HarnessPro: Recursive Harness Self-Improvement with Pareto Optimization for Performance-Cost Trade-offs
We introduce HarnessPro, a label-free method for evolving harnesses to improve task performance while reducing execution cost.
ProofPaper ↗
Key points
- Harness development plays a crucial role in building capable AI agents.
- Recent approaches improve harnesses through recursive self-improvement (RSI), but their performance gains often come with higher execution costs.
- Specialized agents diagnose execution trajectories, plan updates with different performance-cost priorities, and implement them as candidate harnesses.
- Rather than returning a single harness, HarnessPro uses normalized weighted squared-distance selection to provide cost-focused (lite), balanced (medium), and performance-focused (heavy) harnesses for different deployment budgets.
Sources (2)
- [1]HarnessPro: Recursive Harness Self-Improvement with Pareto Optimization for Performance-Cost Trade-offsHugging Face Daily Papers · Oct 19, 12:00 AM
We introduce HarnessPro, a label-free method for evolving harnesses to improve task performance while reducing execution cost.
Harness development plays a crucial role in building capable AI agents.
- [2]HarnessPro: Recursive Harness Self-Improvement with Pareto Optimization for Performance-Cost Trade-offsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 05:27 PM · same content
Extractive summary: sentences quoted from the sources.
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