Bridging the Evidence-to-Execution Gap:A Reflective Agent for Multi-Objective Peptide Design

arXiv:2610.06190v2 Announce Type: replace Abstract: Large language models can reason over scientific literature, but textual plans do not reliably produce biological sequences with the intended properties. Sequence generators capture sequence patterns, yet they do not interpret evidence, choose interventions, or reflect on outcomes. This separation creates an evidence-to-execution gap between scientific reasoning and sequence-level control. We introduce EASER, a reflective agent that connects the two through a learned property interface. The interface consists of property-specific low-rank dir

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