Can the Evidence Reach the Answer? Diagnostic Reachability over Uncertain Evidence Graphs for Agentic Whole-Slide Reasoning.

Whole-slide image visual question answering (WSI-VQA) requires agentic systems to collect diagnostically relevant evidence that is often sparse and spatially dispersed across gigapixel slides. Existing agentic WSI-VQA systems typically decompose reasoning into three stages: evidence acquisition, acquisition sufficiency assessment, and answer generation, yet represent the acquired evidence as unstructured patch level information. Because they do not explicitly preserve the diagnostic evidence pathways that connect morphological observations to an answer through diagnostic concepts, they exhibit two limitations: unreliable pathway agnostic evidence evaluation within stages and diagnostic evidence loss across stages. To address these limitations, we propose ARGUE (Active Reasoning over a Graph of Uncertain Evidence), a training-free agentic WSI-VQA system. Within stages, ARGUE uses diagnostic reachability to enable pathway-aware evaluation by quantifying the probability of realizing evidence pathways under uncertain diagnostic relations. Across stages, ARGUE maintains a shared Uncertain Evidence Graph (UEG) to preserve these pathways, thereby preventing the loss of structured evidence. Experiments on three benchmarks show that ARGUE achieves the best overall performance among all baselines. Ablation studies further validate the effectiveness of ARGUE's core components, while robustness analyses show consistent performance under external noise and different implementation choices.