Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence
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
Record details
Published: 12 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Children & education · Agents & autonomy
Retrieved: 13 August 2026
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ethics.ai (12 August 2026), “Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence,” evidence record 19023, https://ethics.ai/record/19023 (originally published by arXiv cs.AI).
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