HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents
The paper introduces HeLa-Mem, a novel memory architecture for Large Language Model agents inspired by biological memory mechanisms. It emphasizes the importance of associative memory and proposes a dual-level organization to enhance memory retention and retrieval. Experimental results indicate that HeLa-Mem outperforms existing systems while using fewer context tokens.
- ▪HeLa-Mem addresses the limitations of fixed context windows in Large Language Models.
- ▪The architecture incorporates mechanisms such as association, consolidation, and spreading activation, which are prevalent in human memory.
- ▪Experiments demonstrate that HeLa-Mem achieves superior performance across various question categories.
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Computer Science > Computation and Language arXiv:2604.16839 (cs) [Submitted on 18 Apr 2026] Title:HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents Authors:Jinchang Zhu, Jindong Li, Cheng Zhang, Jiahong Liu, Menglin Yang View a PDF of the paper titled HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents, by Jinchang Zhu and 4 other authors View PDF HTML (experimental) 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.
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