Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence

• URL: http://arxiv.org/abs/2608.12290v1
• Date: Wed, 12 Aug 2026 17:35:16 GMT
• Title: Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence
• Abstract summary: Black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation.<n>Lack of fine-grained control and reliability presents significant challenges in professional video creation.<n>We introduce the Agentic Self-Improvement" framework, which reframes video into a closed-loop, goal-directed optimization.
• Score: 2.512325213832099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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" framework, which reframes video synthesis into a closed-loop, goal-directed optimization. Our framework systematically navigates the generation parameter space using a novel two-stage approach. In the first stage, an iterative prompt optimization loop uses a multimodal Large Language Model (mLLM) to refine the input prompt. This refinement implements two automated evaluations: Davidsonian Scene Graph (DSG) queries ensure semantic adherence, and Common Mistake Questions (CMQ) for artifact detection. At the second stage, we use Bayesian optimization to efficiently co-optimize stochastic seeds and CFG scales. This search is guided by a suite of quality metrics, including the novel Video-Text Adherence (VTA) score derived from the DSG and CMQ evaluations. Our framework significantly outperforms unguided search methods: in human preference studies, videos generated via our agentic approach were strongly preferred over baseline outputs, achieving win rates up to 69\%. This work provides a practical and extensible methodology for enhancing the predictability and control of state-of-the-art video generation models, moving the field beyond speculative curiosities toward reliable, production-ready tools.

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