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Writing study: Information Flow Reveals When to Trust Language Models

← ICML 2026 oral writing corpus

Rui Xu, Yi Chen, Jiujiu Chen, Sihong Xie · Official ICML 2026 oral record

Figure, table and section ledger

Editorial contribution type: Empirical analysis, diagnosis & auditing (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
20 physical pages
Before references / appendix
≈ 9.04 page-equivalents
Main figures / tables
6 / 2
Attached appendix figures / tables
1 / 11
Attached appendix span
≈ 7.91 page-equivalents
Before references / appendix: ≈ 45.2%References / intervening material: ≈ 15.25%Attached appendix: ≈ 39.55%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 headingStarts on PDF pageEstimated spanShare of entire PDF
1. Introduction1≈ 0.99 pp.4.95%
2. Related work2≈ 0.55 pp.2.75%
3. Background2≈ 0.89 pp.4.45%
4. Method3≈ 3.48 pp.17.4%
5. Experiment7≈ 0.61 pp.3.05%
6. Discussion7≈ 1.89 pp.9.45%
7. Conclusion9≈ 0.1 pp.0.5%
Impact Statement9≈ 0.05 pp.0.25%
Acknowledgement9≈ 0.15 pp.0.75%
References10≈ 3.04 pp.15.2%
A. Dominance of self-contributions via residual connection13≈ 0.24 pp.1.2%
B. Computation of rank-biased overlap (RBO)13≈ 0.39 pp.1.95%
C. KL Divergence for discrete probability distributions13≈ 0.75 pp.3.75%
D. Multi-level granularity14≈ 0.32 pp.1.6%
E. Inference Setup and Correctness Evaluation14≈ 0.85 pp.4.25%
F. Stability Analysis of UQ Methods15≈ 0.2 pp.1%
G. Discriminative ability15≈ 1.65 pp.8.25%
H. Computational cost and applicability17≈ 0.59 pp.2.95%
I. Performance on calibration metrics18≈ 0.38 pp.1.9%
J. Ranker model bias measurement18≈ 1.13 pp.5.65%
K. Effect of post-hoc calibration19≈ 1.41 pp.7.05%
All detected figure and table locations

Argument and organization

  1. 1 Introduction; 2 Related Work; 3 Background

    Locates the reliability problem in contextual grounding and defines layer-wise contributions.

  2. 4 Method

    Progresses from emergence and path aggregation to context slicing, external relevance and confidence aggregation.

  3. 5 Experiment; 6 Discussion; 7 Conclusion

    Separates discrimination results from transfer, intervention, ranker bias and calibration controls.

Problem, claim and contribution

The general trust headline is operationalized as uncertainty estimation for retrieved-context question answering, not universal model reliability.

Abstract, p.1; setup, p.7 · Main paper

Argument and section order

The method explains two complementary flow views with a worked path example and analogy before combining them into a supervised calibrator.

Sections 4.2-4.5, pp.5-7 · Main paper

Figures, captions and visual explanation

The path schematic marks admissible and invalid routes using the same colors as the matrix factors; paired ablation bars distinguish removing high-ranked from low-ranked tokens.

Figure 5, p.5; Figure 6, p.9 · Main paper

Tables, comparisons and evidence

The authors test token removal on one hundred initially correct examples, and separately acknowledge that ranker-derived relevance is an estimated reference rather than gold annotation.

Sections 6.2-6.3, p.8 · Main paper

Appendix and reproducibility

Calibration error, ranker verification and quadratic-in-length memory requirements are reported separately from the main AUROC/AUPRC ranking.

Appendices H-J, pp.17-18 · Main paper

Clarity and writing conventions

The results explicitly retain non-winning cells, but causal-faithfulness language should be limited to the tested input-removal intervention and judged correctness protocol.

Section 5.2, p.7; Section 6.2, p.8 · Main paper

What to reuse—and what not to infer

Transferable lesson: Distinguish discrimination, probability calibration, attribution intervention and external-reference quality instead of treating one reliability score as all four.

Boundary: The method requires internal model access, labeled calibration data and an external ranker; token removal does not establish complete causal faithfulness or trust outside the tested RAG setting.

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-oriented scan of the main argument through physical p.9, with focused physical-page text inspection on 1, 3, 5, 6, 7, 8, 9. Framing, section progression, mechanism/proof explanation, displays, comparison conditions and conclusions were inspected. This is not exhaustive technical reading, proof verification or reproduction.
Appendix / supplement
Appendix organization and main-text pointers were checked; focused text inspection of physical pages 13, 17, 18. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
Visually inspected PDF pages
5, 9

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.