🩺 Medical NLP

🔬 ICLR2026 · 5 paper notes

📌 Same area in other venues: 💬 ACL2026 (8) · 🤖 AAAI2026 (2) · 🧠 NeurIPS2025 (6)

🔥 Top topics: Medical Imaging ×3 · Dialogue ×2 · Reasoning ×2

ATPO: Adaptive Tree Policy Optimization for Multi-Turn Medical Dialogue

This paper proposes ATPO (Adaptive Tree Policy Optimization), which models multi-turn medical dialogue as a hierarchical Markov decision process (H-MDP). ATPO dynamically allocates rollout budgets via an uncertainty-aware adaptive tree expansion mechanism, using a composite uncertainty measure combining Bellman error and action-value variance to guide exploration. With Qwen3-8B, ATPO surpasses GPT-4o on three medical dialogue benchmarks.

BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases

This paper introduces BiomedSQL, the first benchmark specifically designed to evaluate the scientific reasoning capabilities of Text-to-SQL systems on biomedical knowledge bases. It comprises 68,000 question/SQL/answer triples and reveals a substantial gap between the best-performing model (GPT-o3-mini, 62.6%) and domain experts (90%).

CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmarking of LLMs in Mental Health QA

CounselBench is a two-component benchmark constructed with 100 licensed mental health professionals — CounselBench-EVAL (2,000 expert annotations across six clinical dimensions) and CounselBench-Adv (120 adversarial questions with 1,080 annotated responses) — systematically revealing that LLMs achieve superficially high scores in mental health open-ended QA while exhibiting safety risks such as over-generalization and unsolicited medical advice, and demonstrating that LLM-as-Judge is severely unreliable in safety-critical domains.

From Conversation to Query Execution: Benchmarking User and Tool Interactions for EHR Database Agents

This paper proposes EHR-ChatQA, the first benchmark to evaluate the end-to-end interactive workflow of database agents in electronic health record (EHR) settings — covering ambiguity clarification, terminology mismatch resolution, SQL generation, and answer return. Evaluation reveals that the strongest model (o4-mini) achieves Pass@5 above 90% but suffers a substantial drop in Pass∧5 (all-success rate), with a gap of up to 60%, exposing critical robustness deficiencies in safety-sensitive clinical domains.

MedAgentGym: A Scalable Agentic Training Environment for Code-Centric Reasoning in Biomedical Data Science

This work introduces MedAgentGym, the first unified agentic training environment for biomedical data science, comprising 72,413 task instances spanning 12 real-world scenarios and 129 categories, equipped with an executable sandbox and verifiable ground truth. A systematic benchmark evaluation of 29 LLMs reveals a substantial gap between commercial and open-source models. By combining efficient multi-threaded trajectory sampling with offline/online RL, the authors train Med-Copilot, achieving gains of +43.02%/+45.28% respectively and attaining performance competitive with GPT-4o.