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SEPO: Evidence-Grounded Prompt Optimization via Structural Editing

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

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 ta

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-08-28T08:37:34.000Z

First collected: 2026-09-21T08:21:55.975Z. This is not the publication date.