Writing study: Clarification as Supervision: Reinforcement Learning for Vision-Language Interfaces

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← NeurIPS 2026 oral writing corpus

John Gkountouras, Ivan Titov · Official NeurIPS 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

22 physical pages

Before references / appendix

≈ 9.16 page-equivalents

Main figures / tables

3 / 5

Attached appendix figures / tables

1 / 1

Attached appendix span

≈ 8.89 page-equivalents

Before references / appendix: ≈ 41.64%References / intervening material: ≈ 17.95%Attached appendix: ≈ 40.41%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Source-bound correction: Top-level heading and reference/appendix boundaries checked against source PDF text. Bibliography titles/author initials, numbered contribution/property bullets, diagram steps, equations and TOC entries are not substantive headings. Section spans are page-coordinate estimates, not ink area or prose-only length. This is an author manuscript mirror, not a verified accepted-version template. Source layout uses physical single-column y/page-height, including equation-heavy pages.

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	≈ 1.62 pp.	7.36%
2 RELATED WORK	3	≈ 0.53 pp.	2.41%
3 METHODOLOGY	3	≈ 1.39 pp.	6.32%
4 EXPERIMENTS	5	≈ 4.53 pp.	20.59%
5 CONCLUSION	9	≈ 0.23 pp.	1.05%
ACKNOWLEDGMENTS	9	≈ 0.29 pp.	1.32%
REFERENCES	10	≈ 3.95 pp.	17.95%
A AC-RL ALGORITHM	14	≈ 2 pp.	9.09%
B UNBIASEDNESS OF THE THREE-TIER REWARD	16	≈ 1.54 pp.	7%
C GENERATION DIVERSITY DURING TRAINING	17	≈ 0.2 pp.	0.91%
D PROMPT TEMPLATES	17	≈ 3.61 pp.	16.41%
E TRAINING HYPERPARAMETERS	21	≈ 0.81 pp.	3.68%
F LLM USAGE STATEMENT	22	≈ 0.73 pp.	3.32%

All detected figure and table locations

Main text

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

Attached appendix

• Figure 4 · p. 18
• Table 6 · p. 21

Argument and organization

1. Problem and contribution

   Identifies omitted visual information in a captioner–reasoner interface rather than assuming the reasoner itself lacks capability.

2. Argument sequence

   Separates training interaction, reward construction and inference before main results and interface-behavior diagnostics.

3. Evidence and verification

   One table isolates modularization, binary reward and AC-RL under single-pass inference; a second measures clarification requests rather than only answer accuracy.

Problem, claim and contribution

Identifies omitted visual information in a captioner–reasoner interface rather than assuming the reasoner itself lacks capability.

Introduction · Main paper

Argument and section order

Separates training interaction, reward construction and inference before main results and interface-behavior diagnostics.

Sections 3–4 · Main paper

Figures, captions and visual explanation

The architecture uses distinct direct-answer and training-only clarification regions, with frozen/trainable icons and dotted gradient paths.

Figure 1, p. 2 · Main paper

Tables, comparisons and evidence

One table isolates modularization, binary reward and AC-RL under single-pass inference; a second measures clarification requests rather than only answer accuracy.

Tables 2–3, p. 7 · Main paper

Appendix and reproducibility

Formal reward analysis, algorithms, prompt templates, diversity checks and hyperparameters have separate destinations.

Appendices A–F · Main paper

Clarity and writing conventions

Ablation paragraphs identify incremental changes and their settings instead of treating all gains as a single architectural effect.

Section 4.2, p. 7 · Main paper

What to reuse—and what not to infer

Transferable lesson: Make privileged training feedback visibly separate from deployment and test whether the interface actually changes.

Boundary: The September 2025 author version predates the 2026 accepted record; its ICLR-like layout is not a NeurIPS template example. Clarification-rate reduction is a diagnostic, not semantic completeness.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-10-02

Reading status

Writing-focused manuscript scan + selected visuals inspected

Main text

Writing-oriented distributed manuscript scan of Sections 1–5, pp. 1–9; reference region pp. 10–13: abstract/framing, section sequence, substantive text windows, captions, evidence organization and conclusion; not exhaustive technical verification.

Appendix / supplement

Writing-oriented scan of Appendices A–F, pp. 14–22; code and experiments were not executed.

Visually inspected PDF pages

2, 7

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.

• Public author arXiv manuscript 2509.26594v1; not verified identical to the accepted or camera-ready OpenReview revision · 22 pages