H-Mem: A Novel Memory Mechanism for Evolving and Retrieving Agent Memory via a Hybrid Structure
The paper introduces H-Mem, a new memory mechanism designed for agents using Large Language Models. This mechanism aims to enhance the evolution and retrieval of memory data, addressing limitations in current approaches. H-Mem utilizes a hybrid structure that combines temporal and semantic trees with knowledge graphs to improve performance on question-answering tasks.
- ▪H-Mem is a novel memory mechanism for agents using Large Language Models.
- ▪It effectively models the evolution of memory data over time and improves retrieval efficiency.
- ▪Extensive experiments demonstrate that H-Mem achieves state-of-the-art performance on question-answering tasks.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15701 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
| 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 | 1lP8ew8GZw5X |
| 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 |
Rights status (four layers)
WeSearch handling by dimension
| 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:2605.15701 (cs) [Submitted on 15 May 2026] Title:H-Mem: A Novel Memory Mechanism for Evolving and Retrieving Agent Memory via a Hybrid Structure Authors:Jiawei Yu, Yixiang Fang, Xilin Liu, Yuchi Ma View a PDF of the paper titled H-Mem: A Novel Memory Mechanism for Evolving and Retrieving Agent Memory via a Hybrid Structure, by Jiawei Yu and 3 other authors View PDF HTML (experimental) Abstract:Memory data are ubiquitous in Large Language Model (LLM)-based agents (e.g., OpenClaw and Manus).
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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