State Contamination in Memory-Augmented LLM Agents
The paper discusses the issue of state contamination in memory-augmented LLM agents. It highlights a failure mode called memory laundering, where toxic information can be compressed into memory summaries that appear safe. The authors propose a new metric to measure the hidden influence of such memory states on downstream toxicity.
- ▪Memory-augmented LLM agents rely on persistent state for long-horizon interaction.
- ▪Toxic or adversarial context can be compressed into memory summaries that evade detection.
- ▪The authors introduce the sub-threshold propagation gap (SPG) to quantify hidden influences on behavior.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.16746 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
| 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 | b0ZGiaTO0Ih4 · 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 |
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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 > Artificial Intelligence arXiv:2605.16746 (cs) [Submitted on 16 May 2026] Title:State Contamination in Memory-Augmented LLM Agents Authors:Yian Wang, Agam Goyal, Yuen Chen, Hari Sundaram View a PDF of the paper titled State Contamination in Memory-Augmented LLM Agents, by Yian Wang and 3 other authors View PDF HTML (experimental) Abstract:LLM agents increasingly rely on persistent state, including transcripts, summaries, retrieved context, and memory buffers, to support long-horizon interaction. This makes safety depend not only on individual model outputs, but also on what an agent stores and later reuses.
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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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