Policy compliance in enterprise conversational AI is not binary: the same response can satisfy one stakeholder’s policy while violating another’s. Existing evaluations largely treat compliance as a single-policy problem, which makes cross-role conflict cases difficult to construct and assess. GRACE (Generating Conflict-Aware Cases for Evaluating Policy Compliance) addresses this gap by generating matched normal and conflict cases from the same underlying scenario, assigning role-wise expected verdicts through independent per-role policy evaluation, and scoring responses independently for each role rather than collapsing compliance into a single binary label. Across four domains, this process yields 1,109 labeled scenarios, including 309 cross-role conflicts. Evaluating five LLMs shows that deny cases are easiest, allow cases are depressed by over-refusal, and conflict cases are consistently hardest, revealing failures that single-label evaluation obscures. Per-persona analysis further shows that these errors are localized to specific domain-role combinations rather than uniform across models. GRACE therefore supports both policy review and deployment-time model selection.

Automatic dubbing (AD) aims to replace the original speech in a video with translated speech that maintains precise temporal alignment (isochrony). Achieving natural synchronization between dubbed speech and visual content remains challenging due to variations in speech durations across languages. To address this, we propose an end-to-end AD framework that leverages large language models (LLMs) to integrate translation and timing control seamlessly. At the core of our framework lies Duration-based Translation (DT), a method that dynamically predicts the optimal phoneme count based on source speech duration and iteratively adjusts the translation length accordingly. Our experiments on English, Spanish, and Korean language pairs demonstrate that our approach substantially improves speech overlap—achieving up to 24% relative gains compared to translations without explicit length constraints—while maintaining competitive translation quality measured by COMET scores. Furthermore, our framework does not require language-specific tuning, ensuring practicality for multilingual dubbing scenarios.