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LLMs can leapfrog state-of-the-art scientific algorithms and human-designed solutions, but only if you scale the evaluation loop, not just the model.
End-to-end retrosynthetic planning, previously reliant on fragmented prediction-search hybrids, now achieves state-of-the-art performance thanks to a unified, reasoning-driven generative framework.
