心の理論で物語を読み解く！動画の時間検索を革新するStoryTR

Current video moment retrieval excels at action-centric tasks but struggles with narrative content. Models can see textit{what is happening} but fail to reason textit{why it matters}. This semantic gap stems from the lack of textbf{Theory of Mind (ToM)}: the cognitive ability to infer implicit intentions, mental states, and narrative causality from surface-level observations. We introduce textbf{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 textbf{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 textbf{Shorts-Moment} model, trained on ToM-guided data, improves +15.1% relative IoU over baselines, demonstrating that textit{narrative reasoning capability matters more than parameter scale}.