6 papers
Metis: Memory Foundation Model
Zeyu Zhang, Ziliang Guo, Yihang Sun +14
The paper presents Metis, a memory foundation model that embeds a persistent, dynamically updated memory state within the model backbone, allowing it to store and retrieve informat…
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…
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…
Explore Like Humans: Autonomous Exploration with Online SG-Memo Construction for Embodied Agents
Xu Chen, Shichao Xie, Zhining Gu +7
Constructing structured spatial memory is essential for enabling long-horizon reasoning in complex embodied navigation tasks. Current memory construction predominantly relies on a…
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…
Explicit v.s. Implicit Memory: Exploring Multi-hop Complex Reasoning Over Personalized Information
Zeyu Zhang, Yang Zhang, Haoran Tan +2
In large language model-based agents, memory serves as a critical capability for achieving personalization by storing and utilizing users' information. Although some previous studi…