🩺 Medical LLM

🧪 ICML2025 · 4 paper notes

📌 Same area in other venues: 📷 CVPR2026 (1) · 🔬 ICLR2026 (20) · 💬 ACL2026 (47) · 🧪 ICML2026 (4) · 🤖 AAAI2026 (12) · 🧠 NeurIPS2025 (17)

Agent WARPP: Workflow Adherence via Runtime Parallel Personalization

Proposes WARPP, a training-free multi-agent framework that dynamically prunes conditional branch workflows at runtime based on user attributes, executing them through a parallelized Personalizer agent in coordination with modular domain-specific agents, thereby improving tool call precision and parameter fidelity while reducing token consumption.

Autoformulation of Mathematical Optimization Models Using LLMs

This paper proposes a method that combines Large Language Models (LLMs) with Monte-Carlo Tree Search (MCTS) to automatically convert optimization problems described in natural language into mathematical programming models solvable by solvers, significantly improving search efficiency through symbolic pruning and LLM-based value estimation.

EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration

This paper proposes the BanditBench benchmark and three enhancement strategies (inference-time algorithm guidance, few-shot demonstration, and oracle fine-tuning) to systematically evaluate and improve the in-context exploration capabilities of LLMs in bandit environments, enabling smaller models to outperform larger ones through algorithm distillation.

On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains

This paper systematically reveals the vulnerability of RAG retrieval systems in knowledge-intensive domains (healthcare, law) to universal poisoning attacks. It proposes the "orthogonal augmentation" property to explain the cause of the attack and designs a detection-based defense method using distribution-aware distance, achieving near-perfect detection rates in almost all scenarios.