Writing study: Let Features Decide Their Own Solvers: Hybrid Feature Caching for Diffusion Transformers

ICLR overviewCorpus atlasBy contribution typeVisual galleryRules & templateWriting patternsPaper studies

← ICLR 2026 oral writing corpus

Shikang Zheng, Guantao Chen, Qinming Zhou, Yuqi Lin, Lixuan He, Chang Zou, Peiliang Cai, Jiacheng Liu, Linfeng Zhang · Official ICLR 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: Inference, algorithms & systems efficiency (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

24 physical pages

Before references / appendix

≈ 10.66 page-equivalents

Main figures / tables

8 / 5

Attached appendix figures / tables

7 / 0

Attached appendix span

≈ 9.89 page-equivalents

Before references / appendix: ≈ 44.42%References / intervening material: ≈ 14.37%Attached appendix: ≈ 41.21%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	1	≈ 1.64 pp.	6.83%
2 RELATED WORK	3	≈ 0.5 pp.	2.08%
3 METHOD	3	≈ 1.82 pp.	7.58%
4 EXPERIMENTS	5	≈ 3.33 pp.	13.88%
5 DISCUSSION	8	≈ 1.96 pp.	8.17%
6 CONCLUSION	10	≈ 0.45 pp.	1.88%
ETHICS STATEMENT	11	≈ 0.29 pp.	1.21%
REPRODUCIBILITY STATEMENT	11	≈ 0.2 pp.	0.83%
REFERENCES	11	≈ 3.44 pp.	14.33%
A APPENDIX	15	≈ 9.89 pp.	41.21%

All detected figure and table locations

Main text

• Figure 1 · p. 1
• Figure 2 · p. 2
• Figure 3 · p. 4
• Table 1 · p. 5
• Table 2 · p. 6
• Table 3 · p. 6
• Figure 4 · p. 7
• Table 4 · p. 7
• Figure 5 · p. 8
• Table 5 · p. 8
• Figure 6 · p. 9
• Figure 7 · p. 9
• Figure 8 · p. 10

Attached appendix

• Figure 9 · p. 19
• Figure 10 · p. 19
• Figure 11 · p. 20
• Figure 12 · p. 21
• Figure 13 · p. 21
• Figure 14 · p. 23
• Figure 15 · p. 24

Argument and organization

1. Heterogeneous feature dynamics

   Motivates a dimension-wise rather than uniform cache predictor.

2. Offline selection and inference

   Separates calibration from deployed feature prediction.

3. Task-wise results and compatibility

   Tests generation, video, editing, distilled models and LoRA variants.

Problem, claim and contribution

The introduction identifies an overlooked unit of heterogeneity: feature dimensions, not tokens. The trajectory plots motivate assigning different predictors instead of only increasing a single predictor's order.

§1 and Figure2, pp.1–2 · Main paper

Argument and section order

Compatibility with distillation is tested separately rather than conflated with a new distillation algorithm. Table5 clearly defines perceptual fidelity relative to the corresponding distilled baseline, not to real images or the original50-step model.

§4.5 and Table5, p.8 · Main paper

Figures, captions and visual explanation

Figure3 explicitly separates offline clustering and solver selection from inference. Dimension colors persist through the pipeline, and skipped computations are visually distinct from full computations, clarifying what is actually accelerated.

Figure3, p.4 · Main paper

Tables, comparisons and evidence

The tables use separate latency and FLOP columns, but the abstract calls FLOP ratios speedups. For FLUX,5.55× FLOP reduction corresponds to4.01× measured speed; for HunyuanVideo,5.56× corresponds to5.09×. Summaries must preserve that distinction.

Abstract, p.1; Tables2–3, p.6 · Main paper

Appendix and reproducibility

The appendix usefully exposes descriptors and cache-level implementation, but Eq.32 does not algebraically simplify to Eq.33. Substitution gives2.5F_n−2F_(n−1)+0.5F_(n−2), so the deployed predictor needs code verification before its derivation can be reused.

AppendixA.6, Eqs.30–33, pp.22–23 · Main paper

Clarity and writing conventions

Input-invariance and near-lossless wording should remain empirical qualifications. Early clustering can be unstable, only two LoRA styles are tested, and high-acceleration Qwen ImageReward declines from1.2547 to1.0811 despite stronger general quality claims.

Table1, p.5; AppendicesA.5 and A.7, pp.21–24 · Main paper

What to reuse—and what not to infer

Transferable lesson: For acceleration work, distinguish calibration, arithmetic savings, measured latency and quality reference; validate every predictor equation against its implementation before turning an analogy into a guarantee.

Boundary: HyCa is caching, not a new KD objective. Classical implicit-solver descriptions do not by themselves establish numerical guarantees for substituted finite-difference predictors. One-step feature MSE and clustering agreement need not guarantee image quality under all prompts, adapters or hardware.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-09-19

Reading status

Full text + selected visuals inspected

Main text

pp.1–10, §§1–6; ethics, reproducibility and references pp.11–14

Appendix / supplement

pp.15–24, appendices A.1–A.7 including solver formulations, qualitative evidence and extreme settings

Visually inspected PDF pages

4, 22

• official ICLR 2026 proceedings PDF · 24 pages