{"id":"929a7db3-f7db-470c-9e9a-466e8bdac299","arxiv_id":"2507.23488","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":1,"one_line_summary":"A four-stage, PC-algorithm-based prompting pipeline raises OpenAI o3-mini's F1 on the Corr2Cause benchmark from 66.28 to 83.83, a new high for in-context causal discovery.","lead":"Researchers benchmarked two 'reasoning-first' AI model families on a causal discovery task and found they beat conventional models by a wide margin. They then split the task into four smaller prompts, which lifted the best model's F1 score from 66.3 to 83.8 without fine-tuning.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Reported F1 gains may be inflated by the evaluation protocol: a single pipeline run, prompts engineered against a public test set, and no contamination check for the API models.","rationale":"The reader's strongest claim is that modular in-context decomposition unlocks latent causal reasoning in o3-mini, with the pipeline's 83.83 F1 beating the 66.28 single-prompt baseline and the 33.38 BART MNLI baseline. The reader's weakest assumption is training-data contamination; I agree this is a real risk. However, I would weigh three protocol issues together rather than contamination alone: (1) the headline pipeline score comes from a single run, with Appendix B explicitly stating only one pipeline experiment was executed; (2) prompts were iteratively engineered with no reported held-out split, so test-set selection is uncontrolled; and (3) no contamination check exists for the closed API models. The first two issues threaten even the within-model pipeline-versus-baseline comparison if the pipeline run was favorable or the prompts were tuned on the test set; contamination mainly threatens the absolute score and the comparison to published baselines. These are addressable with an out-of-sample evaluation, repeated runs, and a leakage check, so the conditional-acceptance verdict is appropriate. I therefore keep the reader's verdict unchanged while narrowing the required conditions.","tokens_in":12488,"tokens_out":7908,"duration_ms":84830,"concrete_test":"Freeze the four Appendix D prompts and run them, without any further prompt modification, on a freshly generated Corr2Cause-style test set that re-labels variable identifiers and paraphrases premise/hypothesis text (the original benchmark's adversarial splits are a suitable template). If o3-mini pipeline F1 drops from 83.83 toward the ~50 range, memorization or test-set overfitting is the driver. Also run the o3-mini pipeline 5 times on a random 200-sample subset to estimate run-to-run F1 variance; a standard deviation above roughly 2-3 F1 points would show the single-run headline is not stable.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section 4.1's headline number, o3-mini pipeline F1 = 83.83, rests on an evaluation protocol that is weaker than the claim requires. Appendix B reports three independent baseline runs but only one pipeline experiment, so the 17.55-point gain over the single-prompt baseline has no run-to-run error bar; Appendix E admits that confidence intervals are provided only for the DeepSeek-R1 API baseline and defers the rest to a 'final version.' Separately, Appendix D describes an iterative prompt-engineering process, but the paper never defines a development/validation split, so the four prompts may have been selected using the same 1,162-sample test split whose scores are then reported. Finally, Corr2Cause is publicly hosted on Hugging Face and has been used in prior LLM evaluations, and the paper provides no leakage analysis for o3-mini or DeepSeek-R1 API. Contamination would inflate the absolute F1 scores and the comparison to BART MNLI's 33.38, undermining the 'latent causal-reasoning ability' interpretation; the within-model pipeline-versus-baseline difference is less affected. These issues are addressable, not fatal, but they are load-bearing for the central claim.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper studies causal discovery on the Corr2Cause benchmark using reasoning-specialist large language models (DeepSeek-R1-70B, DeepSeek-R1 API, and OpenAI o3-mini). It proposes a four-stage modular in-context pipeline that decomposes the PC algorithm into skeleton extraction, v-structure identification, Meek-rule orientation, and hypothesis evaluation, with Python parsers passing structured intermediate outputs between stages. Table 2 reports an F1 of 83.83 for o3-mini in the pipeline, compared with 66.28 for the single-prompt baseline and 33.38 for the best published conventional baseline (BART MNLI), and the paper interprets these gains as evidence that reasoning-specialist LLMs possess latent causal-reasoning ability that careful prompt decomposition can unlock. The paper also analyzes token usage, stage-wise errors, and qualitative reasoning-trace differences.","tokens_in":12736,"tokens_out":5641,"duration_ms":54586,"significance":"The proposed modular pipeline is a simple and potentially useful empirical contribution: it requires no fine-tuning, it is grounded in a well-known algorithm, and the stage-wise error analysis and token-cost comparison are informative. The authors promise to release code, prompt templates, and evaluation scripts, which would aid reproducibility. However, the headline claims currently rest on single-run point estimates from one public benchmark, prompt engineering was performed iteratively on that benchmark without a defined validation split, and no contamination analysis is provided for the API models. If these evaluation gaps are closed, the paper could be a solid empirical contribution to LLM-based causal discovery; in its current form the evidence is not strong enough to support the strength of the conclusions.","major_comments":[{"comment":"The headline pipeline results are single-run point estimates. Appendix E explicitly states that bootstrap confidence intervals were computed only for the DeepSeek-R1 API single-prompt baseline (mean F1 0.6527, 95% CI [0.6461, 0.6616]) and that standard deviations for the remaining experiments will be reported 'in the final version.' Without run-to-run variability for the o3-mini pipeline result (83.83) and the 66.28 single-prompt baseline, the 17.55-point improvement cannot be assessed for statistical significance, and the token-efficiency comparison in Figure 3 is similarly based on unreplicated measurements. The authors should provide multiple runs and bootstrap intervals for all cells in Table 2, or at minimum for the headline baseline-versus-pipeline comparisons.","section":"Section 4.1, Table 2, Appendices B and E"},{"comment":"The paper states that the stage-wise prompts were 'refined through a careful, iterative prompt engineering process' but never defines a development/validation split. Evaluation is performed on the full 1,162-sample test split of Corr2Cause. If prompt variants were selected by their scores on this same split, the reported F1 is optimistically biased, and the claim that the framework is a generalizable blueprint is not supported. The authors should report how many prompt variants were tried, define a held-out development split for prompt selection, and evaluate the final prompts on a separate test split or through a nested protocol.","section":"Section 4.1 and Appendix D"},{"comment":"No contamination or leakage check is performed for the closed API models. Corr2Cause is a publicly hosted Hugging Face dataset that has been used in earlier LLM evaluations, and the training corpora of o3-mini and DeepSeek-R1 are undisclosed. If those models memorized benchmark instances, the absolute gains over BART MNLI and the 'native causal reasoning' interpretation would be inflated. The authors should add a perturbation analysis (for example, variable-identifier substitution or paraphrasing, as in the original Corr2Cause study) and/or a leakage probe. The within-model pipeline-versus-baseline difference would be less affected by contamination, so such a check would help bound the main claim.","section":"Section 4.1 and Appendix B"}],"minor_comments":[{"comment":"The phrase 'nearly three-fold improvements' overstates Table 2: 83.83 versus 33.38 is a factor of 2.51, not three.","section":"Abstract and Section 5"},{"comment":"The model name 'LlaMa3.3-70B' should be written as 'LLaMA-3.3-70B'.","section":"Appendix B"},{"comment":"The prompt templates use 'Casual skeleton' where 'Causal skeleton' is meant.","section":"Appendix D, Listings 3 and 4"},{"comment":"The text repeatedly uses 'common model' where 'conventional model' is meant; this makes the failure analysis harder to read.","section":"Section 4.3"},{"comment":"The statement 'Using five bootstrap samples (R = 5, B = 1000)' is confusing: if B = 1000 resamples, R = 5 likely refers to five independent bootstrap repetitions, but this should be stated explicitly.","section":"Appendix E"},{"comment":"PC-SubQ is discussed quantitatively in the text but its published F1 values are not included in Table 2, making the claimed comparison difficult for the reader to verify.","section":"Section 3.5"},{"comment":"The reference to Meek (2013) appears to be a misdating of the standard citation Meek (1995) for causal-inference rules with background knowledge; please verify and correct.","section":"References"}],"recommendation":"major_revision","confidential_remarks":"The central empirical claim is plausible, but the missing error bars, the unstated prompt-selection protocol, and the absence of a contamination check are exactly the kinds of evidence this journal's readership would expect before accepting a benchmark-driven LLM paper. I would encourage the editor to request these additions rather than reject, because they are within the scope of a revision and the underlying idea is worth publishing if the evaluation is made rigorous. I would also verify that the GitHub repository actually contains the promised code and prompt templates."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this is a credible engineering paper with a useful result, but the headline numbers are not yet trustworthy because the evaluation protocol is under-specified. The staged-prompting idea is not new — PC-SubQ already did staged prompting for the PC algorithm — but the application to reasoning-specialist models and the stage-wise error analysis are new. The paper does several things well: it releases code and prompt templates, it reports a consistent within-model improvement for every pipeline over its baseline (all three models gain), and it is candid about limitations, including the single benchmark and the missing confidence intervals, which Appendix E explicitly defers to a final version. The reasoning-trace analysis is a nice addition.\n\nThe soft spots are exactly where the reader puts them. The o3-mini pipeline F1 of 83.83 rests on one run with no error bar. Appendix B says there were three baseline runs but only one pipeline run. Appendix D describes iterative prompt engineering but never defines a development/validation split, so the prompts may have been selected on the same 1,162-sample test split whose scores are then reported. And there is no contamination check for the API models; Corr2Cause is public and has been used in prior LLM evals, so the absolute numbers could be inflated. The within-model pipeline-versus-baseline gap is less vulnerable to contamination, and the fact that it holds across all three models is reassuring. But the \"latent causal-reasoning ability\" interpretation rests on the absolute comparison with BART MNLI, which is exactly the comparison contamination would inflate.\n\nMy view: the central argument survives in a weaker form — staged prompting helps reasoning models on this benchmark. That is worth publishing once error bars, held-out prompt selection, and a leakage check are added. As written, it is a solid workshop paper or a conditional acceptance with required revisions. I would not cite the absolute numbers yet, but I would read the revision and expect the authors can address these concerns without much trouble.","headline":"A useful pipeline result that is likely real, but the headline numbers need error bars, a validation split, and a contamination check before the absolute gains are believable.","tokens_in":13250,"tokens_out":1532,"would_cite":false,"duration_ms":16424,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Reasoning models reach 83.8 percent F1 on causal discovery when the PC algorithm is split into four prompts.","keywords":["causal discovery","large language models","in-context learning","PC algorithm","Corr2Cause benchmark","reasoning models","prompt decomposition","zero-shot"],"falsifier":"Permute the variable names and re-parse the independence statements of the Corr2Cause test set while keeping every causal relation identical, then run the four-stage o3-mini pipeline; if F1 collapses toward the 33.38 conventional baseline, the reported gain is mostly benchmark memorization.","tokens_in":12316,"feed_emoji":"🧩","tokens_out":6229,"duration_ms":59364,"temperature":0.7,"pith_summary":"The paper tries to establish that state-of-the-art reasoning LLMs can do causal discovery from natural-language conditional-independence statements, and that their ability is sharply amplified when the PC algorithm is split into four separate in-context prompts. On the Corr2Cause benchmark, the four-stage pipeline lifts OpenAI o3-mini's F1 from 66.28 with a single combined prompt to 83.83, against 33.38 for the best published conventional baseline, with no fine-tuning. If that is right, prompt decomposition, not extra training or model scale alone, is the key to turning LLM reasoning traces into reliable algorithmic inference. The paper also shows that the decomposition concentrates errors in the later orientation and hypothesis-evaluation stages, and that reasoning models outperform conventional ones by revisiting and correcting their own intermediate conclusions.","feed_headline":"Four prompt steps lift causal discovery F1 to 83.8 percent","feed_subtitle":"Decomposing the PC algorithm into staged prompts beats fine-tuned baselines using only in-context reasoning.","key_machinery":"The load-bearing machinery is the PC algorithm recast as a four-stage prompt pipeline: undirected skeleton extraction from correlation and independence statements, v-structure identification using separation sets, edge orientation via Meek's rules subject to cycle avoidance and v-structure preservation, and hypothesis evaluation against the Markov equivalence class expressed as a CPDAG. Each stage is a separate persona-framed prompt whose output is parsed into a canonical Python object and fed into the next prompt, so the model's internal reasoning is restarted four times instead of once. The CPDAG is the intermediate object that lets the final stage answer true only when the hypothesis holds in every valid DAG, which is exactly what the benchmark asks.","core_discovery":"On its own terms, the paper reports that reasoning-first LLM families, OpenAI o3-mini and DeepSeek-R1, already beat every published Corr2Cause result in a zero-shot single prompt that embeds the full PC algorithm, and that a four-module version of the same prompt, skeleton extraction, v-structure identification, Meek-rule orientation, and hypothesis evaluation, raises performance to an F1 of 83.83 for o3-mini, with precision 90.91 and accuracy 95.32. The authors attribute the gain to stage-wise re-activation of the model's reasoning trace, structured intermediate artifacts passed between prompts via parsers, and a persona that anchors each stage in the PC algorithm. They further claim that the pipeline delivers consistent improvements for every model tested and that the later stages are the main bottleneck.","pith_inferences":["Beyond the paper: the within-model gain from single-prompt to pipeline is largely immune to benchmark-contamination worries, because both conditions use the same model and API, so the modularity benefit is a cleaner signal than the absolute F1.","Beyond the paper: a cheap falsification test would freeze the four prompts and shuffle variable names and rephrase the independence statements; if the pipeline F1 drops far below 83.83, the model is exploiting surface statistics rather than causal structure.","Beyond the paper: the same stage architecture should apply to other constraint-based causal-discovery algorithms such as FCI, where the extra stages for latent confounders would give an immediate test of generality.","Beyond the paper: the claim that longer reasoning traces cause the gains predicts that artificially capping per-stage token budgets, or suppressing the model's self-check behavior, should degrade later stages more than skeleton extraction."],"forward_implications":["If the result holds, causal discovery from verbalized independence statements no longer requires fine-tuning: a reasoned prompt decomposition matches or beats trained baselines.","The four-stage blueprint transfers to any task with a known algorithmic decomposition, since each stage gets focused attention and an inspectable intermediate artifact, at the cost of extra tokens and latency.","Stage-wise scoring identifies the bottleneck: skeleton and v-structure stages are nearly perfect, while Meek-rule orientation and hypothesis evaluation drive the remaining errors, so those are the stages where better prompts or verification would pay off.","The reported token-to-F1 improvement implies a practical budgeting rule: accuracy can be traded against cost by choosing how many pipeline stages to run."],"supporting_citations":[{"why":"Supplies the Corr2Cause benchmark, its test split, and the published baselines that the paper must beat.","marker":"Jin et al., 2024"},{"why":"Provides the PC algorithm whose four stages the modular pipeline decomposes.","marker":"Spirtes et al., 2001"},{"why":"Chain-of-thought prompting is the baseline technique the pipeline extends.","marker":"Wei et al., 2023"},{"why":"Tree-of-thoughts supplies the decomposition rationale for multi-stage in-context reasoning.","marker":"Yao et al., 2023"},{"why":"Describes DeepSeek-R1's RL-driven reasoning, the open reasoning model family evaluated.","marker":"DeepSeek-AI, 2025"},{"why":"Describes o3-mini, the best-performing model in the pipeline experiments.","marker":"OpenAI, 2025"},{"why":"PC-SubQ is the closest staged-prompting competitor whose lower F1 motivates the four-module design.","marker":"Sgouritsa et al., 2024"}],"fun_headline_variants":["Four prompt steps lift causal discovery F1 to 83.8%","Modular in-context prompting hits 83.8 F1 on causal discovery","Staged reasoning beats fine-tuned baselines on Corr2Cause","Reasoning models excel with four-step prompt decomposition","In-context modular pipeline triples causal discovery performance"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The result presupposes that o3-mini and the DeepSeek-R1 API were not trained on Corr2Cause, so their answers reflect causal reasoning rather than memorized examples; the paper reports no contamination check.","fun_headline_variants_meta":{"raw":{"variants":["Four prompt steps lift causal discovery F1 to 83.8%","Modular in-context prompting hits 83.8 F1 on causal discovery","Staged reasoning beats fine-tuned baselines on Corr2Cause","Reasoning models excel with four-step prompt decomposition","In-context modular pipeline triples causal discovery performance"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000443,"raw_usage":{"total_tokens":2218,"prompt_tokens":892,"completion_tokens":1326,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":508,"completion_tokens_details":{"reasoning_tokens":1237}},"tokens_in":508,"tokens_out":1326,"duration_ms":11257,"temperature":1.0,"reasoning_tokens":1237,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T10:41:34.018316+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Permute the variable names and re-parse the independence statements of the Corr2Cause test set while keeping every causal relation identical, then run the four-stage o3-mini pipeline; if F1 collapses toward the 33.38 conventional baseline, the reported gain is mostly benchmark memorization.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the Corr2Cause benchmark, its test split, and the published baselines that the paper must beat."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the PC algorithm whose four stages the modular pipeline decomposes."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Chain-of-thought prompting is the baseline technique the pipeline extends."},{"cited_title":"L., Cao, Y., and Narasimhan, K","cited_arxiv_id":null,"evidence_quote":"Tree-of-thoughts supplies the decomposition rationale for multi-stage in-context reasoning."},{"cited_title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning","cited_arxiv_id":null,"evidence_quote":"Describes DeepSeek-R1's RL-driven reasoning, the open reasoning model family evaluated."},{"cited_title":"Openai o3-mini: Pushing the frontier of cost-effective reasoning","cited_arxiv_id":null,"evidence_quote":"Describes o3-mini, the best-performing model in the pipeline experiments."}],"review_version":1}