arXiv:2605.10302v3 Announce Type: replace Abstract: Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching admits a different control interface: adaptation through examples. For deterministic interpolants, the velocity field is solely governed by a conditional endpoint mean; shifting this mean shifts the flow itself. This yields a simple principle for controllable generation: steer a pretrained model by changing the reference set it follows. We instantiate this idea in two forms. Reference-Mean Guidance
The paper addresses current limitations in controllable AI generation by proposing a novel, simpler method leveraging flow matching, building on recent advances in generative models.
This development could simplify and improve the efficiency of controllable AI generation, making advanced AI models more accessible and adaptable for specific tasks without extensive retraining or complex control mechanisms.
The method shifts control from fine-tuning or auxiliary networks to a more intuitive reference-guided approach, potentially reducing computational overhead and development complexity for customized AI outputs.
- · AI developers
- · Generative AI platforms
- · Creative industries using AI
- · Companies reliant on complex fine-tuning services
- · Legacy controllable generation methods
More flexible and cost-effective AI model deployment for specific applications.
Accelerated development of domain-specific AI agents and content creation tools.
Democratization of advanced AI capabilities, leading to new forms of autonomous content and workflow generation.
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Read at arXiv cs.LG