{
  "id": 19023,
  "url": "https://arxiv.org/abs/2608.12290v1",
  "title": "Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence",
  "summary": "Modern black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation, yet their lack of fine-grained control and reliability presents significant challenges in professional workflows. Their inherent stochasticity causes minor variations in textual prompts or hyperparameters to yield drastically different outputs often necessitating inefficient, brute-force trial-and-error processes. To address these limitations, we introduce the ``Agentic Self-Improvement\" frame",
  "authors": "Aman Tyagi, Hemanth Boinpally, Jonathan Chen, Douglas Gebert, Steven Hickson",
  "category": "research",
  "topics": "children-education,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T17:35:16.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19023",
  "original_url": "https://arxiv.org/abs/2608.12290v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}