RESEARCH · RESEARCH · #2558
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.
KEY POINTS
- 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.
- Provides a practical, reproducible evaluation method for live-data conversational agents and shows a guarded prompt‑compression technique that reduces prompt size without harming (and sometimes improving) task correctness.
WHY IT MATTERS
Provides a practical, reproducible evaluation method for live-data conversational agents and shows a guarded prompt‑compression technique that reduces prompt size without harming (and sometimes improving) task correctness.