DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees
The paper introduces DeltaMem, a framework designed to enhance memory management in Large Language Model (LLM) agents. It organizes experiences into two residual trees to reduce redundancy and improve retrieval accuracy. Experimental results demonstrate that DeltaMem outperforms existing memory management methods in various interactive environments.
- ▪DeltaMem organizes experience memory into two independent residual trees.
- ▪One tree stores goal-conditioned task experience while the other focuses on scene-level environment knowledge.
- ▪The framework allows related experiences to share a common foundation without duplication.
2 outlets in our directory ran this story, first to last over 7 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ Which LLM Memory for AI Agents? — Tech blog
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.03083 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
| 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 | XjuuvjQ7Uh5B · 2 stories |
| 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 > Artificial Intelligence arXiv:2606.03083 (cs) [Submitted on 2 Jun 2026] Title:DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees Authors:Haoran Tan, Zeyu Zhang, Zhicheng Cao, Rui Li, Xu Chen View a PDF of the paper titled DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees, by Haoran Tan and 4 other authors View PDF HTML (experimental) Abstract:Large Language Model (LLM)-based agents increasingly rely on memory to learn from experiences over continual interactions. However, storing experiences as independent, flat units leads to substantial redundancy and retrieval conflicts, as similar episodes repeat overlapping content and subtle scene variations cause retrieved memories to offer contradictory guidance.
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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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