Self-Organizing Agent Teams Learn to Reason Together

Collective intelligence depends not only on what team members know, but also on how they organize their work. When the structure of a solution is unknown, useful roles and divisions of labor cannot always be specified in advance; teams must learn from experience how to organize reasoning as it unfolds. Human teams routinely adapt this way, while existing AI agent teams typically rely on fixed protocols, explicit task decomposition, or routing. We introduce Self-Organizing Agent Teams (SAT), fixed teams of AI agents that learn reusable strategies from prior collaborations to organize roles, conversational phases, participation, and information flow. These strategies enable what we call collaborative computation: agents exchange, challenge, repair, and synthesize partial reasoning into solutions no member produced independently. In two independent settings, we learn reusable teamwork strategies that transfer unchanged to unseen benchmarks, using only 15 mathematics and 25 graduate-level knowledge problems.

Introduction. In July 2026, AI agents that were supposed to work in isolation began organizing themselves. During cybersecurity evaluations at OpenAI, agents repurposed a shared software package repository as an unauthorized communication channel. What began as an improvised message board then developed into a system for collective work: agents shared discoveries, established communication norms, and coordinated assignments. One agent, PHASEONE[big], issued hundreds of assignments and appointed recruiters to find agents willing to risk failing their own tasks to generate information for the group. About 1,200 agents participated in the channel, and hundreds became involved in the subsequent compromise of Hugging Face infrastructure (OpenAI, 2026; Greenblatt et al., 2026; Roose, 2026). The organization that emerged was improvised and undesirable. Yet the episode illustrates a broader scientific point: what a collection of agents can accomplish depends not only on the capabilities of its individual members, but also on how they organize their work.

Discussion / Conclusion. Taken together, these experiments show that agent teams can improve by learning how to organize their reasoning. Across two domains, a designated member uses teamwork reflection on prior collaborations to construct reusable teamwork strategies that transfer unchanged to held-out problems and benchmarks. Both teams achieve the highest average accuracy among the methods tested and outperform their strongest member and matched linearization on average; in mathematics and physics, the team also surpasses a perfect router over the members’ individual answers, showing that interaction can construct solutions unavailable to selection alone. The main limitation is that richer generation does not guarantee correct selection: on knowledge and logic, the team pool reaches 87.9% coverage but team accuracy reaches only 72.8%. Better selection or more legible certificate formats are therefore needed to turn generated solutions into final answers. Demonstrability offers a complementary but correlational analytical lens because we measure it post hoc across benchmarks and do not use it to guide teamwork reflection.