EviRead: AI Agents Learn to Read Biological Evidence through Recursive Improvement

AI agents have made substantial progress in software engineering and mathematics, building on language models strengthened by data and model scaling. The same agent wave has also reached biology, with the hope of combining agents with bioinformatics tools to accomplish downstream tasks more intelligently and automatically. A gap remains between them, however: the outputs of biological tools, such as biological language models (BioLMs), do not automatically translate into better evidence an agent can read and reason over. We introduce EviRead, a two-level framework that turns this heterogeneous biological evidence into agent-readable evidence through recursive improvement (RI). We instantiate EviRead on TempoGO-HQ, a retrospective temporal benchmark of 1,389 proteins for Gene Ontology term prediction. The BioLM-only workflow EviRead_base runs five RI rounds and improves on both the training and validation splits, showing that the loop can diagnose and iteratively refine the workflow. The second, tool-augmented workflow EviRead_+tools integrates sequence alignment, structural search, and GO predictors, and its final workflow outperforms all twelve compared methods in ontology-macro Fmax. By making evidence construction and tool coordination learnable at the workflow level, EviRead offers a concrete route to connect specialized biological models and tools with agent-based inference.