Papers for

workflow automation engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

RobustSGPO improves AI agent prompt editing and task success rates

RobustSGPO: Search-Space Control for Agent Harness Evolution

Abstract: Semantic-gradient-based prompt optimization (SGPO) improves agent harnesses using execution feedback, but its local update rule leaves the choice of edit scope and operation unresolved. We introduce RobustSGPO, which specifies the requested edit, constructs and checks the patch, and continues search from either the incumbent or retained snapshots. We evaluate permission scheduling, cumulative controls, and task-family transfer in the AgentX brainstorming workflow using 120 tasks, 95 runs, and 7,350 candidate attempts. Periodic $1\to2\to3$ scheduling exceeds fixed maximum permission by 0.28 test-score points. RobustSGPO increases completion on 30 held-out tasks from 60.0% to 80.0% and improves test quality from 3.77 to 4.14 under a 20-million-token budget. Category retention reduces source-task degradation after a shift, whereas random retention reaches a higher destination endpoint. Search-space control benefits quality through executable edits and alternative starting points, with measurable retention overhead.

Wed 9 SeptArtificial Intelligence

The gist

Improving AI agents often involves tweaking how they are instructed or prompted. The authors found that a common method can get stuck by only making local, small changes without clear rules about how to edit. They created RobustSGPO, a method that controls where and how edits happen, checks these edits before applying them, and manages different starting points during the search. This approach led to better performance on a set of brainstorming tasks, increasing how often tasks were completed and improving overall quality.

Open → 2609.09646v1