A swarm of AI/ML models subjected to deliberate mutations of their model coefficients away from optimality can reliably and sustainably improve performance in changing environments by acting as a statistical hedge against non-stationarity. ^[1] According to Allora Labs, its paper "Flawed in Nature, Perfect through Evolution" by J. M. Diederik Kruijssen was published on September 2, 2026, in the journal Allora Decentralized Intelligence, Volume 3, pages 1-32. ^[2] Allora Labs Chief Scientist Diederik Kruijssen claimed that any single AI model, however well trained, accumulates error as soon as its training environment changes, and that the paper mathematically proves a population of deliberately mutated models breaks through this "inescapable information-theoretic ceiling". ^[3] In synthetic linear regression tasks, the mutated swarm delivers the best model in approximately 80% of environment changes. ^[4] The paper proves via four theorems that the resulting regret reduction is guaranteed under general conditions. ^[5] Allora Labs claimed that the best model in a mutated model swarm outperforms the best model in an optimized swarm approximately 80% of the time when conditions change. ^[6] Allora Labs claimed the paper contains four mathematical theorems proving no single model can escape the performance ceiling and that a population of mutated models provably breaks through it, with numerical experiments validating the theorems at high statistical significance. ^[7] The performance of artificial intelligence and machine learning models degrades when the problem they were trained on drifts, a near-universal feature of real-world problems that change unpredictably. ^[8]