This work introduces MARCO, an evaluator-grounded reinforcement-learning framework that trains molecular editors on bounded proposal--feedback--revision trajectories and aggregates shaped turn rewards into an undiscounted trajectory return for group-relative policy optimization.
Shi-Cheng Fang, Yu-Xin Wang, Zhuo Yang et al.· 0 citations
TSBench is introduced, a benchmark in which an LLM agent uses structure-editing tools to construct three-dimensional transition-state (TS) guesses verified by an automated quantum-chemical pipeline, yielding a physics-grounded pass/fail verdict, establishing a mechanism-level yardstick for LLM agents in mechanism-sensi...
Applications in materials analysis, molecule design, and protein or antibody screening, together with experiments on scientific reading, idea generation, molecule generation, and antibody screening, show that SCION outperforms existing autonomous research-agent baselines, especially in decomposition, verification, refi...
Y. Zheng, Yuxin Wang, Jiahao Lu et al.· 0 citations
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