CoEvolve: Traceable Reflective Memory Evolution for Prompt Optimization

Reflective prompt optimization turns model failures into reusable natural-language guidance, but free-form memory is difficult to attribute, repair, and control as it grows. We introduce , a prompt optimizer that represents each candidate as a traceable set of reflection identifiers and evolves this memory under an explicit budget. The system couples candidate lineage with a persistent reflection store and combines automatic failure-to-policy Distill, structured memory mutations, shared evaluation, and parent-relative memory-growth-aware selection. In five-run, equal-budget locked-test comparisons spanning financial tagging and arithmetic, multi-hop question answering, and differential diagnosis, CoEvolve-Distill leads on FINER, HotpotQA, and DDXPlus. An independent-split protocol reduces the selection optimism gap from 0.023 to 0.007–0.011. Removing persistent state causes the largest ablation loss and variability, whereas score-only selection preserves quality but uses substantially more policy tokens. In controlled recovery episodes, full provenance improves harmful-memory identification and recovery while retaining at least 0.978 of task score. These results support treating reflective memory as an explicit, auditable optimization object rather than unstructured prompt text.