Feasibility-Constrained Projection for Discrete Diffusion Models
nandofioretto · x · 2026-07-11
- **Background**: Discrete diffusion models generate data by iteratively unmasking tokens, but the native process cannot guarantee the physical or logical feasibility of the results (e.g., generating invalid molecules or broken schedules). - **Solution**: The paper proposes projecting the results onto a constraint set at each unmasking step, directly ensuring the feasibility of the generated outputs at a structural level. - **More Info**: This work includes a related NeurIPS paper and subsequent extended research.
Related event: Projection Method Ensures Validity in Discrete Diffusion Models(2 posts)→
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