Writing study: Learning to See Before Seeing: Demystifying LLM Visual Priors from Language Pre-training

ICLR overviewCorpus atlasBy contribution typeVisual galleryRules & templateWriting patternsPaper studies

← ICLR 2026 oral writing corpus

Junlin Han, Shengbang Tong, David Fan, Yufan Ren, Koustuv Sinha, Philip Torr, Filippos Kokkinos · 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: Empirical analysis, diagnosis & auditing (primary) · Learning & optimization algorithms.

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

≈ 10.39 page-equivalents

Main figures / tables

8 / 3

Attached appendix figures / tables

6 / 3

Attached appendix span

≈ 13.65 page-equivalents

Before references / appendix: ≈ 33.52%References / intervening material: ≈ 22.45%Attached appendix: ≈ 44.03%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	≈ 1.03 pp.	3.32%
2 PROBLEM FORMULATION	2	≈ 1.02 pp.	3.29%
3 DEMYSTIFYING LLM VISUAL PRIORS: STUDIES AND FINDINGS	3	≈ 6.34 pp.	20.45%
4 SCALING UP AND TRAINING A VISION-AWARE LLM	10	≈ 0.64 pp.	2.06%
5 CONCLUSION	10	≈ 0.43 pp.	1.39%
ETHICS STATEMENT	11	≈ 0.11 pp.	0.35%
REPRODUCIBILITY STATEMENT	11	≈ 0.11 pp.	0.35%
REFERENCES	11	≈ 6.96 pp.	22.45%
A DISCUSSION AND HYPOTHESES	18	≈ 2.82 pp.	9.1%
B PROBLEM FORMULATION DETAILS	21	≈ 1.93 pp.	6.23%
C RELATED WORK	23	≈ 1 pp.	3.23%
D LIMITATIONS AND FUTURE RESEARCH DIRECTIONS	24	≈ 0.39 pp.	1.26%
E BROADER IMPACT	24	≈ 0.29 pp.	0.94%
F VISUAL WORLD AND REASONING-CENTRIC LANGUAGE DATA	24	≈ 0.85 pp.	2.74%
G VISUAL INSTRUCTION TUNING DATA CLASSIFICATION	25	≈ 0.23 pp.	0.74%
H ROBUST PARSING FOR VQA EVALUATIONS	25	≈ 0.81 pp.	2.61%
I MULTI-LEVEL EXISTENCE BENCHMARK CONSTRUCTION	26	≈ 0.18 pp.	0.58%
J MULTI-LEVEL EXISTENCE BENCHMARK RESULTS	26	≈ 0.95 pp.	3.06%
K BLIND VISUAL INSTRUCTION TUNING	27	≈ 0.97 pp.	3.13%
L HALLUCINATIONS IN BLIND VQA	28	≈ 2.07 pp.	6.68%
M ADDITIONAL QUALITATIVE EXAMPLES	30	≈ 1.15 pp.	3.71%

All detected figure and table locations

Main text

• Figure 1 · p. 3
• Figure 2 · p. 3
• Figure 3 · p. 4
• Figure 4 · p. 5
• Table 1 · p. 6
• Table 2 · p. 7
• Figure 5 · p. 7
• Figure 6 · p. 8
• Figure 7 · p. 9
• Table 3 · p. 10
• Figure 8 · p. 18

Attached appendix

• Figure 9 · p. 19
• Figure 10 · p. 19
• Figure 11 · p. 20
• Table 4 · p. 25
• Figure 12 · p. 27
• Table 5 · p. 27
• Table 6 · p. 28
• Figure 13 · p. 31
• Figure 14 · p. 31

Argument and organization

1. Introduction / Problem formulation

   Defines visual priors operationally and introduces common adaptation/evaluation settings.

2. Demystifying LLM visual priors: studies and findings

   Organizes controlled interventions around six numbered findings.

3. Scaling up and training a vision-aware LLM

   Checks whether a small-scale recipe transfers to a larger training budget.

4. Appendices A–M

   Separates hypotheses, protocols, limitations, benchmark details and qualitative counterexamples.

Problem, claim and contribution

The paper explicitly excludes a Bayesian interpretation of 'prior' and frames it as text-pretrained knowledge that helps later vision adaptation. Three contribution axes—structure, source and recipe—make a large empirical project navigable.

§1, p.2 · Main paper

Argument and section order

Each study opens with a boxed finding, then states its controlled variable and fixed budget. The sequence progresses from size and source to mixture selection, cross-encoder tests and a 1T-token validation.

§§3.1–4.2, pp.3–10 · Main paper

Figures, captions and visual explanation

Table 2 places all mixture proportions beside text/vision metrics, exposing trade-offs. Figure 5 compresses cross-task correlations, while Figure 14 displays a counterexample where more visual text does not monotonically improve perception.

Table 2 and Figure 5, p.7; Figure 14, p.31 · Main paper

Tables, comparisons and evidence

Table 3 tests two 7B models at the same 1T-token budget and reports language and vision together. This is stronger evidence for the recipe than selecting a favorable category from the earlier 3B grid search.

§4 and Table 3, p.10 · Main paper

Appendix and reproducibility

Explicit protocols disclose the fixed seed, answer parser and data-classification prompts. Separate limitations restrict architectural and modality scope; blind-tuning gains are accompanied by a warning about language shortcuts and hallucination.

Appendices B, D, F–H and K, pp.21–28 · Main paper

Clarity and writing conventions

Appendix A labels speculative mechanisms as hypotheses, but main-text language of statistical independence is stronger than the correlation evidence. The distinction should remain explicit when reusing this narrative style.

§3.5, pp.7–8; Appendix A, pp.18–20 · Main paper

What to reuse—and what not to infer

Transferable lesson: Use finding-led sections for a multi-experiment study, make controls explicit, and validate a derived recipe at a new scale while retaining negative qualitative examples.

Boundary: Correlations do not establish independent latent mechanisms. 'Universality' is tested with three encoders, not all multimodal architectures; Appendix B uses one fixed seed, and dataset/answer-parser choices affect conclusions.

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

PDF pp.1–10, abstract and §§1–5; ethics, reproducibility, AI disclosure and references pp.11–17

Appendix / supplement

PDF pp.18–31, Appendices A–M, including protocols, limitations, annotation prompts, parser, benchmark construction and blind-VQA examples

Visually inspected PDF pages

7, 10, 31

• official ICLR 2026 proceedings PDF · 31 pages