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Cheng-Xin Hu

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#artificial intelligence Preprint Oct 2026

From Research Goals to Scientific Closure: Evaluating Agents Through Evidence-Supported Physics Research

Can AI agents organize scientific investigations that lead to well-supported conclusions? To evaluate this capability, we introduce PhysClosureBench, a benchmark of 50 expert-verified, goal-specified research tasks spanning strong-field, attosecond, and neighboring physics areas. Each task specifies the research object...

Yi-Jia Mao, Song-Bin Dai, Tao Chen et al. · 0 citations
#artificial intelligence Preprint Oct 2026

EvoProbe: Evaluation-Driven Benchmark Evolution for LLM Agents

Static benchmarks for large language model (LLM) agents face saturation and data contamination risks, motivating continual updates to evaluation tasks. However, aggregate scores and success rates offer limited insight into specific problem-solving bottlenecks and provide limited guidance for subsequent task design. We...

Qian-Wen Guo, Wei Xie, Xiao-Fang Cai et al. · 0 citations

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