StoryTR: Narrative-Centric Video Temporal Retrieval with Theory of Mind Reasoning

Current video moment retrieval excels at action-centric tasks but struggles with narrative content. Models can see textitwhat is happening but fail to reason textitwhy it matters. This semantic gap stems from the lack of Theory of Mind (ToM): the cognitive ability to infer implicit intentions, mental states, and narrative causality from surface-level observations. We introduce StoryTR, the first video moment retrieval benchmark requiring ToM reasoning, comprising 8.1k samples from narrative short-form videos (shorts/reels). These videos present an ideal testbed. Their high information density encodes meaning through subtle multimodal cues. For instance, a glance paired with a sigh carries entirely different semantics than the glance alone. Yet multimodal perception alone is insufficient; ToM is required to decode that a character ``smiling'' may actually be ``concealing hostility.'' To teach models this reasoning capability, we propose an Agentic Data Pipeline that generates training data with explicit three-tier ToM chains (intent decoding, narrative reasoning, boundary localization). Experiments reveal the severity of the reasoning gap: Gemini-3.0-Pro achieves only 0.53 Avg IoU on StoryTR. However, our 7B Shorts-Moment model, trained on ToM-guided data, improves +15.1% relative IoU over baselines, demonstrating that textitnarrative reasoning capability matters more than parameter scale.