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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.
The implementation lottery reveals that relying on a single run can mislead research conclusions, with winner reversals occurring in up to 43.6% of cases.
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.
General-purpose LLM agents stumble badly when faced with the messy reality of diverse, multi-domain tasks, and simply scaling interactions or parallel sampling doesn't fix it.
