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

Excerpt

arXiv:2610.06190v1 Announce Type: new Abstract: Large language models (LLMs) can reason over scientific literature to devise design strategies, yet fail to reliably implement them for biological sequences. While protein generative models learn sequence patterns, they lack the capacity to incorporate literature evidence for multi-step reflective reasoning, forming an evidence-to-execution gap between scientific reasoning and sequence manipulation. We present EASER (Evidence-Aware Sequence Enginee