Benchmarking and Standardization of Evaluation Protocols: A Feedback-Driven Framework Using LLM Judges to Gatekeep and Iteratively Improve Synthetic Benchmarks

Most evaluation pipelines treat LLM judges as scorers or one-shot filters: models generate items, a rubric assigns scores, and low-quality samples are discarded. We take a different path. We position LLM judges as gatekeepers that actively improve synthetic data through a nine-layer, iterative grading and feedback loop. Each candidate prompt--response pair is scored against targeted rubrics (schema conformity; BLUF/CTA quality; MECE structure; numeric/evidence consistency; risk$\to$mitigation$\to$guardrail completeness; factuality; tone/audience fit; novelty/contamination; CTA feasibility). When a layer fails, the judge emits machine-actionable repair instructions; the item is revised or regenerated, re-evaluated, and only admitted after passing all nine layers. Unlike prior paradigms that log evaluations as by-products, we publish schema-based audit traces (per-layer scores, repair histories, judge versions, similarity fingerprints) as first-class benchmark artifacts, enabling contamination checks, reproducibility, and governance. Applied to six structured genres, this closed-loop gatekeeping produces higher-quality synthetic datasets that better align with human raters and yield more stable model deltas than ungated or one-pass filtered baselines. We release rubric prompts, repair templates, audit schemas, and evaluation scripts to support standardized, auditable benchmarking.