Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models
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Computer Science > Computation and Language
Title:Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models
Abstract:Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally support format-constrained tasks like parseable JSON or reasoning templates. While straightforward fixed anchors can enforce such constraints, they often impose rigid spans, leading to truncated reasoning or redundant content. To overcome this, we propose Dynamic Infilling Anchors (DIA), a training-free method that dynamically estimates end-anchor positions to adjust generation length before iterative infilling. This flexible mechanism ensures structural correctness and semantic coherence, avoiding the inefficiencies of fixed-span methods. Experiments on reasoning benchmarks demonstrate that DIA substantially improves format compliance and answer accuracy, achieving significant zero-shot gains on GSM8K and MATH. These results establish DIA as a robust pathway toward reliable, structure-aware generation.
| Comments: | Accepted to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.04535 [cs.CL] |
| (or arXiv:2606.04535v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.04535
arXiv-issued DOI via DataCite (pending registration)
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