Harmony Harness: Multi-Objective Self-Evolution of Agent Harnesses
We introduce Harmony Harness, a framework for multi-objective, compositional harness evolution.
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Key points
- Recent work on self-evolving LLM agents treats the agent harness as an optimization target that can be iteratively improved through execution feedback.
- Existing approaches, however, largely optimize a single objective and evolve the harness as a monolithic artifact, limiting their ability to handle competing goals and to separate transferable design from environment-specific adaptation.
- In parallel, we introduce factorized evolution operators that separately refine general-purpose harness mechanisms and benchmark-specific adaptations, together with a dynamic composition workflow that integrates them at inference time.
- Our results show that explicitly modeling both objective trade-offs and harness structure yields substantially stronger and more transferable agent evolution than existing single-objective, monolithic approaches.
Sources (1)
- [1]Harmony Harness: Multi-Objective Self-Evolution of Agent HarnessesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 08:06 PM
We introduce Harmony Harness, a framework for multi-objective, compositional harness evolution.
Recent work on self-evolving LLM agents treats the agent harness as an optimization target that can be iteratively improved through execution feedback.
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