Writing study: Task-free Adaptive Meta Black-box Optimization

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← ICLR 2026 oral writing corpus

Chao Wang, Licheng Jiao, Lingling Li, Jiaxuan Zhao, Guanchun Wang, Fang Liu, Shuyuan Yang · 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: Learning & optimization algorithms (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

31 physical pages

Before references / appendix

≈ 9.44 page-equivalents

Main figures / tables

5 / 2

Attached appendix figures / tables

11 / 6

Attached appendix span

≈ 17.89 page-equivalents

Before references / appendix: ≈ 30.45%References / intervening material: ≈ 11.84%Attached appendix: ≈ 57.71%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	≈ 1.02 pp.	3.29%
2 RELATED WORKS	2	≈ 0.62 pp.	2%
3 ADAPTIVE META BLACK-BOX OPTIMIZATION MODEL	3	≈ 3.07 pp.	9.9%
4 EXPERIMENTS	6	≈ 3.45 pp.	11.13%
5 CONCLUSION AND DISCUSSION	9	≈ 0.46 pp.	1.48%
ACKNOWLEDGMENTS	10	≈ 0.22 pp.	0.71%
REFERENCES	10	≈ 3.66 pp.	11.81%
A REPRODUCIBILITY STATEMENT	14	≈ 0.3 pp.	0.97%
B USE OF LARGE LANGUAGE MODELS	14	≈ 0.12 pp.	0.39%
C PSEUDOCODE OF ABOM	14	≈ 0.31 pp.	1%
D CONVERGENCE ANALYSIS OF ABOM	14	≈ 2.93 pp.	9.45%
E CONVERGENCE ANALYSIS OF PARAMETER ADAPTATION	17	≈ 1.08 pp.	3.48%
F TASK CONFIGURATION	18	≈ 0.82 pp.	2.65%
G BASELINES	19	≈ 1.43 pp.	4.61%
H COMPARISON WITH EPOM	21	≈ 0.34 pp.	1.1%
I SENSITIVITY ANALYSIS OF LEARNING RATE	21	≈ 0.41 pp.	1.32%
J PRELIMINARY GENERALIZATION ANALYSIS OF ABOM	21	≈ 0.98 pp.	3.16%
K EXPERIMENTAL RESULTS	22	≈ 9.16 pp.	29.55%

All detected figure and table locations

Main text

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

Attached appendix

• Figure 6 · p. 18
• Figure 7 · p. 18
• Table 3 · p. 19
• Table 4 · p. 20
• Table 5 · p. 20
• Figure 8 · p. 21
• Figure 9 · p. 21
• Table 6 · p. 22
• Table 7 · p. 23
• Table 8 · p. 24
• Figure 10 · p. 25
• Figure 11 · p. 26
• Figure 12 · p. 27
• Figure 13 · p. 28
• Figure 14 · p. 29
• Figure 15 · p. 30
• Figure 16 · p. 31

Argument and organization

1. Task-free motivation and formalization

   Contrasts offline task-distribution training with online target-task adaptation.

2. Attention-based evolutionary operators

   Maps selection, crossover and mutation onto differentiable population transformations.

3. Four research questions

   Organizes performance, operator visualization, ablation and sensitivity analysis.

Problem, claim and contribution

The opening comparison distinguishes learning from a task distribution from learning during a single optimization run. The phrase task-free is defined operationally, not as absence of a target task.

Figure 1 and Section 3.1, pages 2–3. · Main paper

Argument and section order

Four explicit research questions give the results section a readable agenda and ensure that interpretability and sensitivity are not buried behind the main benchmark.

Section 4, pages 6–9. · Main paper

Figures, captions and visual explanation

A compact optimization loop sits above a magnified, color-separated operator diagram. Repeated population and fitness labels help connect the global process to individual matrix operations.

Figure 2, page 4. · Main paper

Tables, comparisons and evidence

The runtime plot separates optimization from offline training, while the ablation retains configurations that win on individual functions. Claims of universal matching or improvement exceed those function-level results.

Figure 3 and Table 2, page 8; Tables 7–8. · Main paper

Appendix and reproducibility

Reproducibility, pseudocode, proof and benchmark details are independently addressable appendices; full per-instance curves and distributions preserve heterogeneity hidden by normalized averages.

Appendices A, C–K, pages 14–31. · Main paper

Clarity and writing conventions

The convergence proof claims a positive conditional parameter density from discrete dropout and later turns time-indexed positive constants into a uniform bound without justification. This should not be copied as an established global guarantee.

Appendix D, Equations 19–26, pages 15–16. · Main paper

What to reuse—and what not to infer

Transferable lesson: Give each empirical question a named subsection, and show both the outer learning loop and its internal transformations in one hierarchical figure.

Boundary: An expressive parameterization does not establish that its actual stochastic updates reach every parameter neighborhood. Interpretability heatmaps and decreasing adaptation loss do not repair that exploration-proof gap.

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

Pages 1–10, Sections 1–5; references through page 13.

Appendix / supplement

Pages 14–31, Appendices A–K, including pseudocode, convergence arguments, all experimental configurations and supplementary results.

Visually inspected PDF pages

4, 8

• ICLR 2026 final proceedings · 31 pages