Compile as Hypothesis: A Headroom Typology for Prompt Optimization on Real Reasoning Models
Zhengshuai Yang, Changyun Li
Abstract
Automated prompt compilers search for or rewrite prompts under the implicit assumption that optimization consistently improves downstream task performance. We evaluate this assumption on frontier reasoning models through rigorous paired, same-day measurements. Across four optimizer families (genome search, profile-guided mutation, GEPA, and ESPO) and eight tasks, structured search yields no statistically significant accuracy gains on strong reasoners. However, a discrete structural genome provides essential architectural stability, preventing catastrophic bimodal seed-collapse (where unconstrained search collapses toward the judge floor in 3/5 runs). Through 190-problem evaluations on knowledge-intensive benchmarks, we establish a three-dimensional headroom typology: prompt optimization cannot compensate for intrinsic knowledge deficits or physical token truncation, but successfully resolves protocol and formatting errors. On gpt-6, while free-text reflective search (GEPA) yields a +.14 gain on a task where structured search remains flat, qualitative ablation reveals that ~.035 stems from judge-rubric reconstruction while ~.105 reflects genuine instruction refinement. Our pre-registered AutoAPC-Select deployment gate matches the oracle policy in 5/7 groups and rescues seed-collapse arms with zero regret. Finally, we expose silent backend-mixing in commercial APIs during outage windows and provide automated per-call audit verification tools to ensure empirical evaluation fidelity.