用贝叶斯信息增益优化病理切片推理，更高效地获取诊断关键证据。
Beyond Relevance: Bayesian Evidence Acquisition for Agentic Whole-Slide Image Reasoning
- 将切片推理转为贝叶斯证据获取，按信息增量选区域而非仅看相关性。
- 在五个基准上零样本表现最优，证据获取效率提升显著。
- 无需训练，适配临床病理诊断与不确定性强的智能分析场景。
全切片图像（WSI）推理要求智能体在回答诊断问题前逐步获取视觉证据。现有免训练框架将此过程建模为基于语义相关性的迭代补丁检索，但语义相关性未必带来诊断区分力——在计算病理学中，不同诊断常呈现相似重叠的形态特征，导致大量补丁虽相关却无鉴别意义。这会造成冗余观察且无法消除诊断不确定性。本文提出BEACON，一种可即插即用的免训练智能体框架，将WSI推理重新定义为贝叶斯证据获取问题：维护对多个诊断假设的概率信念，并通过最大化期望信息增益（EIG）来选择最能降低不确定性的补丁。一个证据控制器决定是否回答、继续采集或进行更高分辨率检查。整个系统仅由现成基础模型构建，无需额外训练。在五个WSI-VQA基准上的零样本实验表明，BEACON在免训练框架中表现最强，同时大幅提高证据获取效率，确立了贝叶斯证据获取作为不确定性感知智能体推理的原理性范式。代码已开源：https://github.com/bryanwong17/BEACON。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。