DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees
The paper introduces DeltaMem, a framework designed to enhance memory management in Large Language Model (LLM) agents. It organizes experiences into two residual trees to reduce redundancy and improve retrieval accuracy. Experimental results demonstrate that DeltaMem outperforms existing memory management methods in various interactive environments.
- ▪DeltaMem organizes experience memory into two independent residual trees.
- ▪One tree stores goal-conditioned task experience while the other focuses on scene-level environment knowledge.
- ▪The framework allows related experiences to share a common foundation without duplication.
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Computer Science > Artificial Intelligence arXiv:2606.03083 (cs) [Submitted on 2 Jun 2026] Title:DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees Authors:Haoran Tan, Zeyu Zhang, Zhicheng Cao, Rui Li, Xu Chen View a PDF of the paper titled DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees, by Haoran Tan and 4 other authors View PDF HTML (experimental) 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.
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