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AI agents for autonomous scientific discovery must decide which experiments to run under a limited budget. Most benchmarks for science agents provide fixed datasets, while others that support active experimentation largely rely on simulation. We introduce Autonomous Science Bench, an initial release comprising six budg...
POLO (Preference-guided Optimization for Lead Optimization), a multi-turn Reinforcement Learning framework for sample-efficient lead optimization, which extracts learning signals from each trajectory at two complementary levels: trajectory-level policy optimization reinforces successful multi-turn strategies, while tur...
Lead optimization in drug discovery requires iteratively refining molecular candidates while preserving structural similarity to the original compound. Since each evaluation is costly, sample efficiency, the ability to achieve strong performance with limited oracle calls, becomes critical. Existing methods, from geneti...
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