AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning
The paper introduces AgentFugue, a framework designed for scaling agent capabilities in long-horizon tasks through collective reasoning. It emphasizes the potential of multiple peer agents working in parallel to enhance task performance without centralized planning. The findings suggest that this approach can yield significant capability gains compared to traditional methods.
- ▪AgentFugue is built around a shared reasoning hub that facilitates communication among peer agents.
- ▪The framework allows agents to access and utilize discoveries made by others, enhancing their individual search processes.
- ▪The study demonstrates that collective reasoning can improve performance in challenging long-horizon settings.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24486 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| 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 | nc_yZMH_4Csn · 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:2605.24486 (cs) [Submitted on 23 May 2026] Title:AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning Authors:Yuyang Hu, Hongjin Qian, Shuting Wang, Jiongnan Liu, Tong Zhao, Xiaoxi Li, Zheng Liu, Zhicheng Dou View a PDF of the paper titled AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning, by Yuyang Hu and 7 other authors View PDF HTML (experimental) Abstract:Recent progress on long-horizon agentic tasks has been driven largely by scaling up individual agents through stronger models, better tools, and more effective scaffolding.
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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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