Memory-Augmented LLM Agents Face Continual Learning Bottleneck at Retrieval
A new study on arXiv (2604.27003) reveals that memory-augmented large language model (LLM) agents do not escape the stability-plasticity dilemma but relocate it to memory access. Researchers introduce a (k,v) framework to disentangle experience representation and organization in external memory. Experiments in ALFWorld and BabyAI show abstract procedural memories transfer more reliably than detailed trajectories, and negative transfer disproportionately harms hard cases. Finer-grained memory organization is also examined.
Key facts
- arXiv paper 2604.27003 studies continual learning in memory-augmented LLM agents.
- The stability-plasticity dilemma resurfaces at the memory level under limited context windows.
- A (k,v) framework disentangles experience representation and organization for retrieval.
- Experiments conducted in ALFWorld and BabyAI environments.
- Abstract procedural memories transfer more reliably than detailed trajectories.
- Negative transfer disproportionately harms hard cases.
- Finer-grained memory organization is explored.
- The paper is categorized as cross-type on arXiv.
Entities
Institutions
- arXiv