Self-organizing agent teams hit 66.7% across five benchmarks, beating their strongest member
Aneesh Pappu · hf · 2026-09-25
A new paper introduces Self-Organizing Agent Teams (SAT): fixed agent teams that learn reusable strategies for roles, phases, participation, and information flow from prior collaborations, enabling "collaborative computation."
- Strategies learned from just 15 math and 25 graduate-level problems transfer unchanged to unseen benchmarks
- 66.7% average accuracy across five math/physics benchmarks vs 48.8% for the strongest member, 58.7% compute-matched, 59.0% perfect router; +13.4 points over the router on AIME 2026
- Across eight benchmarks, "demonstrability" strongly predicts gains (Spearman ρ=0.90, p=0.005)
Takeaway: organization itself can become an agent capability.
Related event: Stanford's self-organizing agent teams beat best single models(3 posts)→
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