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cs.AI2026
DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees
Haoran Tan, Zeyu Zhang, Zhicheng Cao +2
Large Language Model (LLM)-based agents increasingly rely on memory to learn from experiences over continual interactions. However, storing experiences as independent, flat units l…
cs.AI2026
From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents
Haoran Tan, Zeyu Zhang, Chen Ma +3
Large language model-based agents have recently emerged as powerful approaches for solving dynamic and multi-step tasks. Most existing agents employ planning mechanisms to guide lo…
cs.AI2026
NextMem: Towards Latent Factual Memory for LLM-based Agents
Zeyu Zhang, Rui Li, Xiaoyan Zhao +4
Memory is critical for LLM-based agents to preserve past observations for future decision-making, where factual memory serves as its foundational part. However, existing approaches…