DINGO: Constrained Inference for Diffusion LLMs

Diffusion LLMs are a promising, efficient alternative to autoregressive models but struggle to enforce formal constraints like regular expressions, limiting their reliability for structured output tasks. To address this, the authors propose DINGO, a dynamic programming-based decoding method that efficiently and provably preserves the model's distribution while strictly satisfying user-defined constraints, significantly improving constrained generation performance.