{
  "id": 1151,
  "url": "https://arxiv.org/abs/2606.12217v1",
  "title": "Making Foresight Actionable: Repurposing Representation Alignment in World Action Models",
  "summary": "World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions. However, our empirical observations reveal a phenomenon: generating plausible visual futures does not always guarantee the extraction of accurate actions. To diagnose this failure, we conduct action-head attention analysis and causal interventions. We find that the action decoder fails to focus on task-relevant interaction re",
  "authors": "Lu Qiu, Yizhuo Li, Yi Chen, Yuying Ge, Yixiao Ge, Xihui Liu",
  "category": "research",
  "topics": "safety-alignment,healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-10T15:31:25.000Z",
  "fetched_at": "2026-07-14T14:15:03.615Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1151",
  "original_url": "https://arxiv.org/abs/2606.12217v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}