发表机构
A*STAR（新加坡科技研究局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出BEACON框架，将WSI推理建模为贝叶斯证据获取问题，通过最大化预期信息增益减少诊断不确定性，在五个WSI-VQA基准的零样本实验中，其性能优于同类无训练智能体框架，提升了证据获取效率。
AI 中文摘要
全切片图像（WSI）推理要求智能体依次获取视觉证据后再回答诊断问题。现有的无训练智能体框架将该过程建模为基于与问题语义相关性的迭代斑块检索。然而在计算病理学中，语义相关性不一定意味着诊断信息量，因为不同的竞争诊断常表现出相似且重叠的形态模式，导致许多斑块虽语义相关却不具备诊断区分性。因此，基于相关性的检索可能获取冗余观测结果，且无法解决诊断不确定性。我们提出BEACON，这是一个即插即用的智能体框架，它将WSI推理重新建模为贝叶斯证据获取问题。BEACON维护关于竞争诊断假设的概率信念，并通过最大化预期信息增益（EIG）依次获取斑块以减少诊断不确定性。随后，证据控制器决定是给出答案、获取更多证据还是执行更高分辨率的检查。BEACON完全由现成的基础模型构建，无需额外训练或微调。在五个WSI-VQA基准上进行的大量零样本实验表明，BEACON在无训练智能体框架中实现了最强的整体性能，同时大幅提高了证据获取效率，确立了贝叶斯证据获取作为面向不确定性感知的智能体WSI推理的原则性范式。代码可在该https URL获取。
英文摘要
Whole-slide image (WSI) reasoning requires an agent to sequentially acquire visual evidence before answering a diagnostic question. Existing training-free agentic frameworks formulate this process as iterative patch retrieval based on semantic relevance to the question. However, semantic relevance does not necessarily imply diagnostic informativeness in computational pathology, where competing diagnoses often exhibit similar and overlapping morphological patterns, making many patches semantically relevant yet diagnostically non-discriminative. Consequently, relevance-based retrieval may acquire redundant observations and leave diagnostic uncertainty unresolved. We propose BEACON, a plug-and-play agentic framework that reformulates WSI reasoning as a Bayesian evidence acquisition problem. BEACON maintains a probabilistic belief over competing diagnostic hypotheses and sequentially acquires patches by maximizing expected information gain (EIG) to reduce diagnostic uncertainty. An evidence controller then determines whether to answer, acquire additional evidence, or perform higher-resolution inspection. Built entirely from off-the-shelf foundation models, BEACON requires no additional training or fine-tuning. Extensive zero-shot experiments across five WSI-VQA benchmarks demonstrate that BEACON achieves the strongest overall performance among training-free agentic frameworks while substantially improving evidence acquisition efficiency, establishing Bayesian evidence acquisition as a principled paradigm for uncertainty-aware agentic WSI reasoning. The code is available at https://github.com/bryanwong17/BEACON
