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As generative models and AI agents propose chemical reactions at a scale beyond expert review, feasibility verifiers decide which proposals enter synthesis planning. But do their decisions agree with chemists across different kinds of candidates? We introduce FREA, a benchmark of 751 reactions labeled by expert chemist...
This work presents AutoSDT, an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows and is the only automatically collected and the largest open dataset for data-driven scientific discovery.
MMORF is presented, a framework for constructing language model-based multi-agent systems (MAS) for multi-objective retrosynthesis planning that achieves strong safety and cost metrics on soft-constraint tasks, frequently Pareto-dominating baseline routes, and outperforming state-of-the-art baselines.
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