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TOPIC · ENTITY #23697

prompt optimization

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RESEARCH · 1 SOURCE · arXiv cs.AI

StruPO: structure-aware prompt editor that improves faithfulness in text-to-image generation

The authors introduce StruPO, a structure-aware prompt editor that conditions on a calibrated scene graph to guide token-level prompt edits (deletions, reordering, insertions) and uses image-grounded supervision and smooth reward shaping to train a reusable editing policy. Evaluated on three disjoint 2K-prompt test sets across four frozen generation backbones and validated by automated metrics plus a 15-annotator study, StruPO reportedly improves the trade-off between aesthetics and relational faithfulness without iterative critique–rewrite loops.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

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.

6.0