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DCP provides a common evidence language for useful outcomes, alternative routes, and feedback effects across AI research, and is applied to Core, recovered, and audit-incomplete decisions.
Nine out of ten AI-selected modeling changes in materials science remain effective when tested on unseen data, showcasing the potential for reusable AI-driven discoveries.
Continual learning for LLM agents hits a wall: scaling models doesn't reliably improve skill generation, and self-feedback can lead to recursive drift.
