Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study
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Computer Science > Computation and Language
arXiv:2610.13287 (cs)
[Submitted on 9 Oct 2026]
Title:Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study
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Abstract:Prompt optimizers rewrite a prompt to raise one target metric, but prompts also state requirements the metric does not score, such as an output format, citations, or abstention. In a preregistered study, we run GEPA, MIPROv2, and TextGrad on HotpotQA, SQuAD 2.0, and IFEval and count per-item violations of unscored requirements with deterministic checks. By the preregistered criterion, all three optimizers produce regressions, substantially on HotpotQA, where net regression in citation validity reaches 10.7 to 41.6 points while exact match rises by 21 to 34 points. A post-hoc decomposition changes this reading. The optimized prompts elicit reasoning before the required JSON object, and under the 200-token output cap used throughout, 13.9% to 47.0% of responses end before any answer appears. Among parseable outputs, net regression is within 3 points of zero. With a 1,024-token cap, extraction failures disappear, content regression stays below 1 point, and exact match rises further. Two simple gates fail the preregistered criterion. The side effect is narrower than hypothesized: optimized prompts are more accurate and break an explicit output contract, and a lenient target metric does not show the cost.
| Comments: | 8 pages, 7 tables. Preregistered study |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.13287 [cs.CL] |
| (or arXiv:2610.13287v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.13287
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study, by Sangdo Han and 1 other authors
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