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arXiv AI By Zibo Zhao, Jijun Shi, Mo Zhou, Zhongyuan Wang, Shifu Bie, Yunfei Zhang, Xuanting Zhou, Xiangyu Wu, Bin Liu, Ruiming Tang, Wenwu Ou, Kun Gai

RobustSGPO: Search-Space Control for Agent Harness Evolution

Read the original on arXiv AI →

RobustSGPO extends semantic‑gradient‑based prompt optimization (SGPO) by explicitly defining the edit scope, constructing and validating patches, and allowing search to resume from either the current or retained snapshots. In the AgentX brainstorming workflow, experiments on 120 tasks and 95 runs show that periodic permission scheduling improves test scores, while RobustSGPO raises completion rates on held‑out tasks from 60.0% to 80.0% and test quality from 3.77 to 4.14 within a 20‑million‑token budget. The approach also demonstrates that category‑based retention mitigates source‑task degradation after a shift, and that search‑space control enhances quality through executable edits and alternative starting points.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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