchmarking the Benchmarks of LLMs

Longyuan Zhu, Hairan Hua, Linlin Miao +1

Large Language Models (LLMs) are advancing rapidly, yet the benchmarks used to measure this progress are becoming increasingly unreliable. Score inflation and selective reporting h…

cs.AI2026

Credit Where It is Due: Cross-Modality Connectivity Drives Precise Reinforcement Learning for MLLM Reasoning

Zhengbo Jiao, Shaobo Wang, Zifan Zhang +4

Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet how visual evidence is…