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Writing study: CausalGame: Benchmarking Causal Thinking of LLM Agents in Games

← ICML 2026 oral writing corpus

Zhenhao Chen, Yongqiang Chen, Chenxi Liu, Junchi Yu, Xiangchen Song, Zijian Li, Jialin Li, Philip Torr, Bo Han, Kun Zhang · Official ICML 2026 oral record

Figure, table and section ledger

Editorial contribution type: Datasets, benchmarks & measurement (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
40 physical pages
Before references / appendix
≈ 9.23 page-equivalents
Main figures / tables
12 / 4
Attached appendix figures / tables
12 / 18
Attached appendix span
≈ 23.91 page-equivalents
Before references / appendix: ≈ 23.08%References / intervening material: ≈ 17.15%Attached appendix: ≈ 59.77%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Source-bound correction: Table 12 has a split label/caption at the top of physical p.28; source text and a rendered page confirm the omitted caption.

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≈ 1.55 pp.3.88%
2. Related Work2≈ 0.9 pp.2.25%
3. CausalGame Benchmark3≈ 2.06 pp.5.15%
4. Experiments5≈ 3.99 pp.9.98%
5. Conclusions9≈ 0.19 pp.0.47%
Acknowledgements10≈ 0.11 pp.0.27%
Impact Statement10≈ 0.08 pp.0.2%
References10≈ 6.85 pp.17.13%
A. Limitations and Future Works17≈ 0.31 pp.0.78%
B. Related Work17≈ 1.29 pp.3.23%
C. Details of CausalGame Benchmark18≈ 6.16 pp.15.4%
D. Details of Experimental Results24≈ 7.34 pp.18.35%
E. Rubrics used for evaluation32≈ 2.89 pp.7.23%
F. Sample Agent Trajectories35≈ 1.77 pp.4.42%
G. Additional Analysis36≈ 4.15 pp.10.38%
All detected figure and table locations

Argument and organization

  1. 1 Introduction; 2 Related Work

    Motivates a causal-thinking evaluation gap in scientific-agent benchmarks.

  2. 3 CausalGame Benchmark

    Defines interaction budgets, hidden mechanisms, biased observations and separate outcome/rubric scoring.

  3. 4 Experiments; 5 Conclusions

    Organizes observations, execution-mode controls, cross-benchmark correlations and judge/trajectory failure diagnostics.

Problem, claim and contribution

The benchmark distinguishes obtaining a good final design from correctly explaining the mechanism, making the construct broader than reward maximization alone.

Sections 3.1 and 3.3, physical pp.4–5 · Main paper

Argument and section order

Simulator rules and exploration/evaluation budgets precede the leaderboard; rubric reliability is discussed before interpreting rubric-derived failure categories.

Sections 3–4, pp.3–8 · Main paper

Figures, captions and visual explanation

The pipeline explicitly joins observed history, active experiments and a final report; the analysis page places failure definitions alongside correlation and inter-rater plots.

Figure 2, p.3; Table 4 and Figures 8–9, p.8 · Main paper

Tables, comparisons and evidence

Main error bars summarize variation across scenarios, whereas experiments use three independent trials per model/scenario and appendices separately report uncertainty. The agent/prompt modes differ in several capabilities, not only interaction count.

Section 4.1 and Figures 4–6, p.6 · Main paper

Appendix and reproducibility

SCM tables make intervention, hidden variables, observed cues and win thresholds inspectable; the rubric table assigns explicit weights and the judge prompt asks for evidence-grounded decisions.

Tables 7–8, p.21; Table 18 and Appendix E, p.32 · Main paper

Clarity and writing conventions

A judge-independent configuration-path analysis complements LLM-scored explanations, separating an observable search failure from an inferred reasoning defect.

Configuration-based analysis and Figure 12, p.9 · Main paper

What to reuse—and what not to infer

Transferable lesson: For a reasoning benchmark, define the latent mechanism, permissible interventions, outcome score and process rubric separately, then validate the rubric with another observable analysis.

Boundary: Toy simulator failures are not proof that all scientific reasoning is absent or that medical deployment is safe/unsafe. Agreement among LLM judges is not ground-truth validity. The main 5–8-point threshold description does not cover Weather Noise’s 78% optimum versus 55% threshold in Table 8; preserve scenario-specific rules.

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 scan across physical PDF pp.1–9, covering framing, section progression, definitions, method/evidence explanations, captions and scope. Physical pages extracted individually to avoid form-feed pagination errors. Selected full-page rasters inspected; not exhaustive line-by-line technical verification or experimental reproduction.
Appendix / supplement
Appendix heading/structure inventory checked across the source; selected detail reading of SCM specifications and optimal-survival definitions C.3–C.4 (p.21) and rubric/prompt E (p.32). Full prompts, all trajectory examples and numerical appendices were not exhaustively read or reproduced.
Visually inspected PDF pages
3, 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.