Self-Organizing Agent Teams Learn to Reason Together
Self-organizing agent teams learn collaboration strategies and beat their strongest member on math and physics benchmarks.
Self-Organizing Agent Teams are fixed groups of AI agents that learn reusable strategies for roles, conversational phases, participation, and information flow from prior collaborations. Strategies learned from only 15 mathematics and 25 graduate-level problems transfer unchanged to unseen benchmarks. Across five math and physics benchmarks the teams average 66.7% accuracy, versus 48.8% for the strongest member and 59.0% for a perfect router, and they exceed that router by 13.4 points on AIME 2026. Demonstrability of correct reasoning strongly predicts collaboration gains, with Spearman ρ of 0.90.
- Teams trained on 40 problems transfer strategies to unseen benchmarks.
- Average accuracy is 66.7% versus 48.8% for the strongest member.
- On AIME 2026 they exceed a perfect router by 13.4 points.
- Demonstrability tracks gains over the strongest member with Spearman ρ of 0.90.
From the original article
1 min read at huggingface.co
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 be specified in advance; teams must learn from experience how to organize reasoning as it unfolds.
By Aneesh Pappu, Mirac Suzgun, Yongchan Kwon · huggingface.co
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