Adaptive Window Diffusion Decoding for Long-Context Language Generation Under Memory Constraints

Abstract

Masked diffusion language models (MDLMs) are attractive for long-form generation because they denoise all positions with bidirectional context, but this advantage also creates a severe inference-time memory bottleneck: each denoising step requires dense attention over the active sequence. Recent sliding-window and speculative wrappers have shown that the bottleneck can be reduced without retraining the base model. However, fixed window sizes remain inefficient under heterogeneous documents, where some regions require broad context and others can be decoded safely with smaller memory footprints. This paper proposes Adaptive Window Diffusion Decoding (AWDD), a memory-budgeted inference framework that adjusts window size, overlap commitment, and context-summary capacity according to observable uncertainty in the partially masked sequence. AWDD uses a lightweight dependency-pressure score, a bounded summary buffer, and confidence-aware overlap commitment to preserve long-range terms while keeping peak memory below a fixed budget. We derive the memory and computational bounds of the scheduler and provide a reproducible CPU benchmark rather than unsupported large-model claims. On a controlled long-range dependency benchmark and a real-text sanity set extracted from the uploaded SW-SpeedDLM article, AWDD improves long-range entity reconstruction over fixed-window baselines while using substantially less peak memory than a large fixed window. These results support adaptive windowing as a practical direction for memory-constrained diffusion decoding and provide code, raw results, and document-level figures for replication.