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Self-evolving AI agents for protein discovery and directed evolution

arXiv cs.AI / 3/31/2026

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Key Points

  • The paper proposes an autonomous “self-evolving” multi-agent framework (VenusFactory2) aimed at removing bottlenecks in protein scientific discovery caused by manual orchestration of information and algorithms.
  • Instead of relying on static tool calls, VenusFactory2 dynamically synthesizes workflows to better handle complex, domain-specific protein projects that general-purpose agents struggle with.
  • The authors report that VenusFactory2 outperforms several established agent approaches on the VenusAgentEval benchmark.
  • VenusFactory2 is described as able to organize protein discovery and optimization end-to-end starting from a single natural-language prompt, including both discovery and directed evolution tasks.

Abstract

Protein scientific discovery is bottlenecked by the manual orchestration of information and algorithms, while general agents are insufficient in complex domain projects. VenusFactory2 provides an autonomous framework that shifts from static tool usage to dynamic workflow synthesis via a self-evolving multi-agent infrastructure to address protein-related demands. It outperforms a set of well-known agents on the VenusAgentEval benchmark, and autonomously organizes the discovery and optimization of proteins from a single natural language prompt.

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