Meta-Cognitive Memory Policy Optimization for Long-Horizon LLM Agents

ArXi:2605.30159v1 Announce Type: new Memory-augmented LLM agents tackle complex long-horizon tasks by recursively summarizing interaction trajectories into compact memory. However, existing approaches typically train these memory policies using outcome-based reinforcement learning, failing to localize where intermediate memory quality degrades. As interactions unfold, ambiguous recursive summaries progressively discard task-relevant information and