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

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. 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: this https URL Comments: Accepted to ACL 2026 Subjects: Computation and Language (cs.CL) Cite as: arXiv:2604.16839 [cs.CL] (or arXiv:2604.16839v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2604.16839 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jinchang Zhu [view email] [v1] Sat, 18 Apr 2026 05:11:18 UTC (3,832 KB) Full-text links: Access Paper: View a PDF of the paper titled HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents, by Jinchang Zhu and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces…

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