2 papers
cs.LG2026
TAP: Efficient Long-Horizon Agent Pruning via Trajectory-Anchored Recovery
Yuanzhe Li, Pengxin Wang, Yuxin Ren +5
Emerging long-horizon agentic tasks require repeated model calls, worsening the inference cost of already-costly language models. While narrow agentic tasks suggest potential for a…
cs.MA2026
Learning Coordinated Preference for Multi-Objective Multi-Agent Reinforcement Learning
Pengxin Wang, Lihao Guo, Yi Xie +3
Cooperative multi-objective multi-agent reinforcement learning (MOMARL) models team decision making under multiple, potentially conflicting objectives. In this setting, conflicts a…