This Is Why Distilled Models Collapse

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• Distilled models occupy “narrower capability manifolds” — they appear capable on benchmarks but fail on edge cases and agentic task compositions that define real production work.
• Model provenance matters for production reliability, not just ethics: a distilled model trained on frontier model outputs may fail unpredictably on tasks the frontier model handled robustly.