MuSEAgent: A Multimodal Reasoning Agent with Stateful Experiences
Research agents have recently achieved significant progress in information seeking and synthesis across heterogeneous textual and visual sources. In this paper, we introduce MuSEAgent, a multimodal reasoning agent that enhances decision-making by extending the capabilities of research agents to discover and leverage stateful experiences. Rather than relying on trajectory-level retrieval, we propose a stateful experience learning paradigm that abstracts interaction data into atomic decision experiences through hindsight reasoning. These experiences are organized into a quality-filtered experience bank that supports policy-driven experience retrieval at inference time. Specifically, MuSEAgent enables adaptive experience exploitation through complementary wide- and deep-search strategies, allowing the agent to dynamically retrieve multimodal guidance across diverse compositional semantic viewpoints. Extensive experiments demonstrate that MuSEAgent consistently outperforms strong trajectory-level experience retrieval baselines on both fine-grained visual perception and complex multimodal reasoning tasks. These results validate the effectiveness of stateful experience modeling in improving multimodal agent reasoning.
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Shijian Wang
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2026-03-31T09:24:39.267Z
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Shijian Wang
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Jiarui Jin
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2026-03-31T09:24:44.206Z
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Jiarui Jin
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user
jinjiarui97
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Runhao Fu
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Zexuan Yan
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Xingjian Wang
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Mengkang Hu
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2026-03-31T09:40:58.155Z
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Mengkang Hu
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MengkangHu
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Eric Wang
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Xiaoxi Li
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Kangning Zhang
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Li Yao
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Wenxiang Jiao
admin_assigned
2026-03-31T09:41:12.620Z
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Wenxiang Jiao
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wxjiao
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Xuelian Cheng
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Yuan Lu
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Zongyuan Ge
MuSEAgent: A Multimodal Reasoning Agent with Stateful Experiences
Research agents have recently achieved significant progress in information seeking and synthesis across heterogeneous textual and visual sources. In this paper, we introduce MuSEAgent, a multimodal reasoning agent that enhances decision-making by extending the capabilities of research agents to discover and leverage stateful experiences. Rather than relying on trajectory-level retrieval, we propose a stateful experience learning paradigm that abstracts interaction data into atomic decision experiences through hindsight reasoning. These experiences are organized into a quality-filtered experience bank that supports policy-driven experience retrieval at inference time. Specifically, MuSEAgent enables adaptive experience exploitation through complementary wide- and deep-search strategies, allowing the agent to dynamically retrieve multimodal guidance across diverse compositional semantic viewpoints. Extensive experiments demonstrate that MuSEAgent consistently outperforms strong trajectory-level experience retrieval baselines on both fine-grained visual perception and complex multimodal reasoning tasks. These results validate the effectiveness of stateful experience modeling in improving multimodal agent reasoning.
MuSEAgent enhances multimodal reasoning through stateful experience learning that abstracts interactions into decision experiences for improved policy-driven retrieval and adaptive search strategies.
multimodal reasoning agent stateful experience learning hindsight reasoning experience bank policy-driven experience retrieval wide-search strategy deep-search strategy multimodal guidance compositional semantic viewpoints