arXiv:2606.08938v1 Announce Type: cross Abstract: 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
The rapid advancement in large language models requires sophisticated training methodologies to improve performance in complex domains like medicine, leading to innovations like PACT.
This development represents a significant step in enhancing the diagnostic capabilities of AI in healthcare, offering more flexible and accurate reasoning under incomplete information.
AI-based medical diagnostic systems can now be trained to use multiple reasoning paradigms more effectively, potentially leading to more reliable and nuanced clinical decisions.
- · AI healthcare developers
- · Patients
- · Medical diagnostic companies
- · LLM research labs
- · Traditional diagnostic software developers (if they fail to adapt)
Improved accuracy and reliability of AI-driven medical diagnostics.
Increased adoption of AI in clinical settings for complex case analysis and physician support.
Re-evaluation of medical training curricula to incorporate AI diagnostic tools and multi-paradigm reasoning.
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Read at arXiv cs.AI