arXiv:2505. 23399v2 Announce Type: replace Abstract: We propose GAM-Agent, a game-theoretic multi-agent framework for enhancing vision-language reasoning.
By Jusheng Zhang, Yijia Fan, Wenjun Lin, Ruiqi Chen, Haoyi Jiang, Wenhao Chai, Jian Wang, Keze Wang
MedResearcher‑R1 is a medical deep‑research agent that uses a Knowledge‑Informed Trajectory Synthesis (KISA) framework to build medical knowledge graphs and generate complex multi‑hop question‑answer pairs, especially for rare medical entities. It also incorporates a medical retrieval engine and general‑purpose tools to reliably synthesize medical evidence. The resulting 2,100+ trajectories across 12 specialties enable supervised fine‑tuning and reinforcement learning, producing a 32B model that outperforms larger proprietary systems on MedBrowseComp and shows strong performance on GAIA and xBench benchmarks.
By Ailing Yu, HaoYang Hong, Lan Yao, Jingnan Liu, Zhe Chen, Jiajun Yin, Yuan Wang, Xinhao Liao, Zhiling Ye, Ji Li, Yun Yue, Hansong Xiao, Hualei Zhou, Chunxiao Guo, Peng Wei, Junwei Liu, Jinjie Gu
The paper introduces a challenge‑aware adaptive recruitment system for medical multi‑agent reasoning. An external CHALLENGER monitors the evolving reasoning trace to detect factual or expertise gaps, triggering targeted knowledge retrieval or specialist recruitment mid‑discussion. Experiments on the MEDAGENTSBENCH hard subset show that challenge‑aware in‑between retrieval and Challenger‑triggered specialist recruitment improve accuracy, while naive combinations degrade performance, underscoring the importance of timing and coordination in resource recruitment.
By Riccardo Riccio, Haitz S\'aez de Oc\'ariz Borde, Pietro Li\`o
arXiv:2606. 15419v1 Announce Type: cross Abstract: Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA).
By Zaifu Zhan, Shuang Zhou, Rui Zhang
Lingshu is a medical‑specialized multimodal large language model that addresses key limitations of existing medical MLLMs, such as narrow knowledge coverage, hallucinations, and weak reasoning. The authors curate a comprehensive dataset combining medical imaging, texts, and general‑domain data, then train Lingshu in multiple stages to embed medical expertise and improve task performance. They also introduce MedEvalKit, a unified evaluation framework, and demonstrate that Lingshu outperforms current open‑source multimodal models on multimodal QA, text‑based QA, and medical report generation.
By Weiwen Xu, Hou Pong Chan, Long Li, Mahani Aljunied, Ruifeng Yuan, Jianyu Wang, Chenghao Xiao, Guizhen Chen, Chaoqun Liu, Zhaodonghui Li, Yu Sun, Junao Shen, Chaojun Wang, Jie Tan, Deli Zhao, Tingyang Xu, Hao Zhang, Yu Rong
The paper introduces a multi-agent peer‑reviewed reasoning approach for medical question answering, where several large language models independently generate chain‑of‑thought explanations and then evaluate each other’s reasoning for factual correctness and logical soundness. The highest‑rated reasoning chain is used to produce the final answer. Experiments with five state‑of‑the‑art LLMs on three benchmark datasets show that this method consistently outperforms single‑model chain‑of‑thought reasoning and majority voting ensembles, achieving an average accuracy of 0.820 versus 0.777 for the best single model.
By Zaifu Zhan, Shuang Zhou, Rui Zhang