From Evidence to Action: Experience- and Knowledge-Grounded Evolution for Materials Discovery

Iterative materials discovery entails a loop of evaluating generated candidates and feeding the results back into search. Yet evaluation alone does not determine which evidence a task should use or which action a changing search state should trigger. We study this evaluation-to-action gap in heterogeneous, budget-constrained materials discovery. We propose Evidence-to-Action (E2A), an evidence-grounded decision framework for iterative materials discovery that converts internal search experience and retrieved external knowledge into task-conditioned workflows and state-conditioned search actions. The Workflow Policy selects an evidence-use pathway once per run, while within the Full-evidence pathway, the Evolution Policy periodically selects an executable search action from the available state and evidence. In experiments across fourteen tasks, E2A improves hit rate and stable-success rate on eleven tasks each under matched GPT-4o run-native evaluation. Task-conditioned routing exceeds every fixed workflow on the historical return table, while a matched three-task controller study shows task-dependent gains from learned state-conditioned search actions. Together, these findings show how evaluated outcomes become useful through task-appropriate evidence pathways and state-conditioned actions.