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English(EN) Sci-MMR: Benchmarking Multi-Step Evidence-Grounded Scientific Reasoning in Multimodal Agents

新基准和框架提升多模态AI推理和可靠性

作者 PulseAugur 编辑部 · [5 个来源] ·

研究人员开发了新的基准和框架，以提高多模态AI智能体的可靠性和基于证据的推理能力。Sci-MMR是科学推理的基准测试，突显了当前模型在答案准确性和证据恢复之间存在的显著差距，并指出了证据获取和整合中的瓶颈。同时，CUSP提供了一种无需训练的方法来量化多智能体多模态系统中的集体不确定性，通过分析模型响应的分散性和冲突来提高可靠性。V-Retrver引入了一个智能体推理框架，该框架利用外部工具主动获取视觉证据，以提高多模态检索的准确性和可靠性。 AI

影响 这些进展旨在通过关注证据基础和不确定性量化来提高多模态AI系统的可靠性和透明度。

排序理由 该集群包含多篇介绍多模态AI推理新基准和框架的研究论文。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 5 个来源。 我们如何撰写摘要 →

新基准和框架提升多模态AI推理和可靠性

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报道来源 [5]

  1. arXiv cs.CL TIER_1 English(EN) · Ji Lu, Lifei Liu, Haoran Yu, Xianglong Wang, Yiru Fang, Kuo Yang, Huiran Duan, Jianping Gou ·

    MedTRACE：用于基于证据的决策的工具增强多模态临床推理代理

    arXiv:2609.14823v1 Announce Type: new Abstract: Multimodal clinical decision-making requires reliable reasoning over heterogeneous evidence from electronic health records, medical images, and physiological signals. Existing models typically map these inputs directly to diagnoses …

  2. arXiv cs.AI TIER_1 English(EN) · Jiaqiang Li, Yajie Yang, Zhiheng Xi, Jiadong Chen, Enyu Zhou, Senjie Jin, Yang Nan, Jiazheng Zhang, Han Wang, Yanxin Li, Dingwei Zhu, Bicheng Deng, Yuhui Wang, Xiang Zheng, Qi Zhang, Lei Bai, Xingjun Ma, Tao Gui ·

    Sci-MMR：多模态智能体中基于多步证据的科学推理基准测试

    arXiv:2609.11243v1 Announce Type: new Abstract: Autonomous research agents are increasingly expected to search the literature, analyze experimental evidence, and generate scientific hypotheses. These capabilities require multi-step evidence grounded reasoning that progressively a…

  3. arXiv cs.AI TIER_1 English(EN) · Chung-En Johnny Yu, David Garcia, Brian Jalaian, Nathaniel D. Bastian ·

    CUSP：可分解的集体不确定性用于多智能体多模态推理

    arXiv:2609.05708v1 Announce Type: new Abstract: Aggregating heterogeneous vision-language models (VLMs) can improve multimodal reasoning, but neither an individual model's confidence nor that of the aggregated answer measures reliability at the system level. We present CUSP (Coll…

  4. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Nathaniel D. Bastian ·

    CUSP：可分解的集体不确定性用于多智能体多模态推理

    Aggregating heterogeneous vision-language models (VLMs) can improve multimodal reasoning, but neither an individual model's confidence nor that of the aggregated answer measures reliability at the system level. We present CUSP (Collective Uncertainty through Semantic Opinion Pool…

  5. arXiv cs.CV TIER_1 English(EN) · Dongyang Chen, Chaoyang Wang, Dezhao Su, Xi Xiao, Zeyu Zhang, Jing Xiong, Qing Li, Yuzhang Shang, Shichao Kan ·

    V-Retrver：通用多模态检索的证据驱动的代理推理

    arXiv:2602.06034v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have recently been applied to universal multimodal retrieval, where Chain-of-Thought (CoT) reasoning improves candidate reranking. However, existing approaches remain largely language-dri…

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