HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents

2026-04-18

AI Summary

This work addresses the limited human-like long-term memory capabilities of large language model (LLM) agents, noting that existing semantic similarity–based memory mechanisms fail to capture the associative structure inherent in human memory. To bridge this gap, the authors propose a cognitive neuroscience–inspired dynamic graph memory architecture that constructs episodic memory graphs via Hebbian learning and integrates a reflective agent to distill frequently activated nodes into structured semantic knowledge, thereby enabling the co-evolution of episodic and semantic memory. This approach uniquely incorporates Hebbian learning, memory consolidation, and spreading activation into LLM memory systems, supporting associative retrieval and knowledge distillation. Experimental results demonstrate superior performance across all four question categories of the LoCoMo benchmark while significantly reducing context token consumption.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Multiagent Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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. Inspired by cognitive neuroscience, we identify three mechanisms central to biological memory: association, consolidation, and spreading activation, which remain largely absent in current research. To bridge this gap, we propose HeLa-Mem, a bio-inspired memory architecture that models memory as a dynamic graph with Hebbian learning dynamics. HeLa-Mem employs a dual-level organization: (1) an episodic memory graph that evolves through co-activation patterns, and (2) a semantic memory store populated via Hebbian Distillation, wherein a Reflective Agent identifies densely connected memory hubs and distills them into structured, reusable semantic knowledge. This dual-path design leverages both semantic similarity and learned associations, mirroring the episodic-semantic distinction in human cognition. Experiments on LoCoMo demonstrate superior performance across four question categories while using significantly fewer context tokens. Code is available on GitHub: https://github.com/ReinerBRO/HeLa-Mem

Problem

• long-term memory
• associative memory
• Large Language Model agents
• Hebbian learning
• memory architecture

Innovation

• Hebbian learning
• associative memory
• episodic-semantic memory
• memory graph
• Hebbian Distillation