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This paper introduces BEACON, a novel framework for whole-slide image reasoning that reformulates the evidence acquisition process as a Bayesian problem focused on maximizing expected information gain (EIG). Unlike traditional relevance-based methods, which often lead to redundant observations, BEACON effectively reduces diagnostic uncertainty by maintaining a probabilistic belief over competing hypotheses and strategically acquiring patches. Extensive zero-shot evaluations across five WSI-VQA benchmarks reveal that BEACON outperforms existing training-free frameworks, demonstrating a significant improvement in evidence acquisition efficiency and diagnostic accuracy.
Bayesian evidence acquisition can dramatically enhance diagnostic accuracy in whole-slide image reasoning by focusing on information gain rather than mere relevance.
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
