HyMem: Hybrid Memory Architecture for LLM Agents
Researchers propose HyMem, a hybrid memory architecture for large language model (LLM) agents that addresses inefficiencies in extended dialogues. Existing methods face a trade-off between efficiency and effectiveness: compression loses critical details, while raw text retention adds overhead. HyMem enables dynamic on-demand scheduling through multi-granular memory representations, inspired by cognitive economy. It adopts a dual-granular storage scheme with a dynamic two-tier retrieval mechanism. The paper is available on arXiv under identifier 2602.13933.
Key facts
- HyMem is a hybrid memory architecture for LLM agents.
- It addresses inefficiencies in extended dialogues.
- Existing approaches trade off efficiency and effectiveness.
- Memory compression risks losing critical details.
- Retaining raw text introduces computational overhead.
- HyMem uses multi-granular memory representations.
- It is inspired by the principle of cognitive economy.
- The paper is on arXiv: 2602.13933.
Entities
Institutions
- arXiv