Writing study: OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent Collaboration

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← ICML 2026 oral writing corpus

Shijun Li, Hilaf Hasson, Joydeep Ghosh · 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: Training, objectives & optimization (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

29 physical pages

Before references / appendix

≈ 8.54 page-equivalents

Main figures / tables

4 / 4

Attached appendix figures / tables

4 / 16

Attached appendix span

≈ 17.91 page-equivalents

Before references / appendix: ≈ 29.45%References / intervening material: ≈ 8.79%Attached appendix: ≈ 61.76%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	≈ 1.38 pp.	4.76%
2. Problem Definition	2	≈ 0.36 pp.	1.24%
3. Methodology	3	≈ 2.63 pp.	9.07%
4. Experiments	5	≈ 2.66 pp.	9.17%
5. Related Work	8	≈ 0.81 pp.	2.79%
6. Conclusion	9	≈ 0.12 pp.	0.41%
Acknowledgment	9	≈ 0.08 pp.	0.28%
Impact Statement	9	≈ 0.16 pp.	0.55%
References	9	≈ 2.54 pp.	8.76%
A. Expanded Related Work	12	≈ 0.6 pp.	2.07%
B. Optimization Dimension Details	12	≈ 1.61 pp.	5.55%
C. Experiment Details and Additional Results	14	≈ 7.03 pp.	24.24%
D. Additional Discussions	21	≈ 2.75 pp.	9.48%
E. Prompts and Examples	24	≈ 5.91 pp.	20.38%

All detected figure and table locations

Main text

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

Attached appendix

• Table 5 · p. 15
• Table 6 · p. 15
• Table 7 · p. 16
• Table 8 · p. 16
• Figure 5 · p. 16
• Table 9 · p. 17
• Figure 6 · p. 17
• Table 11 · p. 17
• Table 10 · p. 17
• Figure 7 · p. 18
• Table 12 · p. 18
• Table 13 · p. 19
• Table 14 · p. 19
• Table 15 · p. 19
• Table 16 · p. 20
• Table 17 · p. 20
• Table 18 · p. 20
• Figure 8 · p. 21
• Table 19 · p. 24
• Table 20 · p. 26

Argument and organization

1. Problem and contribution

   Organizes agent functionality and collaboration structure as nodes and information-flow edges; a footnote explicitly uses optimization to mean improvement, not a mathematical optimum guarantee.

2. Argument sequence

   One-dimensional search is explained before alternating multiple dimensions, then inference selects the highest training-score configuration and testing evaluates transfer.

3. Evidence and verification

   Initializer-only removal, dimensions/order choices and separate inference API-cost tables test different burdens. Appendix C.1 reports three repeats and mean/standard deviation, but its general 3:1:1 split statement conflicts with later task-specific half splits.

Problem, claim and contribution

Organizes agent functionality and collaboration structure as nodes and information-flow edges; a footnote explicitly uses optimization to mean improvement, not a mathematical optimum guarantee.

Introduction footnote, p. 2; Section 3.1, p. 3 · Main paper

Argument and section order

One-dimensional search is explained before alternating multiple dimensions, then inference selects the highest training-score configuration and testing evaluates transfer.

Sections 3.2-3.4, pp. 3-5 · Main paper

Figures, captions and visual explanation

A compact workflow marks the changed agent versus controller; the main ablation table aligns dimensions across initializer-only and full methods, with reported means and variation.

Figure 3, p. 5; Table 4, p. 8; both PNGs inspected · Main paper

Tables, comparisons and evidence

Initializer-only removal, dimensions/order choices and separate inference API-cost tables test different burdens. Appendix C.1 reports three repeats and mean/standard deviation, but its general 3:1:1 split statement conflicts with later task-specific half splits.

Sections 4.3-4.4, pp. 7-8; Appendix C.1, p. 14; Table 11, p. 18 · Main paper

Appendix and reproducibility

Full prompts and before-after examples expose what was actually optimized, while the centralized-system discussion distinguishes broadly applicable definitions from untested structural adaptations.

Appendix D.4, p. 22; E, pp. 24-29 · Main paper

Clarity and writing conventions

The paper separates colloquial optimization from a guarantee, but claims that aligned train/test curves show no overfitting are stronger than the finite plotted evidence warrants.

Footnote, p. 2; Appendix C.2.7, p. 20 · Main paper

What to reuse—and what not to infer

Transferable lesson: Explain a multi-dimensional search through a reusable single-dimension loop, and keep offline search cost, inference savings and generalization checks separate.

Boundary: Prompt-level empirical improvement is not global optimality; training/test split descriptions need reconciliation, and flat-workflow results do not by themselves validate centralized or arbitrary multi-agent deployments.

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 distributed manuscript scan, not exhaustive technical/proof validation. Sections 1-6, pp. 1-9: five optimization dimensions, shared initializer/comparator loop, alternating updates, task evaluation, component controls and cost discussion.

Appendix / supplement

Writing-focused appendix scan; code, data and external assets not executed. Appendices A-E, pp. 12-29: dimension implementation, experimental protocols and extra tasks, cost and training curves, bias/architecture discussion, prompts and before-after examples.

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 ICML 2026 proceedings PDF, PMLR volume 306; retrieved 2026-10-07. Public OpenReview decision notes were not independently reverified. · 29 pages