Evidence Scaling for Zero-Shot Protein Reasoning with Large Language Models

Abstract Large language models (LLMs) show emerging zero-shot capability for protein variant prediction, yet still lag behind specialized protein models. We ask whether this gap can be reduced by scaling access to biological evidence rather than adapting model parameters. We introduce B io E vidence , a training-free and model-agnostic interface that converts structural and evolutionary information from standard biological tools into compact evidence for frozen LLMs. On the ProteinGym benchmark, we observe evidence scaling: performance improves as evidence becomes richer. Structural and evolutionary evidence each improve performance, and combining them yields further gains, while mismatching the same evidence to the wrong variants degrades performance below the no-evidence baseline. Notably, B io E vidence enables zero-shot ranking to reach strong specialized protein predictors on matched evaluations, and the improvement persists on post-cutoff data released after the model’s knowledge cutoff. Evidence also interacts with conventional scaling: for GPT-5.6 Sol, evidence at low reasoning effort outperforms the no-evidence condition at medium effort, while a six-model analysis associates stronger no-evidence performance with larger margins over evolutionary rank fusion. These results identify external evidence as a complementary scaling axis for scientific prediction alongside model capability and inference effort.