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SP-Mind: An Autonomous Reasoning Agent for Spatial Proteomics Analysis

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arXiv:2606.24235v1 Announce Type: new Abstract: Spatial proteomics enables single-cell-resolution characterization of protein expression within tissue architecture, playing a critical role in understanding tumor microenvironments and guiding precision medicine. However, current analysis workflows remain fragmented, requiring expert manual orchestration of heterogeneous tools and limiting research scalability and reproducibility. We present SP-Mind, the first autonomous AI agent designed to unify

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    Computer Science > Artificial Intelligence [Submitted on 23 Jun 2026] SP-Mind: An Autonomous Reasoning Agent for Spatial Proteomics Analysis Yucheng Yuan, Yuanfeng Ji, Zhongxiao Li, Ruijiang Li Spatial proteomics enables single-cell-resolution characterization of protein expression within tissue architecture, playing a critical role in understanding tumor microenvironments and guiding precision medicine. However, current analysis workflows remain fragmented, requiring expert manual orchestration of heterogeneous tools and limiting research scalability and reproducibility. We present SP-Mind, the first autonomous AI agent designed to unify the spatial proteomics analysis pipeline, from raw multiplexed tissue imaging to downstream phenotype discovery. Equipped with expert-curated biological analysis skills and specialized computational tools, SP-Mind converts natural-language queries into end-to-end analytical workflows without task-specific fine-tuning. To rigorously evaluate its capabilities, we introduce SP-Bench, a comprehensive benchmark spanning diverse tissue types, comprising 102 tasks across 18 distinct categories. Through extensive evaluation on SP-Bench and established downstream tasks, SP-Mind achieves state-of-the-art performance compared to existing open-source biomedical agent baselines. Comments: 23 pages, 6 figures. Accepted to ICML 2026. Equal contribution by Yucheng Yuan and Yuanfeng Ji Subjects: Artificial Intelligence (cs.AI) ACM classes: I.2.6; J.3 Cite as: arXiv:2606.24235 [cs.AI]   (or arXiv:2606.24235v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.24235 Focus to learn more Submission history From: YuCheng Yuan [view email] [v1] Tue, 23 Jun 2026 07:24:23 UTC (9,244 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Jun 24, 2026
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    Jun 24, 2026
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