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VeRO: An Evaluation Harness for Agents to Optimize Agents
arXiv – CS AI|Varun Ursekar (Emily), Apaar Shanker (Emily), Veronica Chatrath (Emily), Yuan (Emily), Xue, Sam Denton||6 views
🤖AI Summary
Researchers introduced VeRO (Versioning, Rewards, and Observations), a new evaluation framework for testing AI coding agents that can optimize other AI agents through iterative improvement cycles. The system provides reproducible benchmarks and structured execution traces to systematically measure how well coding agents can improve target agents' performance.
Key Takeaways
- →VeRO introduces the first systematic evaluation harness for agent optimization, where AI agents iteratively improve other AI agents.
- →The framework addresses unique challenges of evaluating agents that combine deterministic code with stochastic LLM completions.
- →VeRO provides versioned agent snapshots, budget-controlled evaluation, and structured execution traces for reproducible research.
- →An empirical study using VeRO analyzed which optimizer configurations and modifications reliably improve target agent performance.
- →The framework has been released as open-source to support research on agent optimization as a core AI capability.
Read Original →via arXiv – CS AI
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