CDEG: Learning Decision-Critical Evidence for Long-Horizon Diagnostic Agents
Quick summary
arXiv:2608.22899v1 Announce Type: new Abstract: Unlike static medical question answering, long-horizon diagnosis captures the sequential nature of clinical practice: evidence is progressively acquired, integrated, and evaluated over multiple rounds of interaction before reaching a final diagnosis. However, existing doctor agents often fail when critical evidence is either not acquired or not adequately incorporated into diagnostic reasoning. Recent agentic approaches attempt to address these failures by reusing historical trajectories or distilled memories. But their diagnostic gains remain co
Key takeaways
- arXiv:2608.22899v1 Announce Type: new Abstract: Unlike static medical question answering, long-horizon diagnosis captures the sequential nature of clinical practice: evidence is progressively acquired, integrated, and evaluated over multiple rounds of interaction before reaching a final diagnosis.
- However, existing doctor agents often fail when critical evidence is either not acquired or not adequately incorporated into diagnostic reasoning.
- Recent agentic approaches attempt to address these failures by reusing historical trajectories or distilled memories.
Why it matters
“CDEG: Learning Decision-Critical Evidence for Long-Horizon Diagnostic Agents” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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