DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees 事件

DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees arXiv:2606.03083v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents increasingly rely on memory to learn from experiences over continual interactions. However, storing experiences as independent, flat units leads to substantial redundancy and retrieval conflicts, as similar episodes repeat overlapping content and subtle scene variations cause retrieved memories to offer contradictory guidance. To addre