Constrained Decoding for Diffusion Language Models via Efficient Inference over Finite Automata
arXiv:2607.07026v1 Announce Type: new Abstract: Constrained decoding is essential for serving LLMs, ensuring that generated outputs follow specific structures such as JSON schema-formatted function calls. Existing systems are designed for autoregressive models and assume left-to-right generation, masking out invalid next tokens at each step. Diffusion language models, however, break this assumption: they sample multiple positions simultaneously from a fully-factorized mean-field distribution at each denoising step. In this paper, we present an exact and tractable algorithm for sampling from th
The paper addresses a critical limitation of diffusion language models (DLMs) — their inability to handle constrained decoding — at a time when DLMs are gaining traction for generation tasks.
This development could significantly broaden the applicability of diffusion language models, enabling them to generate structured outputs vital for agentic systems and reliable programming interfaces.
Diffusion language models can now reliably generate outputs that conform to specific formats like JSON schemas, making them more practical for integration into sophisticated applications.
- · AI model developers
- · Developers leveraging LLMs for structured tasks
- · AI agents
- · Companies reliant solely on autoregressive models for structured generation
Diffusion models become a more viable alternative or complement to autoregressive models for various constrained generation tasks.
This improved capability will likely accelerate the development and deployment of truly autonomous AI agents that require precise, structured outputs for interaction.
The integration of DLMs into agentic systems could lead to a new generation of more robust and reliable AI-driven automation across many industries.
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Read at arXiv cs.LG