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AgentFugue is introduced as a collective reasoning framework where multiple peer agents tackle the same long-horizon task in parallel, communicating via a shared reasoning hub that records and shares concise notes on each agent's progress. This allows agents to selectively access and reuse intermediate reasoning steps discovered by others, creating a connected ecology of knowledge. The communication layer is trained with supervised fine-tuning and end-to-end reinforcement learning, demonstrating performance improvements over strong baselines in challenging long-horizon tasks.
Scaling out peer agents with a shared reasoning hub, AgentFugue, unlocks a new dimension of capability gains in long-horizon tasks, proving that collective reasoning is more than just parallel compute.
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. In contrast, much less is understood about scaling out: whether multiple peer agents, all targeting the same task, can become an additional source of capability without relying on explicit role specialization or workflow orchestration. We study this question and propose AgentFugue, a collective reasoning framework built around a shared reasoning hub. As peer agents explore the same task in parallel, the hub records concise notes on what each agent has established, attempted, or ruled out, and enables each agent to selectively access what other agents have discovered in a form useful for its current search. This design turns otherwise isolated trajectories into a connected ecology of reusable intermediate reasoning without requiring centralized planning. We instantiate the hub as a plug-in communication layer, trained with supervised fine-tuning and end-to-end reinforcement learning. Across the challenging long-horizon settings we study, AgentFugue improves over strong baselines. Our results suggest that collective reasoning can turn scaling out peer agent systems into a distinct source of capability gains, rather than merely a way of spending more compute.
