{
  "id": 8161606,
  "title": "Semantic Action Graph: A Shared Representation for Agent Grounding and Human Interpretation of Sports Highlights",
  "url": "https://urgent.news/2026/09/17/semantic-action-graph-a-shared-representation-for-agent-grounding-and",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-17T17:46:03.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.20768v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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,…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}