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bioRxiv · 10.64898/2026.08.15.745007

CryoForge: A Self-Correcting Agent for Cryo-EM Model Building That Learns When to Act and When to Stop

Feng, W.·Jiang, y.·Sun, F.·Yang, J.·Gao, X.·Zhang, F.·Han, R.
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Abstract

Automated atomic model building has accelerated cryo-EM structure determination, but different builders leave distinct residual error profiles requiring expert inspection. The post-building challenge is to decide which local interpretations are sufficiently supported by experimental evidence to be retained, corrected or rejected. Here we introduce CryoForge, an evidence-gated post-builder agent that separates repair proposal from repair acceptance. Rule and learning-based components identify candidate regions and prioritize legal actions, whereas an independent evidence gate evaluates each edit using map and half-map support, stereochemistry, connectivity and local structural context. Supported edits are retained; unsupported or conflicting modifications are rejected, rolled back, stopped or escalated for expert review. Across a resolution-stratified benchmark, 84.9% of 26,153 released trajectories yielded standard validated improvements and 3.7% yielded low-confidence partial improvements, with no quality-degrading edit retained in the final promoted models. Relative to rule-only control, learned prioritization reduced non-improving candidates and harmful actions while preserving global structural stability. External evaluations using an alternative initializer, same-team automated/manual-assisted challenge submissions and three recently released complex assemblies showed that CryoForge adapts to distinct residual error phenotypes and performs bounded, evidence-supported correction without uncontrolled remodeling. CryoForge provides a builder-independent, scalable and auditable correction layer between automated model generation and expert structural interpretation.

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Feng, W., Jiang, y., Sun, F., Yang, J., Gao, X., Zhang, F., Han, R.. 2026-08-20. CryoForge: A Self-Correcting Agent for Cryo-EM Model Building That Learns When to Act and When to Stop. https://doi.org/10.64898/2026.08.15.745007

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