| title | ParetoSaddler: Pareto-Guided Optimization of Agent Harnesses for Quality and Cost |
|---|---|
| authors | Xingzhi Qian, Sungho Park, Rongyuan Tan, Jianbo Yu, Jue Zhang, Lorenzo Cavallaro, Menglin Xia, Yanzhi Chen, Xinnuo Xu, Yu Kang, Xinran Zheng, Gagan Somashekar, Xuchao Zhang, Chetan Bansal, Qingwei Lin, Victor Rühle |
| date | 2026-11-01 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2611.02079 |
| affiliation | Microsoft |
| upvotes | 16 (Hugging Face Daily Papers, 2026-11-05) |
| links | abs pdf hf |
| cite | |
| abstract | 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 showing credible progress in either quality or cost. Promoted candidates receive broader development evaluation and remain available as parents for later optimization steps. We evaluate ParetoSaddler on ClawMark and GAIA2 using domain-disjoint splits. On ClawMark, ParetoSaddler improves quality from 70.86\% to 81.35\% while reducing cost from \1.17 to 1.01 per task; on GAIA2, it improves quality from 56.22\% to 62.89\% while reducing cost from \0.78 to 0.45 per task. The Pareto-selected harnesses also achieve higher quality at lower cost than quality-only harness optimization. Analysis shows that Pareto-aware optimization retains intermediate candidates that would be discarded by deployment preference but can support later improvements, while Bayesian bootstrap screening filters out candidates whose gains are weakly supported across runs. |
bibtex
@misc{qian2026paretosaddler,
title={ParetoSaddler: Pareto-Guided Optimization of Agent Harnesses for Quality and Cost},
author={Xingzhi Qian and Sungho Park and Rongyuan Tan and Jianbo Yu and Jue Zhang and Lorenzo Cavallaro and Menglin Xia and Yanzhi Chen and Xinnuo Xu and Yu Kang and Xinran Zheng and Gagan Somashekar and Xuchao Zhang and Chetan Bansal and Qingwei Lin and Victor Rühle},
year={2026},
eprint={2611.02079},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2611.02079}
}