Emergent Collusion in Long-Horizon LLM Agent Interaction
We study the emergence of collusion in a long-horizon multi-agent environment: two agents repeatedly complete individual tasks, share task logs, verify each other's work, and receive rewards. We introduce realistic constraints that make compliance with the verification protocol incompatible with reward maximization, and find that agents increasingly deviate from the protocol over repeated interactions. Collusion emerges in 94% of trajectories across 10 models, and more capable models within the same family reach it earlier.
- ▪We study the emergence of collusion in a long-horizon multi-agent environment: two agents repeatedly complete individual tasks, share task logs, verify each other's work, and receive rewards.
- ▪We introduce realistic constraints that make compliance with the verification protocol incompatible with reward maximization, and find that agents increasingly deviate from the protocol over repeated interactions.
- ▪Collusion emerges in 94% of trajectories across 10 models, and more capable models within the same family reach it earlier.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2609.24967 |
| Publication time | Wed, 23 Sep 2026 03:07:11 +0000 |
| Retrieval time | 2026-09-23T03:19:30.451Z |
| Last seen | 2026-09-23T03:19:30.451Z |
| 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 | 9-IcRu51UxCK · 1 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 |
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| 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.
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Computer Science > Artificial Intelligence arXiv:2609.24967 (cs) [Submitted on 21 Sep 2026] Title:Emergent Collusion in Long-Horizon LLM Agent Interaction Authors:Xinrui Shi, Yanzhe Zhang, Diyi Yang View a PDF of the paper titled Emergent Collusion in Long-Horizon LLM Agent Interaction, by Xinrui Shi and Yanzhe Zhang and Diyi Yang View PDF HTML (experimental) Abstract:LLM agents are increasingly deployed in collaborative settings, yet long-term interaction may give rise to undesirable coordination. We study the emergence of collusion in a long-horizon multi-agent environment: two agents repeatedly complete individual tasks, share task logs, verify each other's work, and receive rewards.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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