---
format: "aidr-story-markdown/v1"
id: "6f5139196ce92d6d16d8a0dbbb1026f9e60250a5281b61589e9189f8399b05fe"
canonical_url: "https://aidr.today/6f513919?lang=en"
title: "DramaGrow: A Regime-Aware Benchmark for Auditing LLM Judges of Multi-Episode Drama"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-11-10T05:00:00.000Z"
category: "Data"
topics: ["llm","data-engineering"]
source_urls: ["https://arxiv.org/abs/2611.09938"]
summary: "DramaGrow is a regime-aware benchmark and audit protocol for AI-generated Chinese five-episode drama. The benchmark assigns verifiable constraint violations, four-of-five preferences, and within-panel disagreements to separate denominators and evidence rules. It contains 800 episodes, 120 byte-preserving interventions, and 1,155 blinded five-rater records."
---

# DramaGrow: A Regime\-Aware Benchmark for Auditing LLM Judges of Multi\-Episode Drama

> [Open the canonical story](<https://aidr.today/6f513919?lang=en>)

**Published:** 2026-11-10T05:00:00.000Z
**Category:** Data
**Topics:** llm, data\-engineering

## Summary

DramaGrow is a regime\-aware benchmark and audit protocol for AI\-generated Chinese five\-episode drama\. The benchmark assigns verifiable constraint violations, four\-of\-five preferences, and within\-panel disagreements to separate denominators and evidence rules\. It contains 800 episodes, 120 byte\-preserving interventions, and 1,155 blinded five\-rater records\.

## Sources

- [Story source](<https://arxiv.org/abs/2611.09938>)