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
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 | ≈ 0.99 pp. | 4.95% |
| 2. Related work | 2 | ≈ 0.55 pp. | 2.75% |
| 3. Background | 2 | ≈ 0.89 pp. | 4.45% |
| 4. Method | 3 | ≈ 3.48 pp. | 17.4% |
| 5. Experiment | 7 | ≈ 0.61 pp. | 3.05% |
| 6. Discussion | 7 | ≈ 1.89 pp. | 9.45% |
| 7. Conclusion | 9 | ≈ 0.1 pp. | 0.5% |
| Impact Statement | 9 | ≈ 0.05 pp. | 0.25% |
| Acknowledgement | 9 | ≈ 0.15 pp. | 0.75% |
| References | 10 | ≈ 3.04 pp. | 15.2% |
| A. Dominance of self-contributions via residual connection | 13 | ≈ 0.24 pp. | 1.2% |
| B. Computation of rank-biased overlap (RBO) | 13 | ≈ 0.39 pp. | 1.95% |
| C. KL Divergence for discrete probability distributions | 13 | ≈ 0.75 pp. | 3.75% |
| D. Multi-level granularity | 14 | ≈ 0.32 pp. | 1.6% |
| E. Inference Setup and Correctness Evaluation | 14 | ≈ 0.85 pp. | 4.25% |
| F. Stability Analysis of UQ Methods | 15 | ≈ 0.2 pp. | 1% |
| G. Discriminative ability | 15 | ≈ 1.65 pp. | 8.25% |
| H. Computational cost and applicability | 17 | ≈ 0.59 pp. | 2.95% |
| I. Performance on calibration metrics | 18 | ≈ 0.38 pp. | 1.9% |
| J. Ranker model bias measurement | 18 | ≈ 1.13 pp. | 5.65% |
| K. Effect of post-hoc calibration | 19 | ≈ 1.41 pp. | 7.05% |
All detected figure and table locations
Main text
Argument and organization
- 1 Introduction; 2 Related Work; 3 Background
Locates the reliability problem in contextual grounding and defines layer-wise contributions.
- 4 Method
Progresses from emergence and path aggregation to context slicing, external relevance and confidence aggregation.
- 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.