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Semantic Action Graph: A Shared Representation for Agent Grounding and Human Interpretation of Sports Highlights

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

Generative agents are increasingly used to select and narrate video highlights, but they typically operate over unstructured or frame-level representations. Their output is consequently difficult for a viewer to verify and steer toward individual preferences. We present the semantic action graph, a lightweight domain schema that represents a sports match as performer, action, recipient, moment, and state nodes connected by role, temporal, and outcome edges. The schema demonstrates three key properties: 1) connected event sequences, 2) a shared, closed vocabulary, and 3) frame-addressable momen

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-09-17T17:46:03.000Z
arXiv · Artificial Intelligence · 2026-09-17T17:46:03.000Z

First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.