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As of Oct 11, Sakana AI published MASS, a multi-agent self-supervision loop where one model organizes copies of itself into research teams and trains on selected trajectories.
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Sakana AI publishes MASS, where one model organizes copies of itself into research teams and trains on what worked
Sakana AI published this on Oct 11, 2026 (source: pub.sakana.ai).
Sakana AI and UC Berkeley's MASS (Multi-Agent Self-Supervision) page describes a loop in which the same model acts as executor, workflow optimizer and evaluator: it searches for better multi-agent workflows, then fine-tunes itself on selected team trajectories. Starting from Qwen3.6-27B, after two rounds the paper reports about 1.2–1.6× score per output token vs the base on four research benchmarks (MLR-Bench, DSBench, ScienceAgentBench, AstaBench), and a 69.9% win rate vs base on held-out synthetic test tasks. Distinct from Sakana's earlier Multi-Layered Review item.
Source: Sakana AI
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