Reproducible offline evaluation enables correctness‑gated prompt compression for production agents (arXiv:2610.31972v1)
The paper (arXiv:2610.31972v1) presents an offline evaluation harness that remains reproducible despite live, mutable backend data and can be regenerated when stale, and uses that harness as the fitness function for GEPA-based prompt optimization guarded by a correctness gate to prevent reward hacking. Applied to a production merchant-facing conversational agent, the correctness-gated compression reduced the agent's prompt surface by 3.5–7.6% without regressing held-out task correctness across three independent subject-model families, and improved correctness for one family.