From the 1 of 13 linked papers with an AI index.
13 papers
Multi-Agent LLMs Fail to Explore Each Other
Hyeong Kyu Choi, Jiatong Li, Wendi Li +2
The paper shows that large language model agents struggle to explore each other in multi-agent settings, leading to poor coordination, and introduces the MACE framework that uses s…
Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
Changdae Oh, Wendi Li, Seongheon Park +3
Process reward models enable fine-grained, step-level evaluation of LLMs, yet building them for agentic settings remains prohibitively difficult: long-horizon interactions, irrever…
Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring
Seongheon Park, Wendi Li, Changdae Oh +4
Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnerable to execution failures that…
Cyclical Entropy Eruption: Entropy Dynamics in Agent Reinforcement Learning
Wendi Li, Shawn Im, Sharon Li
Agentic large language models are increasingly used to solve real-world tasks by reasoning over goals, invoking tools, and interacting with external environments. Reinforcement lea…
CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning
Congmin Zheng, Jiachen Zhu, Jianghao Lin +6
Process Reward Models (PRMs) play a central role in evaluating and guiding multi-step reasoning in large language models (LLMs), especially for mathematical problem solving. Howeve…
When Vision Speaks for Sound
Xiaofei Wen, Wenjie Jacky Mo, Xingyu Fu +6
Despite rapid progress in video-capable MLLMs, we find that their apparent audio understanding in videos is often vision-driven: models rely on visual cues to infer or hallucinate…