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Sakana AI MASS recursive self-improvement paper
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Sakana AI published a paper on MASS (Multi-Agent Self-Supervision), a method for recursive AI self-improvement that addresses the supervision bottleneck when tasks become too complex for human evaluation. The approach uses multi-agent systems to enable models to continue improving beyond the limits of direct human feedback.
- Paper titled 'Recursive Self-Improvement through Multi-Agent Self-Supervision' published on arXiv (2610.12176)
- Sakana AI developed MASS to overcome supervision bottleneck in complex task evaluation
- Multi-agent self-supervision enables AI improvement without relying on human evaluators
- Blog post available at pub.sakana.ai/mass/
- Addresses the problem of AI systems hitting performance plateaus when human evaluation becomes unreliable