Agentic Visual Generation: From Generative Models to Agentic Control
Researchers propose an L0-L4 control taxonomy for agentic visual generation, classifying controllers from fixed conditioning to experience-adaptive decision-making.
This paper proposes a taxonomy for agentic visual generation organized by what the controller can directly control in the generation process, rather than by planning depth, tool count, or model size. Levels range from L1 Conditioning Control through L2 Execution Control, L3 Outcome-Adaptive Control, and L4 Experience-Adaptive Control, with L0 Fixed Support denoting systems without a deployed decision-making controller. The framework is applied across image, video, editing, 3D, world, slide, and user-interface generation to map how controller capabilities and mechanisms have evolved across the field.
- Defines L1-L4 levels based on controller decision scope, from conditioning to experience-adaptive control
- L0 Fixed Support covers generators, evaluators, and pipelines without a deployed generation-deciding controller
- Framework applied across image, video, editing, 3D, world, slide, and UI generation systems
From the original article
1 min read at huggingface.co
Visual generation is evolving from generative models used through a single invocation into agentic control processes that can plan, select tools, inspect intermediate synthesized outputs, revise failures, and reuse prior experience. In most existing systems, the controller is an LLM or VLM, while visual generation models serve as tools or executors.
By Yinming Huang, Shuyuan Tu, Xi Yan · huggingface.co
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