Arena-T2I Hard: Benchmarking and Improving Faithfulness with Dependency-Aware Checklist Rewards

**Faithfulness**—how precisely a generated image aligns with its prompt—is increasingly central to the real-world utility of text-to-image (T2I) models. Existing faithfulness benchmarks, however, rely on simple atomic instructions, where top-tier systems already achieve near-perfect scores. As T2I models enter creative workflows, users increasingly issue multi-faceted requests involving intricate spatial relationships, stylistic constraints, and complex text rendering. In this setting, a single binary VLM-judge score no longer reveals **which** specific constraints the model fails to satisfy. We introduce **Arena-T2I Hard**, a 310-prompt stress benchmark drawn from real arena T2I logs, with approximately 30 decomposed yes/no constraints per prompt spanning six categories, including text rendering. The strongest closed-source system we evaluate reaches **0.855**, with a **33 pp** performance gap across 11 systems, demonstrating substantial discriminative power. Moreover, high public-arena rankings fail to predict faithfulness, confirming that holistic Bradley–Terry (BT) preference scores prioritize aesthetics over fine-grained prompt adherence. We propose a **dependency-aware checklist reward** that decomposes each prompt into a DAG of yes/no questions and zeroes descendants of failed parents, turning faithfulness into a per-constraint training signal. Combined with a BT aesthetic reward via **group-decoupled normalization (GDPO)**, which standardizes each reward within its rollout group so neither collapses, our recipe attains a strictly better faithfulness–aesthetics trade-off on **SD3.5-Medium** and **FLUX.1-dev** under MMRB2 pairwise comparisons than every single-reward, naive weighted-sum, or 4-reward BT-ensemble baseline. We release **Arena-T2I Hard** as a public stress benchmark.