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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Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2604.16839 |
| Publication time | Tue, 28 Apr 2026 13:06:01 +0000 |
| Retrieval time | 2026-04-28T13:14:31.934Z |
| Last seen | 2026-04-28T13:14:31.934Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 1lA0fF1VfNGc |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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