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. Across five mathematics and physics benchmarks, self-organizing teams average $66.2\%$ accuracy, versus $48.8\%$ for their strongest member, $59.2\%$ for compute-matched inference by the strongest individual agent, and $59.0\%$ for a perfect router over members' independent answers; on AIME 2026, they exceed this router by $10.6$ percentage points. Because these gains vary across benchmarks, we ask when self-organizing collaboration improves over individual models. Across eight benchmarks, demonstrability—an organizational-psychology construct capturing whether correct reasoning can be distinguished from incorrect reasoning—strongly tracks how much the team improves over its strongest member (Spearman $\rho =0.88$, $p=0.007$), suggesting that self-organizing agent teams benefit most when correct reasoning can be recognized once it appears. More broadly, these results suggest that organization itself can become an agent capability: agent teams can learn how to reason together and produce solutions their members could not reach independently.