arXiv:2602.12468v2 Announce Type: replace Abstract: Diffusion language models offer a promising alternative to autoregressive models due to their global, non-causal generation process, but their continuous latent dynamics make discrete constraints -- e.g., the output should be a JSON file that matches a given schema -- difficult to impose. We introduce a training-free guidance method for steering continuous diffusion language models to satisfy formal syntactic constraints expressed using regular expressions. Our approach constructs an analytic score estimating the probability that a latent sta
The rapid advancement of generative AI, particularly diffusion models, is creating an urgent need to control and constrain their outputs for reliability and safety.
This breakthrough addresses a significant limitation of diffusion models, enabling them to generate highly structured and formally correct outputs, critical for enterprise adoption and agentic systems.
Diffusion language models can now be reliably steered to produce outputs that conform to strict syntactic rules, such as JSON schemas, without demanding costly retraining.
- · AI developers
- · Enterprises adopting AI
- · AI agents
- · Data validation services
- · Systems relying on unstructured AI outputs
- · Generative AI models without robust control mechanisms
Increased trustworthiness and utility of diffusion-based language models in applications requiring precise data formats.
Accelerated development of AI agents that can interact with formal systems and APIs more reliably.
Potential for new classes of AI-generated content that seamlessly integrate into highly structured IT environments and workflows, blurring lines between human and AI-generated data.
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Read at arXiv cs.LG