Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality. Extending them to joint quality--cost optimization
ParetoSaddler improves LLM agent harnesses by separately promoting intermediate candidates via Bayesian bootstrap Pareto support and selecting final deployments, boosting quality while reducing cost.
Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality. Extending them to joint quality--cost optimization introduces a challenge: applying the deployment preference during intermediate updates can prematurely reject candidates that are valuable for later improvements. We introduce ParetoSaddler, which addresses this mismatch by separating intermediate candidate promotion from final deployment selection. At each iteration, an optimizer updates a parent harness to produce a candidate, and then compares the two over repeated executions and uses Bayesian bootstrap Pareto support to promote candidates…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
ParetoSaddler: Pareto-Guided Optimization of Agent Harnesses for Quality and Cost was first seen on Hugging Face · Daily papers on Nov 4, 2026. Since then it has appeared in 11 checks on 2 different days. Its best position was #14 of 30, reached on Nov 4, 09:28 UTC. It dropped off the list after Nov 5, 01:28 UTC.
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It was first seen on Nov 4, 2026 and has been on the list on 2 days (11 checks) since.