arXiv cs.MA· Aakash Madhav Rao, Mark Eastwood, Danielle Belgrave, Fayyaz Minhas·· 12 小时前AI 评分31
TIAgent：将自然语言转化为计算病理学可执行智能体工作流
TIAgent: From Natural Language to Grounded Agentic Workflows for Computational Pathology
AI 导读
TIAgent 是面向计算病理学的自动工作流生成框架，可根据自然语言任务描述和输入数据集，自动构建可检查、可执行的计算图。研究系统评测了 14 个骨干 LLM 在 18 类 CPath 操作、90 个单元任务上的表现，Kimi-K2.7-Code 以 0.967 的 Unit Workflow Success（UWS）居首。分析显示，选对计算操作与正确配置参数是 LLM 生成工作流的两类不同挑战。
正文
Abstract:Computational pathology (CPath) analyses increasingly depend on multi-step computational workflows that require substantial programming expertise, limiting their accessibility to pathologists and other domain experts without extensive coding experience. Although large language models (LLMs) provide a promising natural-language interface for constructing such analyses, hallucinated operations and incorrect workflow specifications limit their reliability in scientific settings. We introduce Tissue Image Analysis Agent (TIAgent), an automated CPath workflow generation framework that, given a natural-language prompt specifying a complex analytical task together with an input dataset, automatically constructs a verifiable computational graph employing existing CPath operations. The resulting graph can be inspected and executed, enabling natural-language construction of complex CPath workflows for applications including automated biomarker discovery and tissue analysis. We demonstrate end-to-end workflow generation and execution and systematically benchmark 14 backbone LLMs across 90 unit tasks spanning 18 CPath operation types. The ability of each LLM to generate a complete and correct unit workflow is quantified using Unit Workflow Success (UWS). Performance varied substantially across backbone models, with Kimi-K2.7-Code achieving the highest UWS of 0.967 (95% CI: 0.907-0.989). Analysis of node and parameter correctness further revealed that selecting the appropriate computational operation and correctly configuring it represent distinct challenges for LLM-based workflow generation. TIAgent provides a foundation for making complex CPath workflow construction more accessible while enabling systematic evaluation of LLM-driven scientific workflow generation.
| Subjects: | Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.32993 [q-bio.QM] |
| (or arXiv:2610.32993v1 [q-bio.QM] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32993 arXiv-issued DOI via DataCite |
Submission history
From: Aakash Rao [view email]
[v1]
Wed, 30 Sep 2026 13:30:12 UTC (4,080 KB)
来源：arXiv cs.MA · arxiv.org