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Diffusion-based language models can run up to 162× faster without training when the response length is predicted adaptively from the model's own [EOS] confidence and decoded-token KV pairs are shared across speculative blocks.

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2026-08-04 06:43 UTC pith:O3V2VCT7

load-bearing objection Useful dLLM serving result; adaptive length prediction plus jump-share speculation is a real combination, but the largest speedup component depends on an unreported threshold that needs sensitivity testing. the 4 major comments →

arxiv 2511.21759 v2 pith:O3V2VCT7 submitted 2025-11-24 cs.CL cs.LG

Orchestrating Dual-Boundaries: An Arithmetic Intensity Inspired Acceleration Framework for Diffusion Language Models

Linye Wei , Wenjue Chen , Pingzhi Tang , Xiaotian Guo , Le Ye , Runsheng Wang , Meng Li This is my paper
classification cs.CL cs.LG
keywords diffusion language modelsinference accelerationadaptive length predictionspeculative decodingKV cache reusebidirectional attentionarithmetic intensitytraining-free framework
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper claims that the two bottlenecks of diffusion-LLM inference—the compute-bound prefill that rebuilds the full response context and the memory-bound block decoding that repeatedly unmasks tokens—can be attacked separately and both be reduced, giving end-to-end speedups of 46–162× over the vanilla model and 2.63–6.30× over the prior Fast-dLLM accelerator. The first mechanism, Adaptive Length Prediction, reads the [EOS] tokens the model itself emits during a full-sequence prefill pass and, when their confidence exceeds a threshold, progressively truncates the response length, eliminating the redundant computation of the default 1024-token allocation. The second, Jump-Share Speculative Decoding, treats below-threshold but high-confidence token candidates as speculative branches, verifies them in parallel, and then reuses the KV pairs of already-decoded tokens across those branches, cutting the number of decoding iterations while staying memory-bound. The framework is training-free and, on five benchmarks, recovers or even exceeds the vanilla model's accuracy, reversing the accuracy drop that earlier KV-caching frameworks introduced. A sympathetic reader would take the main claim to be: the compute and memory boundaries of dLLM inference can be orchestrated separately, and doing so yields order-of-magnitude wall-clock gains.

Core claim

The central claim is that a masked-diffusion language model can be accelerated by explicitly separating its two inference boundaries: (1) during prefill, the model's own [EOS] predictions encode its internal estimate of the correct answer length, and this signal can be used, over successive cache refreshes, to shrink the response length from the fixed default, removing heavy redundant computation; and (2) during decoding, the confidence threshold is not a hard stop: tokens just below it are verifiable candidates, and because a decoded token's KV pair depends only on the already-fixed context, those KV pairs can be shared among several speculative branches whose unmasked sets differ. Combinin

What carries the argument

The paper's machinery consists of two complementary mechanisms joined by an arithmetic-intensity analysis. Adaptive Length Prediction (ALP) exploits the full-sequence prefill forward pass: it scans the unmasked response for [EOS] tokens, reads their confidence, and truncates the response length whenever that confidence exceeds a threshold, progressively lowering the prefill cost of every subsequent cache refresh. Jump-Share Speculative Decoding (JSD) appends speculative blocks to the main decoding block, each representing a different subset of below-threshold token candidates, verifies them in an accept-jump cascade (jumping to the most-verified branch), and then, once enough tokens are deco

Load-bearing premise

The framework assumes that the key-value pairs of already-decoded tokens remain valid when reused inside speculative blocks that unmask different token sets, even though bidirectional attention means each token's representation is altered by which other tokens are visible to it.

What would settle it

Take one benchmark (e.g., GSM8K) and run ODB-dLLM with decoded-share speculation disabled except for one probe: for the same decoding step, compare the accept/reject decision and the top-1 logits of a speculative block that uses shared KV from the main block against a speculative block that recomputes all KV from scratch. If the decisions diverge materially (say, more than ~5% of tokens change acceptance), then the speedup is being bought by altering model outputs, and the accuracy comparison versus Fast-dLLM must be re-measured under exact recomputation. A complementary check: craft prompts t

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • On LLaDA-family masked-diffusion models, the reported end-to-end speedups (46–182× vs vanilla, 2.60–7.22× vs Fast-dLLM) become available with zero training, so any user of these open models can adopt the framework immediately.
  • Because the framework recovers or exceeds vanilla accuracy on most of the five benchmarks, it implies that parallel decoding with approximate KV caching need not trade away correctness—a direct challenge to the assumption that acceleration frameworks necessarily hurt quality.
  • Adaptive Length Prediction offers a training-free alternative to methods that fine-tune the [EOS] token or expand response lengths heuristically, and it can be dropped into any block-cached dLLM inference pipeline.
  • Jump-share speculation's reuse of information from below-threshold 'rejected' tokens is a general principle that should transfer to other block-wise diffusion decoders, not only the two models evaluated here.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The central approximation—reusing decoded-token KV pairs across speculative blocks with different unmasked-token sets—is not exact under bidirectional attention; the reported speedups will hold only to the extent that this KV sharing preserves the model's token-acceptance decisions. A quick empirical check of that faithfulness is the most direct way to probe the framework's limits.
  • The speedup multipliers are measured on a single A100 without batched serving and will shift on hardware with different compute/memory ratios: on memory-heavy devices the speculative sharing should help more, on compute-heavy devices the prefill trimming matters more, so the 2.63–6.30× versus Fast-dLLM range is hardware-specific.
  • The framework does not address the cost of the very first prefill pass (which still computes the full fixed length before the first truncation), so on short-answer tasks the first-step overhead remains; a batched or streaming variant might amortize it further.
  • The accuracy gains from speculative decoding are attributed to 'effective NFE'—jumping across states rather than sampling them independently—which suggests the same strategy could be adapted to improve quality in other non-autoregressive generators, not just speed.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes ODB-dLLM, a training-free acceleration framework for diffusion-based large language models (dLLMs). The framework targets the two phases of Fast-dLLM-style inference: (i) an adaptive length prediction (ALP) mechanism that truncates the pre-defined response length during prefill based on [EOS] confidence, and (ii) a 'jump-share' speculative decoding scheme that combines accept-jump and decoded-share strategies to increase tokens accepted per decoding iteration. On LLaDA-Instruct and LLaDA-1.5, the authors report 46–182× speedups over vanilla dLLMs and 2.60–7.22× speedups over Fast-dLLM, while also partially recovering the accuracy lost by Fast-dLLM. An ablation attributes roughly 2.84× of the 4.19× average speedup over Fast-dLLM to ALP and the remainder to the speculative decoding component.

Significance. If the empirical claims hold, the paper makes a practical contribution: it demonstrates that a large portion of the computational waste in dLLM inference comes from over-long fixed outputs, and that careful orchestration of the prefill/decode boundary can yield substantial wall-clock speedups without a large accuracy penalty. The work also highlights a genuinely non-trivial issue—KV reuse under bidirectional attention—and proposes a heuristic that appears to work in the reported settings. However, the strength of the central claim depends on the robustness of the threshold-based ALP and the validity of the decoded-share KV approximation, neither of which is currently established. The absence of any statistical analysis, together with an invented metric (Eff_NFE) used to rationalize accuracy behavior, leaves the result in a 'promising but unverified' state.

major comments (4)
  1. [Sec. 4.1, Table 4, Fig. 10] The ALP 'truncate threshold' is never reported, and no sensitivity analysis or error bars are given. The ablation attributes roughly 2.84× of the 4.19× speedup over Fast-dLLM to ALP, which reduces the average response length from 1024 to 272 tokens. This makes the core speedup claim highly dependent on an unvalidated threshold. If the threshold is aggressive, the speedup may reflect truncated answers; if conservative, the speedup largely disappears. The paper must report the threshold value for each benchmark, a sensitivity sweep (e.g., threshold vs. accuracy/speed), and multiple seeds with variance.
  2. [Sec. 4.2.2, Fig. 8] The decoded-share speculative strategy reuses KV pairs of already-decoded tokens across speculative blocks whose unmasked-token sets differ. Because dLLM attention is bidirectional, a token's representation is context-dependent; the same token placed in different speculative blocks has different attention contexts. The paper states this as a 'key insight' but provides no validation that the approximation preserves distributional fidelity. A concrete test is needed: compare the shared-KV decoding outputs against full-recompute outputs on a held-out set, quantifying agreement rate and downstream accuracy. Without this, the speedup and accuracy numbers for the full ODB-dLLM cannot be cleanly attributed.
  3. [Sec. 5.2, Tables 2 and 3] All accuracy values are reported as point estimates without standard deviations, confidence intervals, or significance tests. Many differences between Fast-dLLM and ODB-dLLM are 0.5–2.0 accuracy points (e.g., GSM8K 77.79→79.00, BBH 53.20→55.23). Given typical evaluation noise on these benchmarks, the claim that ODB-dLLM 'mitigates accuracy degradation' is not statistically supported. The authors should report the number of evaluation runs or seeds, standard errors, and ideally a paired significance test across the benchmark items.
  4. [Sec. 5.3, Fig. 11b] The Eff_NFE metric is introduced post hoc to explain why speculative decoding can improve accuracy, but its definition is informal ('measures the number of effective state changes in the unmasking sequence'). No formula is given, and it is not clear how it is computed from an actual decoding trajectory. Because this metric is used to justify the accuracy behavior of the different speculative variants, it must be precisely defined and, ideally, validated by showing it correlates with output quality on a separate set of runs. As written, the reader cannot reproduce Eff_NFE or falsify the claim that a higher Eff_NFE improves accuracy.
minor comments (5)
  1. [Sec. 2.1] Equation (1) uses notation without defining q0|t or the conditional distribution over unmasked tokens; a short explanation would improve readability.
  2. [Figure 3 / Table 4] The text says the default response length is 1024, while Table 4 lists 'Vanilla/Fast-dLLM' lengths of 2031, 1709, etc. It would help to state clearly whether the table numbers include the prompt length or are per-block lengths, to avoid confusion.
  3. [Sec. 5.2] The speedup values in Tables 2 and 3 are reported as 'Speed 1.00/–' for vanilla, but the denominator is not defined anywhere; state explicitly that the numbers after the slash are relative to Fast-dLLM.
  4. [Sec. 5.3] The claim that 'tokens unmasked within the same step are sampled independently' needs a citation or a proof in the context of τ-leaping; it is not obvious from Eq. (1), which defines the reverse transition per-token but says nothing about joint sampling.
  5. [References] Several arXiv preprint references are dated 2025; ensure the final version includes publication venues where available (e.g., [8], [10], [12]).

Circularity Check

0 steps flagged

No significant circularity: ODB-dLLM's speedups are empirical measurements and its explanatory metrics are post hoc, not result-defining fits.

full rationale

ODB-dLLM is an empirical systems paper. The headline speedups are wall-clock measurements on an NVIDIA A100 against vanilla dLLMs and Fast-dLLM, and the accuracy numbers are benchmark scores; neither is derived from a fitted parameter. Adaptive Length Prediction (Sec. 4.1) is a heuristic stopping rule: it truncates when a prefill [EOS] token exceeds a threshold. The threshold is a hyperparameter, not a parameter fitted to benchmark answers, and the reduction in response length is the mechanism's explicit effect rather than a hidden regression. Jump-share speculative decoding (Sec. 4.2) is an algorithmic procedure evaluated by ablation; its speedup is measured end-to-end. The Eff_NFE metric (Sec. 5.3) is introduced post hoc to interpret accuracy differences and is not used to generate the reported speedups or to define the method's output. The only self-citation (ref. [38], LLaDA-MoE, which lists L. Wei among its authors) supports a background statement about bidirectional attention preventing standard KV caching; it is not load-bearing and does not enter the speedup or accuracy claims. The unreported truncate threshold and missing sensitivity analysis are robustness concerns, not evidence of circularity: no prediction in the paper reduces by construction to its inputs, and no derivation depends on a self-citation chain. Score 0 reflects the absence of circular steps rather than a judgment on tuning robustness.

Axiom & Free-Parameter Ledger

3 free parameters · 3 axioms · 1 invented entities

The framework rests on two practical assumptions: EOS confidence as a length oracle, and KV reuse across similar speculative blocks. Both are plausible engineering heuristics but are neither proved nor open-sourced with detailed thresholds. The only invented entity is the post-hoc Eff_NFE metric.

free parameters (3)
  • truncate_threshold = not reported
    Confidence threshold used to decide whether an [EOS] token in prefill output truncates the response length (Sec. 4.1). Directly controls speed-accuracy trade-off.
  • unmask_acceptance_threshold = not reported
    Confidence threshold used to accept unmasked tokens during decoding; inherited from Fast-dLLM but still a hand-set parameter affecting speed and accuracy.
  • num_spec_candidates = 4
    Number of extra candidate tokens in jump-share speculative decoding is fixed to four (Sec. 4.2.2) by design; affects arithmetic intensity and acceptance rate.
axioms (3)
  • domain assumption [EOS] positions in prefill output encode the model's intrinsic assessment of required response length (Sec. 4.1)
    The method relies on the prefill pass's EOS confidence being a reliable early indicator of correct response length. This is plausible but unproven.
  • ad hoc to paper KV of decoded tokens can be reused across speculative blocks with different unmasked-token sets (Sec. 4.2.2, Fig. 8)
    Introduced specifically to make the speculation cheap. Because dLLM attention is bidirectional, this is an approximation requiring validation.
  • domain assumption Confidence-threshold-based parallel unmasking with fast acceptance needs top-k supervision to preserve accuracy (Sec. 4.2)
    The design assumes a fixed number of below-threshold candidates can be verified with a few extra blocks without changing the final output too much.
invented entities (1)
  • Eff_NFE (Effective NFE) no independent evidence
    purpose: Metric introduced to count effective state changes in the unmasking sequence and explain why speculative variants change accuracy (Sec. 5.3)
    Defined and used only in this paper; no external validation or benchmark.

pith-pipeline@v1.3.0-alltime-deepseek · 11175 in / 9338 out tokens · 88836 ms · 2026-08-04T06:43:27.393061+00:00 · methodology

0 comments
read the original abstract

Diffusion-based large language models (dLLMs) have recently gained significant attention for their exceptional performance and inherent potential for parallel decoding. Existing frameworks further enhance its inference efficiency by enabling KV caching. However, its bidirectional attention mechanism necessitates periodic cache refreshes that interleave prefill and decoding phases, both contributing substantial inference cost and constraining achievable speedup. Inspired by the heterogeneous arithmetic intensity of the prefill and decoding phases, we propose ODB-dLLM, a framework that orchestrates dual-boundaries to accelerate dLLM inference. In the prefill phase, we find that the predefined fixed response length introduces heavy yet redundant computational overhead, which affects efficiency. To alleviate this, ODB-dLLM incorporates an adaptive length prediction mechanism that progressively reduces prefill overhead and unnecessary computation. In the decoding phase, we analyze the computational characteristics of dLLMs and propose a dLLM-specific jump-share speculative decoding method to enhance efficiency by reducing the number of decoding iterations. Experimental results demonstrate that ODB-dLLM achieves 46-162x and 2.63-6.30x speedups over the baseline dLLM and Fast-dLLM, respectively, while simultaneously mitigating the accuracy degradation in existing acceleration frameworks.

Figures

Figures reproduced from arXiv: 2511.21759 by Le Ye, Linye Wei, Meng Li, Pingzhi Tang, Runsheng Wang, Wenjue Chen, Xiaotian Guo.

Figure 1
Figure 1. Figure 1: Inference with ODB-dLLM on GSM8K dataset [ [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Inference with parallel decoding and DualCache. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Dual-boundary challenges for dLLM and the overview of ODB-dLLM’s design. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Proportion of step counts and execution time across prefill and decoding phases. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: (a) Distribution of the effective response lengths and (b) Analysis of next-step acceptance [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Adaptive length prediction strategy. 4.2 Jump-share Speculative Decoding To further enhance inference efficiency, we integrate speculative sampling into the memory-bound parallel decoding phase to increase the token acceptance rate. Specifically, among the tokens whose confidence scores fall below the predefined acceptance threshold, we select the top-k tokens as speculative candidates. Due to the bidirect… view at source ↗
Figure 7
Figure 7. Figure 7: Inference process of (a) traditional dLLM speculative and (b) accept-jump speculative [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Position IDs and attention mask with decoded-share speculative strategy. For clarity of [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of ODB-dLLM perfor￾mance against prior works [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of (a) relative NFE proportion and speedup and (b) relative effective NFE [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗

discussion (0)

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Forward citations

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Reference graph

Works this paper leans on

38 extracted references · 23 linked inside Pith · cited by 3 Pith papers

  1. [1]

    Agrawal, R

    S. Agrawal, R. Garrepalli, R. Goel, M. Lee, C. Lott, and F. Porikli. Spiffy: Multiplying diffusion llm acceleration via lossless speculative decoding.arXiv preprint arXiv:2509.18085, 2025. 3, 6, 7

    Pith/arXiv arXiv 2025

  2. [2]

    Austin, A

    J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. Cai, M. Terry, Q. Le, et al. Program synthesis with large language models.arXiv preprint arXiv:2108.07732, 2021. 8

    Pith/arXiv arXiv 2021

  3. [3]

    Ben-Hamu, I

    H. Ben-Hamu, I. Gat, D. Severo, N. Nolte, and B. Karrer. Accelerated sampling from masked diffusion models via entropy bounded unmasking.arXiv preprint arXiv:2505.24857, 2025. 1

    Pith/arXiv arXiv 2025

  4. [4]

    a is b" fail to learn

    L. Berglund, M. Tong, M. Kaufmann, M. Balesni, A. C. Stickland, T. Korbak, and O. Evans. The reversal curse: Llms trained on" a is b" fail to learn" b is a".arXiv preprint arXiv:2309.12288, 2023. 1

    Pith/arXiv arXiv 2023

  5. [5]

    T. Cai, Y . Li, Z. Geng, H. Peng, J. D. Lee, D. Chen, and T. Dao. Medusa: Simple llm inference acceleration framework with multiple decoding heads.arXiv preprint arXiv:2401.10774, 2024. 3

    Pith/arXiv arXiv 2024

  6. [6]

    C. Chen, S. Borgeaud, G. Irving, J.-B. Lespiau, L. Sifre, and J. Jumper. Accelerating large language model decoding with speculative sampling.arXiv preprint arXiv:2302.01318, 2023. 3, 8

    Pith/arXiv arXiv 2023

  7. [7]

    X. Chen, S. Huang, C. Guo, C. Wei, Y . He, J. Zhang, H. Li, Y . Chen, et al. Dpad: Efficient diffusion language models with suffix dropout.arXiv preprint arXiv:2508.14148, 2025. 1, 8

    Pith/arXiv arXiv 2025

  8. [8]

    Cobbe, V

    K. Cobbe, V . Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, et al. Training verifiers to solve math word problems.arXiv preprint arXiv:2110.14168, 2021. 2, 8

    Pith/arXiv arXiv 2021

  9. [9]

    Y . Dong, C. F. Ruan, Y . Cai, R. Lai, Z. Xu, Y . Zhao, and T. Chen. Xgrammar: Flexible and efficient structured generation engine for large language models.arXiv preprint arXiv:2411.15100, 2024. 1

    Pith/arXiv arXiv 2024

  10. [10]

    Y . Gao, Z. Ji, Y . Wang, B. Qi, H. Xu, and L. Zhang. Self speculative decoding for diffusion large language models.arXiv preprint arXiv:2510.04147, 2025. 3, 8

    arXiv 2025

  11. [11]

    D. T. Gillespie. Approximate accelerated stochastic simulation of chemically reacting systems.The Journal of chemical physics, 115(4):1716–1733, 2001. 2

    2001

  12. [12]

    Grattafiori, A

    A. Grattafiori, A. Dubey, A. Jauhri, A. Pandey, A. Kadian, A. Al-Dahle, A. Letman, A. Mathur, A. Schelten, A. Vaughan, et al. The llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2024. 1

    Pith/arXiv arXiv 2024

  13. [13]

    Hendrycks, C

    D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt. Measuring mathematical problem solving with the math dataset.arXiv preprint arXiv:2103.03874, 2021. 8

    Pith/arXiv arXiv 2021

  14. [14]

    F. Hong, G. Yu, Y . Ye, H. Huang, H. Zheng, Y . Zhang, Y . Wang, and J. Yao. Wide-in, narrow-out: Revokable decoding for efficient and effective dllms.arXiv preprint arXiv:2507.18578, 2025. 8

    arXiv 2025

  15. [15]

    Jiang, F

    J. Jiang, F. Wang, J. Shen, S. Kim, and S. Kim. A survey on large language models for code generation. arXiv preprint arXiv:2406.00515, 2024. 1

    Pith/arXiv arXiv 2024

  16. [16]

    Jiang, Y

    Y . Jiang, Y . Cai, X. Luo, J. Fu, J. Wang, C. Liu, and X. Yang.d2 cache: Accelerating diffusion-based llms via dual adaptive caching.arXiv preprint arXiv:2509.23094, 2025. 1

    arXiv 2025

  17. [17]

    Khanna, S

    S. Khanna, S. Kharbanda, S. Li, H. Varma, E. Wang, S. Birnbaum, Z. Luo, Y . Miraoui, A. Palrecha, S. Ermon, et al. Mercury: Ultra-fast language models based on diffusion.arXiv preprint arXiv:2506.17298,

    Pith/arXiv arXiv

  18. [18]

    J. Kim, K. Shah, V . Kontonis, S. Kakade, and S. Chen. Train for the worst, plan for the best: Understanding token ordering in masked diffusions.arXiv preprint arXiv:2502.06768, 2025. 1

    Pith/arXiv arXiv 2025

  19. [19]

    Leviathan, M

    Y . Leviathan, M. Kalman, and Y . Matias. Fast inference from transformers via speculative decoding. In International Conference on Machine Learning, pages 19274–19286. PMLR, 2023. 3

    2023

  20. [20]

    J. Li, X. Dong, Y . Zang, Y . Cao, J. Wang, and D. Lin. Beyond fixed: Variable-length denoising for diffusion large language models.arXiv e-prints, pages arXiv–2508, 2025. 2, 3, 9

    2025

  21. [21]

    P. Li, Y . Zhou, D. Muhtar, L. Yin, S. Yan, L. Shen, Y . Liang, S. V osoughi, and S. Liu. Diffusion language models know the answer before decoding.arXiv preprint arXiv:2508.19982, 2025. 2

    Pith/arXiv arXiv 2025

  22. [22]

    A. Liu, B. Feng, B. Xue, B. Wang, B. Wu, C. Lu, C. Zhao, C. Deng, C. Zhang, C. Ruan, et al. Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437, 2024. 1

    Pith/arXiv arXiv 2024

  23. [23]

    Y . Liu, W. Chen, Y . Bai, X. Liang, G. Li, W. Gao, and L. Lin. Aligning cyber space with physical world: A comprehensive survey on embodied ai.IEEE/ASME Transactions on Mechatronics, 2025. 1

    2025

  24. [24]

    X. Miao, G. Oliaro, Z. Zhang, X. Cheng, Z. Wang, R. Y . Y . Wong, Z. Chen, D. Arfeen, R. Abhyankar, and Z. Jia. Specinfer: Accelerating generative llm serving with speculative inference and token tree verification. arXiv preprint arXiv:2305.09781, 1(2):4, 2023. 3

    Pith/arXiv arXiv 2023

  25. [25]

    S. Nie, F. Zhu, Z. You, X. Zhang, J. Ou, J. Hu, J. Zhou, Y . Lin, J.-R. Wen, and C. Li. Large language diffusion models.arXiv preprint arXiv:2502.09992, 2025. 1, 7

    Pith/arXiv arXiv 2025

  26. [26]

    R. Pope, S. Douglas, A. Chowdhery, J. Devlin, J. Bradbury, J. Heek, K. Xiao, S. Agrawal, and J. Dean. Efficiently scaling transformer inference.Proceedings of machine learning and systems, 5:606–624, 2023. 3

    2023

  27. [27]

    Sahoo, M

    S. Sahoo, M. Arriola, Y . Schiff, A. Gokaslan, E. Marroquin, J. Chiu, A. Rush, and V . Kuleshov. Simple and effective masked diffusion language models.Advances in Neural Information Processing Systems, 37:130136–130184, 2024. 2

    2024

  28. [28]

    J. Shi, K. Han, Z. Wang, A. Doucet, and M. Titsias. Simplified and generalized masked diffusion for discrete data.Advances in neural information processing systems, 37:103131–103167, 2024. 2 11

    2024

  29. [29]

    Y . Song, Z. Zhang, C. Luo, P. Gao, F. Xia, H. Luo, Z. Li, Y . Yang, H. Yu, X. Qu, et al. Seed diffusion: A large-scale diffusion language model with high-speed inference.arXiv preprint arXiv:2508.02193, 2025. 1

    Pith/arXiv arXiv 2025

  30. [30]

    Suzgun, N

    M. Suzgun, N. Scales, N. Schärli, S. Gehrmann, Y . Tay, H. W. Chung, A. Chowdhery, Q. Le, E. Chi, D. Zhou, et al. Challenging big-bench tasks and whether chain-of-thought can solve them. InFindings of the Association for Computational Linguistics: ACL 2023, pages 13003–13051, 2023. 8

    2023

  31. [31]

    Q. Wei, Y . Zhang, Z. Liu, D. Liu, and L. Zhang. Accelerating diffusion large language models with slowfast: The three golden principles.arXiv preprint arXiv:2506.10848, 2025. 1

    arXiv 2025

  32. [32]

    C. Wu, H. Zhang, S. Xue, Z. Liu, S. Diao, L. Zhu, P. Luo, S. Han, and E. Xie. Fast-dllm: Training-free acceleration of diffusion llm by enabling kv cache and parallel decoding.arXiv preprint arXiv:2505.22618,

    Pith/arXiv arXiv

  33. [33]

    Xiong, Y

    Z. Xiong, Y . Cai, Z. Li, and Y . Wang. Unveiling the potential of diffusion large language model in controllable generation.arXiv preprint arXiv:2507.04504, 2025. 1

    arXiv 2025

  34. [34]

    A. Yang, A. Li, B. Yang, B. Zhang, B. Hui, B. Zheng, B. Yu, C. Gao, C. Huang, C. Lv, et al. Qwen3 technical report.arXiv preprint arXiv:2505.09388, 2025. 1

    Pith/arXiv arXiv 2025

  35. [35]

    Y . Yang, C. Wang, S. Wang, Z. Wen, B. Qi, H. Xu, and L. Zhang. Diffusion llm with native variable generation lengths: Let [eos] lead the way.arXiv preprint arXiv:2510.24605, 2025. 3, 8

    arXiv 2025

  36. [36]

    J. Ye, Z. Xie, L. Zheng, J. Gao, Z. Wu, X. Jiang, Z. Li, and L. Kong. Dream 7b: Diffusion large language models.arXiv preprint arXiv:2508.15487, 2025. 1

    Pith/arXiv arXiv 2025

  37. [37]

    F. Zhu, R. Wang, S. Nie, X. Zhang, C. Wu, J. Hu, J. Zhou, J. Chen, Y . Lin, J.-R. Wen, et al. Llada 1.5: Variance-reduced preference optimization for large language diffusion models.arXiv preprint arXiv:2505.19223, 2025. 1, 7

    Pith/arXiv arXiv 2025

  38. [38]

    F. Zhu, Z. You, Y . Xing, Z. Huang, L. Liu, Y . Zhuang, G. Lu, K. Wang, X. Wang, L. Wei, et al. Llada-moe: A sparse moe diffusion language model.arXiv preprint arXiv:2509.24389, 2025. 1 12

    arXiv 2025