H-Mem: Hybrid Memory Mechanism for LLM Agents
Researchers propose H-Mem, a novel memory mechanism for LLM-based agents like OpenClaw and Manus. It uses a hybrid structure combining a temporal and semantic tree with a knowledge graph to model memory evolution and improve retrieval. The tree allows short-term memory to evolve into long-term memory, while the knowledge graph captures relationships. This addresses poor performance in memory utilization for question-answering tasks. The work is published on arXiv under ID 2605.15701.
Key facts
- H-Mem is a memory mechanism for LLM-based agents.
- It uses a hybrid structure with a temporal and semantic tree and a knowledge graph.
- The tree enables short-term memory to evolve into long-term memory.
- The knowledge graph captures relationships between memory data.
- It aims to improve memory retrieval and utilization for QA tasks.
- The paper is on arXiv with ID 2605.15701.
- OpenClaw and Manus are examples of LLM-based agents.
- Previous works lacked principled memory modeling and retrieval.
Entities
Institutions
- arXiv