metadata
license: mit
language:
- en
base_model:
- Qwen/Qwen3-VL-8B-Instruct
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-eagerwhen 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.