Canonical event

MASS researchers report two bounded recursive multi-agent self-improvement cycles

UC Berkeley/Sakana AI researchers published MASS on October 8, 2026. A Qwen3.6-27B model was used as workflow optimizer, evaluator and executor across two laboratory cycles with model weight updates based on self-selected multi-agent trajectories. Reported external-LLM-judge win rates on three held-out synthetic tasks versus the base model were 53.9% after cycle 1 and 69.9% after cycle 2, with cycle 2 winning 60.8% against cycle 1. Research benchmarks reached 1.2–1.6x base score per output token; Terminal-Bench 2.0 (0.96x) and SWE-bench Verified (0.94x) did not improve. This reports bounded preprint evidence, not independently verified indefinite or accelerating recursive self-improvement, a new civilization transition, or proven deployment.