PACT: Learning Diverse Diagnostic Strategies via Privileged Synthesis and Branch Consensus
Clinical diagnosis requires flexible use of multiple reasoning paradigms under incomplete patient information. Existing LLM-based medical agents show strong medical reasoning ability, but single-paradigm or naively mixed dialogue supervision makes these paradigms difficult to learn without interference. We propose \textbf{PACT} (Periodic Anchor Consensus Training), a framework that couples supervised multi-paradigm dialogue synthesis with consensus-based Branch training. At the data level, \textbf{DPS} (Doctor-Patient-Supervisor) uses complete electronic medical records (EMRs) for quality control while keeping the doctor agent restricted to patient-visible information. This produces valid...
| Authors | Gen Li, Yuanze Hu, Zhichao Yang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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