4 papers
Memex(RL): Scaling Long-Horizon LLM Agents via Indexed Experience Memory
Zhenting Wang, Huancheng Chen, Jiayun Wang +1
Large language model (LLM) agents are fundamentally bottlenecked by finite context windows on long-horizon tasks. As trajectories grow, retaining tool outputs and intermediate reas…
Training-Free Agentic AI: Probabilistic Control and Coordination in Multi-Agent LLM Systems
Mohammad Parsa Hosseini, Ankit Shah, Saiyra Qureshi +3
Multi-agent large language model (LLM) systems enable complex, long-horizon reasoning by composing specialized agents, but practical deployment remains hindered by inefficient rout…
Action-Adaptive Continual Learning: Enabling Policy Generalization under Dynamic Action Spaces
Chaofan Pan, Jiafen Liu, Yanhua Li +4
Continual Learning (CL) is a powerful tool that enables agents to learn a sequence of tasks, accumulating knowledge learned in the past and using it for problem-solving or future t…
A Survey of Continual Reinforcement Learning
Chaofan Pan, Xin Yang, Yanhua Li +4
Reinforcement Learning (RL) is an important machine learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in this field…