Deep Research in Physical Sciences: A Multi-Agent Framework and Comprehensive Benchmark
Authors: name: Yigeng Jiang, name: Tengchao Yang, name: Taoyong Cui, name: Jiaxing Wan, name: Yuan Wang, name: Weida Wang, name: Zhiyu Liu, name: Chuyi Peng, name: Binzhao Luo, name: Maoli Gao, name: Huaihai Huang, name: Yuqianer Zeng, name: Ziyang Zheng, name: Dongchen Huang, name: Chao Chen, name: Zichao Liu, name: Weiping Shen, name: Shuchen Pu, name: Siyu Zhou, name: Runmin Ma, name: Yusong Hu, name: Fei Chao, name: Bo Zhang, name: Xiawu Zheng, name: Zifu Wang, name: Lei Bai, name: Yunqi Cai, name: Shufei Zhang
Organization: shanghai ailab 

Deep research agents are Large Language Model (LLM)-based systems designed for autonomous, multi-step scientific reasoning, and they hold immense potential for accelerating research in the physical sciences. However, comprehensive and in-depth evaluations of their capabilities within this domain remain lacking. To address this gap, we introduce PhySciBench, a benchmark highly relevant to physical science research, comprising 200 expert-curated questions, balanced between physics and chemistry, across six task categories that reflect real-world scientific workflows. Evaluations of state-of-the-art models and agent systems on PhySciBench reveal limited performance; even the strongest baseline, Gemini Deep Research, achieves an accuracy of only 33.5%. Analysis of failure cases identifies three recurrent deficiencies: fragility in extended reasoning chains, limited knowledge transfer across steps, and a lack of physics-grounded self-verification. Motivated by these findings, we develop DelveAgent, a modular multi-agent framework equipped with an adaptive planning loop, dual-granularity memory, and a hierarchical physics-grounded reflection mechanism. Across four scientific benchmarks, DelveAgent improves accuracy by up to 7.5 percentage points while reducing inference costs to approximately one-third of the strongest baseline. These results establish the significance of PhySciBench as a critical benchmark for evaluating AI systems in the physical sciences and demonstrate that architectural specialization can effectively enhance the reliability of autonomous scientific research.