## CASE: From Given to Gathered Evidence

**Clinical reasoning is not merely about interpreting evidence, but also about knowing what evidence to seek.** Existing medical AI benchmarks primarily evaluate reasoning over preselected information, overlooking the ability to autonomously gather, compare, and synthesize evidence across clinical records and longitudinal imaging.

We introduce **CASE (Clinical Agents for Seeking Evidence)**, shifting medical AI from passive question answering to **active, evidence-grounded clinical investigation**.

### Key Contributions

- **Longitudinal Multimodal Benchmark.** A UK Biobank-based benchmark comprising **50,401 clinical questions** from **4,739 participants**, integrating clinical records and multi-sequence MRI across baseline and follow-up visits.

- **Autonomous Evidence-Seeking Agents.** A role-conditioned, multi-tool framework enabling compact vision-language models to autonomously select **visits, modalities, anatomical structures, image slices, and specialist tools** for evidence collection and cross-visit reasoning.

- **Beyond Teacher Imitation.** An agentic post-training framework integrating **Agentic RL, On-Policy Self-Distillation (OPSD), and RLAIF** to improve evidence-to-conclusion reasoning while preserving autonomous tool-use decisions.