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    <abstract>Existing API-only prompt optimisers are often described as interpretable, but in practice, this usually means only post-hoc inspectability: each iteration still rewrites the prompt as one opaque string, leaving a trace of full-prompt diffs rather than localisable, machine-readable edits. This paper introduces SEPO (Structural, Evidence-grounded Prompt Optimization), a multi-trajectory prompt optimiser centred on edit-effect lineage feedback. Rather than treating each iteration as an isolated whole-prompt rewrite, SEPO locally edits stable, typed units in a two-layer prompt schema, links the target and realised structural operations of each edit to the examples it newly fixes or breaks, and carries this edit–effect record forward to guide later architect calls on the same search branch. This makes prompt optimisation addressable, attributable, and actionable. Across a 14-task held-out suite, SEPO improves over the strongest baseline, GEPA, by 3.1 pp on Llama-3.1-8B-Instruct and 2.2 pp on Qwen3-8B, reaching 61.9% and 73.3% macro accuracy. SEPO also lies on both the optimisation-time and test-time Pareto frontiers, spending 2.9M optimisation tokens versus 4.1M for GEPA and producing prompts over 5× shorter.</abstract>
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%0 Conference Proceedings
%T SEPO: Evidence-Grounded Prompt Optimization via Structural Editing
%A Ma, Xiaoyu
%A Liu, Haoyue
%A li, Yiwen
%A Zhu, Jionghao
%A Wang, Zhichao
%A Chen, Ye
%A Tang, Xiaoying
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ma-etal-2026-sepo
%X Existing API-only prompt optimisers are often described as interpretable, but in practice, this usually means only post-hoc inspectability: each iteration still rewrites the prompt as one opaque string, leaving a trace of full-prompt diffs rather than localisable, machine-readable edits. This paper introduces SEPO (Structural, Evidence-grounded Prompt Optimization), a multi-trajectory prompt optimiser centred on edit-effect lineage feedback. Rather than treating each iteration as an isolated whole-prompt rewrite, SEPO locally edits stable, typed units in a two-layer prompt schema, links the target and realised structural operations of each edit to the examples it newly fixes or breaks, and carries this edit–effect record forward to guide later architect calls on the same search branch. This makes prompt optimisation addressable, attributable, and actionable. Across a 14-task held-out suite, SEPO improves over the strongest baseline, GEPA, by 3.1 pp on Llama-3.1-8B-Instruct and 2.2 pp on Qwen3-8B, reaching 61.9% and 73.3% macro accuracy. SEPO also lies on both the optimisation-time and test-time Pareto frontiers, spending 2.9M optimisation tokens versus 4.1M for GEPA and producing prompts over 5× shorter.
%R 10.18653/2026.findings-emnlp.1895
%U https://aclanthology.org/2026.findings-emnlp.1895/
%U https://doi.org/10.18653/2026.findings-emnlp.1895
%P 40364-40391

Markdown (Informal)

[SEPO: Evidence-Grounded Prompt Optimization via Structural Editing](https://aclanthology.org/2026.findings-emnlp.1895/) (Ma et al., Findings 2026)

• SEPO: Evidence-Grounded Prompt Optimization via Structural Editing (Ma et al., Findings 2026)

ACL

• Xiaoyu Ma, Haoyue Liu, Yiwen li, Jionghao Zhu, Zhichao Wang, Ye Chen, and Xiaoying Tang. 2026. SEPO: Evidence-Grounded Prompt Optimization via Structural Editing. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 40364–40391, Budapest, Hungary. Association for Computational Linguistics.