AI RESEARCH
HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents
arXiv CS.CL
•
ArXi:2604.16839v1 Announce Type: new Long-term memory is a critical challenge for Large Language Model agents, as fixed context windows cannot preserve coherence across extended interactions. Existing memory systems represent conversation history as unstructured embedding vectors, retrieving information through semantic similarity. This paradigm fails to capture the associative structure of human memory, wherein related experiences progressively strengthen interconnections through repeated co-activation.