Uncertainty-Aware Speculative Decoding for Diffusion Language Models (UASD-DLM), a complementary inference scheduler for long document generation that improves the speed-memory trade-off over fixed-depth speculative decoding and recovers technical-term accuracy lost by summary-free windowing.
Abstract
Masked diffusion language models (MDLMs) are attractive for long-form generation because they denoise many positions under bidirectional context rather than committing tokens strictly from left to right. The same property makes inference expensive: every reverse step may require dense attention over a long partially masked sequence. Recent sliding-window and speculative wrappers demonstrate that pretrained MDLMs can be accelerated without changing their weights, especially the SW-SpeedDLM framework, which combines segmented sliding-window denoising, cross-segment summary compression, and window-level speculative acceptance. This paper proposes Uncertainty-Aware Speculative Decoding for Diffusion Language Models (UASD-DLM), a complementary inference scheduler for long document generation. Instead of using a fixed speculation depth for every window, UASD-DLM estimates local predictive uncertainty from entropy, margin, and summary-conflict signals, then chooses both the draft depth and context refresh policy dynamically. Low-risk windows receive longer drafts, while high-risk windows are verified earlier to avoid wasted drafting and boundary errors. The acceptance step retains the same rejection-sampling correction as standard speculative decoding, so changing the draft length does not alter the verifier distribution. To avoid unsupported large-model claims, we provide a reproducible CPU proxy benchmark using real text extracted from the uploaded SW-SpeedDLM article and a seeded long-range dependency corpus. In this controlled setting, UASD-DLM improves the speed-memory trade-off over fixed-depth speculative decoding and recovers technical-term accuracy lost by summary-free windowing. The complete code, raw results, and Word manuscript accompany this submission.
Adaptive Window Diffusion Decoding (AWDD) is proposed, a memory-budgeted inference framework that adjusts window size, overlap commitment, and context-summary capacity according to observable uncertainty in the partially masked sequence to improve long-range entity reconstruction over fixed-window baselines.
N. Petrov, Rafael Costa, Clara Hoffmann· International Journal of Adv...· 0 citations
This work develops a trajectory-level speculative framework that constructs draft denoising trajectories via confidence-stratified tree exploration and verifies them through blockwise parallel evaluation with bidirectional attention masking, and introduces inter-block speculation, exploiting diffusion models'bidirectional structure to perform cross-block lookahead.
Tian-Xiang Pan, Baitao Gong, Mo Guang et al.· 0 citations
DARD is proposed, a training-free framework that separates tokens into masked, candidate, and unmasked states and adaptively regulates their influence on subsequent decoding, and consistently improves the speed-quality Pareto frontier over recent revocable decoding methods.
Woo-Soon Park, Insu Lee, Minyoung Noh et al.· 1 citation
Masked diffusion language models predict tokens from a partially observed response canvas, enabling bidirectional conditioning and parallel token refinement. Yet standard masked-diffusion decoders use a rigid inference interface: the number of masked positions allocated to the answer is fixed before generation begins. Choosing this length is difficult. A short canvas can truncate reasoning or code, while a long canvas wastes computation and can perturb denoising. We introduce CARVE (Counterfactual-Aware Reveal with Verified Expansion), a training-free variable-length algorithm for masked diffusion LMs. Starting from a shorter canvas, CARVE can grow the response during decoding by inserting additional [MASK] positions. Rather than keeping every insertion, CARVE tests a candidate expanded canvas and asks a counterfactual question: would the model make similar predictions for the unresolved positions in the original canvas if the extra masked space were present? The inserted masks are kept only when they induce low Jensen-Shannon (JS) divergence on aligned unresolved positions. This makes length growth a verified stability decision rather than a pure confidence heuristic. CARVE applies without retraining to both full-canvas and blockwise diffusion decoders. Across code generation and mathematical reasoning benchmarks, CARVE consistently improves average performance over fixed-length baselines across all evaluated model families. Crucially, CARVE achieves these accuracy gains while reducing inference cost, reaching half the FLOPs of fixed-length decoding in some settings.
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HCC directly realizes the hierarchical-compression direction identified as future work by recent segmented-decoding methods, and is validated with controlled and real-text information-retention studies and with direct latency and memory measurements.
Maya Srinivasan, Lucas Bernal· International journal of inf...· 0 citations
Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternative to autoregressive decoding. Recent lookahead-based decoding methods improve the accuracy--efficiency trade-off by exploring future decoding states before committing token updates. However, existing approaches mainly rely on shallow one-step lookahead, which optimizes immediate information gain but can be suboptimal for longer-horizon decoding trajectories. Meanwhile, we find that a naive extension for deeper lookahead is also ineffective, as fixed-depth rollout introduces additional computation and cannot adapt to heterogeneous intermediate decoding states. Thus, in this work, we propose AdaLook, an adaptive lookahead framework for DLM decoding. AdaLook dynamically determines whether to continue rollout based on candidate-score variance and further enables branch expansion when intermediate rollout states require additional exploration. This design avoids unnecessary deep rollout while allowing the decoder to re-trigger lookahead from informative intermediate states. Experiments on various benchmarks and models demonstrate that AdaLook achieves a better accuracy--decoding steps trade-off than existing one-step lookahead decoding methods.
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