If you run automated prompt or harness evolution, this one is worth your time. (bookmark it) Semantic-gradient prompt optimization improves …

If you run automated prompt or harness evolution, this one is worth your time. (bookmark it) Semantic-gradient prompt optimization improves an agent harness from execution feedback, but its local update rule never decides how large an edit to request or which operation to apply. RobustSGPO adds that. It specifies the requested edit, constructs and checks the patch before accepting it, and continues search from either the incumbent or a retained snapshot. Measured over 120 tasks, 95 runs and 7,350 candidate attempts in the AgentX brainstorming workflow, completion on 30 held-out tasks rises from 60.0% to 80.0% and test quality from 3.77 to 4.14 under a 20-million-token budget. Periodic 1 to 2 to 3 edit-permission scheduling beats fixed maximum permission by 0.28 test-score points, so how much the optimizer is allowed to change per step is itself worth scheduling. Retention is a real trade. Category retention reduces source-task degradation after a task-family shift, while random retention reaches a higher destination endpoint, and both carry measurable overhead. Chat with Paper: https://academy.dair.ai/papers/robustsgpo-search-space-control-for-agent-harness-evolution-2609.09646

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Source: DAIR.AI (X) | 2026-09-10