Writing study: WeDLM: Reconciling Diffusion Language Models with Standard Causal Attention for Fast Inference

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← ICML 2026 oral writing corpus

Aiwei Liu, Minghua He, Shaoxun Zeng, Sijun Zhang, Linhao Zhang, Chuhan Wu, Wei Jia, Yuan Liu, Zhou Xiao, Jie Zhou · Official ICML 2026 oral record

Observed writing choices, not acceptance rules. This study examines the inspected manuscript version. It does not establish why the paper was accepted or that its presentation choices should be copied unchanged.

Figure, table and section ledger

Editorial contribution type: Efficiency, inference & systems (primary).

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF

18 physical pages

Before references / appendix

≈ 9.14 page-equivalents

Main figures / tables

5 / 2

Attached appendix figures / tables

4 / 3

Attached appendix span

≈ 6.5 page-equivalents

Before references / appendix: ≈ 50.78%References / intervening material: ≈ 13.11%Attached appendix: ≈ 36.11%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level heading	Starts on PDF page	Estimated span	Share of entire PDF
1. Introduction	2	≈ 1.29 pp.	7.17%
2. Preliminary	3	≈ 0.36 pp.	2%
3. Motivation and Analysis	3	≈ 0.85 pp.	4.72%
4. WeDLM: Training and Inference	4	≈ 2.2 pp.	12.22%
5. Experiments	6	≈ 2.39 pp.	13.28%
6. Related Work	9	≈ 0.54 pp.	3%
7. Conclusion	9	≈ 0.25 pp.	1.39%
Acknowledgements	9	≈ 0.18 pp.	1%
Impact Statement	10	≈ 0.05 pp.	0.28%
References	10	≈ 2.35 pp.	13.06%
A. Streaming Parallel Decoding Algorithm	12	≈ 0.28 pp.	1.56%
B. Training Cost	12	≈ 0.9 pp.	5%
C. Additional Experimental Results	13	≈ 0.15 pp.	0.83%
D. Batch Throughput across Task Types	13	≈ 0.81 pp.	4.5%
E. Ablation on Key Components	14	≈ 0.17 pp.	0.94%
F. Prefix Cacheability Metric	14	≈ 4.2 pp.	23.33%

All detected figure and table locations

Main text

• Figure 1 · p. 1
• Figure 2 · p. 5
• Figure 3 · p. 6
• Table 1 · p. 7
• Figure 4 · p. 8
• Figure 5 · p. 8
• Table 2 · p. 12

Attached appendix

• Table 3 · p. 14
• Table 4 · p. 14
• Figure 6 · p. 15
• Table 5 · p. 15
• Figure 7 · p. 16
• Figure 8 · p. 17
• Figure 9 · p. 18

Argument and organization

1. Problem and claim

   Reframes the bottleneck from how many tokens are predicted per forward pass to how quickly tokens become a reusable contiguous prefix. This motivates the design before its implementation details.

2. Argument sequence

   States two design principles, then separates reordering, training streams and streaming inference. Later ablations address threshold, ordering preference, block size and continued-training controls rather than treating all changes as one package.

3. Evidence and verification

   The frontier exposes that aggressive decoding sacrifices accuracy, while the model-size panel retains cases where the proposed method trails continued AR training. Appendix throughput tables explicitly exclude prefill rather than presenting them as end-to-end latency.

Problem, claim and contribution

Reframes the bottleneck from how many tokens are predicted per forward pass to how quickly tokens become a reusable contiguous prefix. This motivates the design before its implementation details.

Introduction and Section 3, pp. 2–4 · Main paper

Argument and section order

States two design principles, then separates reordering, training streams and streaming inference. Later ablations address threshold, ordering preference, block size and continued-training controls rather than treating all changes as one package.

Sections 3–5, pp. 3–8 · Main paper

Figures, captions and visual explanation

Figure 2 aligns token identities with logical positions so physical reordering remains legible; its companion attention mask distinguishes permitted and blocked regions. Figure 5 places the quality–speed frontier beside block-size and model-scale controls.

Figure 2, physical p. 5; Figure 5, physical p. 8; rasters inspected · Main paper

Tables, comparisons and evidence

The frontier exposes that aggressive decoding sacrifices accuracy, while the model-size panel retains cases where the proposed method trails continued AR training. Appendix throughput tables explicitly exclude prefill rather than presenting them as end-to-end latency.

Figure 5 and Sections 5.4–5.5, pp. 8–9; Appendix D and Tables 3–4, pp. 13–14 · Main paper

Appendix and reproducibility

Pseudocode defines streaming state and fallback behavior; training-cost and batch-throughput tables answer different resource questions. Low- and high-entropy examples illustrate why a single peak tokens-per-second value is insufficient.

Appendices A–F, pp. 12–18 · Main paper

Clarity and writing conventions

The design-principles discussion explicitly rejects equivalence to bidirectional-attention diffusion models. The causal factorization is a modeling choice whose adequacy is tested empirically.

Section 3.2, physical p. 4 · Main paper

What to reuse—and what not to infer

Transferable lesson: Show the altered dependency structure and its operating-point frontier together; distinguish cache behavior, decode throughput and end-to-end latency.

Boundary: The fastest low-entropy example is not representative reasoning speed, and a hardware-matched comparison still needs matching quality and workload definitions.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-10-07

Reading status

Writing-focused manuscript scan + selected visuals inspected

Main text

Writing-focused scan of Sections 1–7 and limitations, pp. 1–9; mechanism equations, causal-mask diagrams, efficiency definitions and ablations. The impact statement is on p. 10.

Appendix / supplement

Structural and selected-detail scan of Appendices A–F, pp. 12–18: decoding algorithm, training cost, base-model results, batch throughput, component ablations, cacheability and contrasting generation examples. No code execution.

Visually inspected PDF pages

5, 8

This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.

• Published proceedings PDF, PMLR volume 306, retrieved 2026-10-07 · 18 pages