15 papers
See, Infer, Intervene: Proactive World Modeling for Goal-Oriented Social Intelligence
Honghui Zhang, Chenmeinian Guo, Yichen Yu +7
Multimodal retail agents should not only recognize what a customer is doing, but also decide whether and how to assist before an explicit request is made. We study this setting thr…
Skill-Pro: Learning Reusable Skills from Experience via Non-Parametric PPO for LLM Agents
Qirui Mi, Zhijian Ma, Mengyue Yang +4
LLM-driven agents excel at sequential decision-making but often rely on on-the-fly reasoning, re-deriving solutions even in recurring scenarios. This insufficient experience reuse…
Learning Stateful Predictive Knowledge From Experience
Yan Song, Xidong Feng, Bo Liu +7
As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights. Viewed through the lens of predicti…
Probing the "Psyche'' of Large Reasoning Models: Understanding Through a Human Lens
Yuxiang Chen, Zuohan Wu, Ziwei Wang +6
Large reasoning models (LRMs) have garnered significant attention from researchers owing to their exceptional capability in addressing complex tasks. Motivated by the observed huma…
A Principle of Targeted Intervention for Multi-Agent Reinforcement Learning
Anjie Liu, Jianhong Wang, Samuel Kaski +2
Steering cooperative multi-agent reinforcement learning (MARL) towards desired outcomes is challenging, particularly when the global guidance from a human on the whole multi-agent…
Curious Causality-Seeking Agents Learn Meta Causal World
Zhiyu Zhao, Haoxuan Li, Haifeng Zhang +4
When building a world model, a common assumption is that the environment has a single, unchanging underlying causal rule, like applying Newton's laws to every situation. In reality…