Post by Hsin-Ling Hsu

[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 Chenwei Wu Donghua Zhang Zizheng Wang Jun-En Ding 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! 🌉 --- 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: https://lnkd.in/gckampfS #COLM2026 #LLM #MedicalAI #ClinicalReasoning #AIforHealthcare #MachineLearning