Netflix (United States)

Large language models write stories that often lack the rising and falling reader experience that makes narrative engaging. A new framework first learns how reader responses such as suspense or humor evolve in high-quality human-written stories, then uses a small model to predict the response the next segment should elicit and steers an off-the-shelf writer model with that prediction. Across suspense and humor, with GPT-5.5 and Qwen3.5-27B as writers, generated stories improve trajectory-based fidelity to human-written stories by 12%–35% while largely preserving story coherence.

Large language models write stories that often lack the rising and falling reader experience that makes narrative engaging. A new framework first learns how reader responses such as suspense or humor evolve in high-quality human-written stories, then uses a small model to predict the response the next segment should elicit and steers an off-the-shelf writer model with that prediction. Across suspense and humor, with GPT-5.5 and Qwen3.5-27B as writers, generated stories improve trajectory-based fidelity to human-written stories by 12%–35% while largely preserving story coherence.

The work introduces Align Then Reason (ATR), in which a monotonic CTC alignment scorer between candidate-text phonetic units and frame-level lip representations supplies per-unit soft tokens and a calibrated global alignment score to an LLM reasoner that outputs a Yes/No judgment with a short explanation, raising Qwen3.5-9B mean AUC from 0.610 to 0.920 on a seven-language benchmark and improving on three unseen MuAViC languages and two real dubbing downstream tasks.

The work introduces Align Then Reason (ATR), in which a monotonic CTC alignment scorer between candidate-text phonetic units and frame-level lip representations supplies per-unit soft tokens and a calibrated global alignment score to an LLM reasoner that outputs a Yes/No judgment with a short explanation, raising Qwen3.5-9B mean AUC from 0.610 to 0.920 on a seven-language benchmark and improving on three unseen MuAViC languages and two real dubbing downstream tasks.