case-8b-rl (step 1912, 1 epoch)

Clinical Agents for Seeking Evidence - agentic harness learning for longitudinal medical imaging. The model autonomously plans and calls tools inside a multi-turn harness (model -> qwen3_xml tool call -> environment executes -> observation -> ... -> final answer); it never answers single-shot. Three role personas: Anatomist (organ segmentation / volumetry), Radiologist (cross-visit image comparison), Consultant (report-grounded synthesis). Paper: From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoning.

• Training: agentic RL - GRPO over multi-turn tool rollouts against a mock tool-replay environment, + OPSD (bucket-IV causal-KL) + rubric-judge reward
• Step: 1912 (final)
• Base model: Qwen3-VL-8B-Instruct
• Format: merged HF checkpoint (single model.safetensors + tokenizer/processor)
• Serving tip: run with a tool backend (the released mock-tool replay configs, or the full LangGraph agents) and start vLLM with --enable-auto-tool-choice --tool-call-parser qwen3_xml (add --enforce-eager when the environment has no full CUDA toolkit)
• Data: trained on UK Biobank-derived, access-controlled data; no UKB-derived data is redistributed here. Research use only.