EVA-Reason: Learning Adaptive Evidence Acquisition for Neuroimaging Reasoning

Strong multimodal models often struggle to answer difficult neuroimaging questions directly from MRI. We show that these failures are not simply explained by a lack of predictive signal. On the same controlled task, an end-to-end CNN reaches 0.962 balanced accuracy and a Qwen2.5-VL post-projector linear probe reaches 0.897, while direct generation is at chance (0.333). Language-side fine-tuning with frozen vision encoder/projector raises direct generation accuracy to 0.743, narrowing but not closing the representation-to-generation gap. We examine whether explicit quantitative evidence can improve this reasoning bottleneck. We evaluate nine frontier and open-weight multimodal models on the same questions with and without access to quantitative measurement tools. On the hardest questions (L3, the most challenging reasoning level), Tool-OFF accuracy ranges from 36.4% to 56.4%. Four frontier models improve significantly with tools, reaching 75.6% to 80.0% L3 accuracy. Other models improve little or degrade, showing that tool access alone is insufficient. Motivated by these findings, we introduce EVA-Reason, a framework for learning adaptive evidence acquisition in neuroimaging. We construct multi-step evidence-seeking trajectories using Claude Sonnet 4.6 and Gemini 2.5 Pro, and use them to fine-tune a 3B VLM; frontier comparators use the same interface zero-shot. We evaluate EVA-Reason on NeuroQA questions organized into three reasoning levels. On the held-out test set, under normalized exact-match scoring, EVA-Reason achieves 96.4% overall accuracy and 89.3% L3 accuracy. Matched Qwen-3B controls reach 64.5/82.2% overall/L3 without quantitative evidence and 94.6/88.9% when a prespecified relevant-evidence set is supplied upfront; EVA-Reason selects evidence autonomously at inference, without a prespecified per-item evidence set. Under zero-shot exact match, EVA-Reason reaches 89.3% L3 versus 80.0/79.6/79.1% for Sonnet 5/Gemini 2.5 Pro/GPT-5.5; several gaps narrow to non-significance under few-shot prompting or option-prefix scoring. EVA-Reason requires no frontier-model calls at inference and runs locally on a single GPU. These results show that a small open-weight model can learn to actively acquire and use quantitative neuroimaging evidence across multiple decision steps.