[COLM 2026] Thrilled to receive this surprise right before the end of my undergrad junior year: our paper MedAction: Towards Active Multi-turn Clinical Diagnostic LLMs has been accepted to COLM 2026! 🎉🎉
This is my first first-author paper at a top-tier conference main track (I had an ACL paper last year, but industry track, though ACL industry was around a 25% acceptance rate too, so still pretty competitive xD). I feel incredibly lucky to have produced this result together with the professors, physicians, and collaborators/seniors at the University of Michigan and Far Eastern Memorial Hospital. Heartfelt thanks to everyone on the team. With COLM's acceptance rate at 29% this year and submissions surging, it was even more competitive than last year, which makes getting in all the more exciting.
See you all in San Francisco this October! 🌉
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Paper overview:
Most medical LLMs are evaluated in a static, single-turn setting: give the model a complete record and have it predict the disease/ICD directly. But real, complex clinical settings aren't always like that. A physician starts from the chief complaint, then step by step orders tests, interprets results, updates the differential diagnosis, and commits to a final diagnosis once confident.
❓ When you turn diagnosis into a truly multi-turn, active process, how do current LLMs do? Even SOTA models run into three major problems: ungrounded test ordering, unreliable update, and degraded coherence. Existing data mostly teaches models to reason when information is complete, but not how to act when the evidence keeps changing.
❓ So how do we close this gap? We propose MedAction, which has LLMs interact with a simulated clinical environment to generate multi-turn diagnostic trajectories, then filters trajectory quality using two newly proposed KG metrics (DTC and RAC). The 8B model trained on it beats a 235B teacher model, reaches SOTA among open-source models, and earned recognition from clinical physicians.
Full paper: arxiv.org/abs/2605.07305
#COLM2026 #LLM #MedicalAI #ClinicalReasoning #AIforHealthcare #MachineLearning
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