Pith. sign in

REVIEW 4 major objections 4 minor 129 references

Com$^2$: A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper introduces Com2, a causal-graph benchmark for complex commonsense reasoning, and claims that large language models reason adequately along a single causal chain but fail when a sudden or uncommon event interrupts the chain.

desk verdict A genuinely new way to build a commonsense benchmark, but the paper hasn't yet shown the items actually demand complex reasoning—the validation strategy and sample items leave that claim unproven. read the letter →

arxiv 2506.07064 v1 pith:M34CNOPI submitted 2025-06-08 cs.CL cs.AI

Kai Xiong , Xiao Ding , Yixin Cao , Yuxiong Yan , Li Du , Yufei Zhang , Jinglong Gao , Jiaqian Liu
show 2 more authors
Bing Qin Ting Liu
This is my paper
classification cs.CLcs.AI
keywords complexcommonsensereasoningcausaleventgraphtheoryinterventioncounterfactualLLMbenchmarkslowthinkingdetectivestories
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Com2 is a benchmark that tries to measure whether large language models (LLMs) can handle complex commonsense reasoning, not just simple one-step facts. The authors build structured causal event graphs, modify them with operations from causal theory such as intervention and counterfactual reasoning, and use a language model to generate questions with slow-thinking traces. Across 2,500 main questions and 1,254 detective-story questions, existing LLMs score highest on direct causal chains and counterfactuals and lowest on transition and intervention tasks, which the authors read as a real limitation in reasoning breadth. If the benchmark is valid, it provides a measurement tool that reveals a weakness in LLMs that math, code, and simple commonsense tests do not expose.

What carries the argument

The load-bearing object is the causal event graph (CEG): a directed acyclic graph whose nodes are natural-language events and whose edges are cause-effect relations. The paper treats the tail of the most likely generated chain as assumed truth, then applies causal-theory operations—chiefly intervention, written $do(X)$, which cuts an event off from its usual causes—and counterfactuals to produce alternative graphs. Those graphs do the work of turning informal commonsense into structured scenarios with a definite answer, and they also guide the slow-thinking trace that accompanies every question.

What would settle it

Have independent human annotators answer a random sample of Com2 questions without being shown the causal graphs; if their answers match the generated ground truth no better than LLMs do, the benchmark's answers are not independently certified and the depth-versus-breadth diagnosis lacks support.

Watch

Extended reading notes

Core claim

The paper's central claim is that LLMs have not mastered complex commonsense reasoning, and that the bottleneck is not stored knowledge but the ability to follow long causal chains and to reroute them when an uncommon or sudden event intervenes. To establish this, the paper constructs Com2: concrete and abstract events seed five-event causal chains; causal theory supplies interventions and counterfactuals that reshape those chains into direct, decision, transition, intervention, and counterfactual scenarios; and gpt-4o-mini turns each graph into a multiple-choice or multi-select question with a slow-thinking answer trace. A harder set is built from detective stories, where identifying the culprit requires combining interwoven clues. Evaluation of general and reasoning-tuned LLMs shows accuracy drops sharply on transition and intervention, and the paper reports that further training on generated examples and providing the slow-thinking trace both lift performance. The authors conclude that current models have limited reasoning depth and breadth, and that both can be partially improved.

Load-bearing premise

The load-bearing premise is that the tail events of gpt-4o-mini-generated causal chains are correct, complex, and representative commonsense; the human evaluation only checks annotator agreement with the graph-to-answer mapping, not the accuracy of the chains themselves.

Editorial extensions

If this is right

  • Commonsense evaluation for LLMs should include tasks that change the usual causal route, because direct-chain questions alone overstate model competence.
  • Training on causally structured commonsense examples can improve performance on the main benchmark and transfer partially to out-of-distribution hard questions.
  • Supplying a slow-thinking trace helps weaker models select better answers, suggesting that causal structure is what they lack rather than raw knowledge.
  • Reasoning-oriented models do not consistently beat general models on the main set, so test-time scaling that helps math and code does not automatically transfer to commonsense.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: the paper's human evaluation checks that annotators, given the causal graphs, agree with the answers; it does not establish that the generated chains themselves are correct, so an independent plausibility study of the chains would directly test the benchmark's foundation.
  • Inference: because Com2-hard draws on published detective stories, some models may have memorized the plots; retesting on newly written stories would separate genuine reasoning from memorization.
  • Inference: the generation pipeline could be reused as an endless data engine, systematically varying chain length and intervention position to study reasoning depth and breadth separately.
  • Inference: reasoning breadth could be made quantitative by measuring accuracy as a function of how far an intervention moves a chain from its most probable path.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper introduces Com2, a benchmark for complex commonsense reasoning. The construction pipeline starts from LLM-proposed concrete and abstract events, generates five-event causal chains, applies causal-theoretic operations (direct, decision, transition, intervention, counterfactual) to form causal graphs, and uses gpt-4o-mini to synthesize 2,500 main and 1,254 hard multiple-choice/multi-select questions with attached slow-thinking traces. The authors evaluate a range of general and reasoning LLMs and report that models struggle especially on Transition and Intervention tasks, that reasoning breadth is limited, and that post-training or providing slow thinking alleviates the difficulties.

Significance. If its construct validity were established, Com2 would address a real gap: a structured, causal-graph-grounded benchmark for complex commonsense reasoning, distinct from math and code benchmarks. The paper's strengths include the transparent and reproducible synthesis pipeline, the public release of code and data, the broad coverage of LLMs, and the honest limitations paragraph. However, the central claim—that Com2 measures complex commonsense reasoning and reveals a distinct LLM weakness—rests on unvalidated LLM-generated ground truth and on examples that in the appendix appear one-step and sometimes inconsistent with the stated task definitions. The claimed finding about reasoning depth and breadth is therefore not yet supported by the evidence presented.

major comments (4)
  1. [Sec. 3.7 and Appendix B] The human evaluation verifies only that annotators, when given the causal graphs, reproduce the graph-to-answer mapping (91–94% agreement); it does not validate that the gpt-4o-mini-generated causal chains are correct, representative, or complex. The Introduction states that 'the most likely outcome is treated as the assumed truth,' but the manuscript never tests that assumption directly. This is load-bearing because the benchmark's validity depends on the tail events of generated chains being genuine, complex commonsense ground truth. Please add direct human annotation of chain plausibility, typicality, and complexity, or construct a second independent gold set, and report inter-annotator agreement on those judgments.
  2. [Appendix C] Several presented examples contradict the task definitions in Secs. 3.3–3.5. The Transition example asks 'What is the first step to build a bookshelf from scratch?'—this is not a long-term-outcome question and requires only a single inference, not a five-event causal chain. The Intervention example ('a child snaps a twig... animal hides in a burrow') is likewise a one-step reaction. The hard Counterfactual example asks 'who is most likely to be the criminal' and provides a non-counterfactual factual question, which does not match the counterfactual construction described in Sec. 3.5. These mismatches suggest the generation pipeline does not reliably produce items that require multi-step, complex reasoning. Please re-audit the data, provide statistics on actual inference depth per item, and replace or filter examples that violate the stated protocols.
  3. [Sec. 4.3 and overall evaluation] The paper does not include baselines against existing simple-commonsense benchmarks (e.g., COPA, Social IQa, CommonsenseQA) or ablated short-chain versions of the same items. Without such controls, the low scores on Transition and Intervention cannot be attributed to a distinct deficit in complex reasoning depth/breadth; they could reflect ambiguous answer keys, arbitrary distractor generation, or annotation noise. Please add these comparisons or item-level human difficulty ratings to support the claim that Com2 measures complex commonsense reasoning beyond what simple benchmarks capture.
  4. [Sec. 5.3] The experiment that provides the gold slow-thinking trace to LLMs (Qwen-tp and LLaMA-tp) measures the model's ability to follow a provided reasoning trace, not the model's own slow-thinking capability. The conclusion that 'slow thinking can alleviate' the limitations conflates external scaffolding with model-generated deliberation. Please separate these conditions, for example by comparing model-generated CoT with the provided gold trace, and by reporting performance without any trace.
minor comments (4)
  1. [Sec. 5.1] There is a typo in 'Interevntion' in the sentence describing the training data distribution; please correct it.
  2. [Appendix A] The intervention prompt contains 'probality' instead of 'probability'; several other prompts use 'slowing thinking' instead of 'slow thinking' inconsistently.
  3. [Sec. 3.3] The Decision graph description says 'The new chain shares the same root event as sc_i', but the context suggests it should be sa_i, since both chains are based on abstract causal chains.
  4. [Figure 3] The label 'Transitive' in the figure is inconsistent with the 'Transition' terminology used in the text; please align the terminology.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Com2's construction is not equivalent to its evaluation claims; the observed validity gaps are benchmark-quality concerns, not derivation circularity.

full rationale

Walking the paper's derivation chain, I find no step where a stated 'prediction' or 'first-principles result' reduces to its own input by construction. The benchmark labels are produced by prompting gpt-4o-mini to generate causal chains and treating 'the most likely outcome' as 'the assumed truth' (Sections 3.1-3.4), and the human evaluation in Section 3.7 verifies only that annotators, when given the causal graphs, agree with the graph-to-answer mapping (91-94% agreement). These are genuine threats to construct validity and label quality: the causal chains themselves are not directly validated for correctness, representativeness, or complexity, and some appendix examples appear one-step. However, this is a validity weakness, not circularity under the required standard. No equation is identical to another by construction, no fitted parameter is renamed as a prediction, and no load-bearing claim is justified solely by a self-citation. The authors' prior work is cited for background concepts such as 'scene drift' (Xiong et al., 2022), but the Transition prompt itself defines scene drift and threshold effect, so the benchmark does not depend on an unverified self-citation for its logical content. The empirical finding that LLMs score lower on Transition and Intervention is an observed outcome of evaluation, not a consequence forced by the dataset construction; indeed, some models score well above chance. The Limitations section similarly frames the synthesis shortcomings as refinements, not as a logical collapse of the method. The appropriate venue for the reader's concerns is construct-validity and correctness risk, not circularity. Score 0.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The benchmark's central claim rests on the validity of LLM-generated causal chains as complex commonsense ground truth, an unvalidated assumption. The paper adds no new physical or formal entities; its contributions are data construction choices (n, prompts, decoding settings) that are not fully specified.

free parameters (2)
  • n (number of seed events)
    The number of concrete and abstract seed events (n) is a hand-chosen design parameter that determines the size of Com2-main. The paper reports 500 examples per task but never states n, so the dataset construction is not fully specified.
  • gpt-4o-mini decoding settings
    The paper does not report temperature, top-p, or other sampling parameters used in data synthesis, which affect the generated causal chains and thus the benchmark content.
assumptions (5)
  • domain assumption A causal event graph is a representation of complex commonsense knowledge, and causal relationships among events in the graph are rigorous (Sec. 2.1).
    The benchmark's core assumption is that CEGs encode complex commonsense; this comes from the authors' prior work and is not independently validated here.
  • ad hoc to paper The most likely outcome in a generated causal chain is the assumed truth (Introduction, Sec. 3.4).
    Ground-truth answers are defined as the tail events of gpt-4o-mini-generated chains, with no external validation of the chains' correctness.
  • domain assumption Causal theory operations (intervention, counterfactual) modify graphs to produce scenarios that match user concerns and are meaningful for commonsense evaluation (Sec. 2.2, 3.3).
    The paper applies Pearl-style operations to generate tasks; whether these correspond to real user concerns or valid reasoning challenges is assumed.
  • domain assumption The BMDS detective stories contain sufficiently complex interwoven clues to make Com2-hard more challenging (Sec. 3.5).
    The use of detective stories as a harder split relies on this premise, which is not directly measured beyond the lower scores observed.
  • domain assumption Human evaluation with three annotators and 100 samples per split is sufficient to establish benchmark quality (Sec. 3.7, Appendix B).
    The quality claim rests on a small annotation study; inter-annotator agreement is reported but no error analysis of disagreements is given.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Com$^2$: A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models." pith.science (2026). https://pith.science/paper/M34CNOPI

@misc{pith2026250607064,
  author       = {Pith},
  title        = {Pith review of: Com$^2$: A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M34CNOPI}},
  note         = {Machine review of arXiv:2506.07064}
}
abstract

Large language models (LLMs) have mastered abundant simple and explicit commonsense knowledge through pre-training, enabling them to achieve human-like performance in simple commonsense reasoning. Nevertheless, LLMs struggle to reason with complex and implicit commonsense knowledge that is derived from simple ones (such as understanding the long-term effects of certain events), an aspect humans tend to focus on more. Existing works focus on complex tasks like math and code, while complex commonsense reasoning remains underexplored due to its uncertainty and lack of structure. To fill this gap and align with real-world concerns, we propose a benchmark Com$^2$ focusing on complex commonsense reasoning. We first incorporate causal event graphs to serve as structured complex commonsense. Then we adopt causal theory~(e.g., intervention) to modify the causal event graphs and obtain different scenarios that meet human concerns. Finally, an LLM is employed to synthesize examples with slow thinking, which is guided by the logical relationships in the modified causal graphs. Furthermore, we use detective stories to construct a more challenging subset. Experiments show that LLMs struggle in reasoning depth and breadth, while post-training and slow thinking can alleviate this. The code and data are available at https://github.com/Waste-Wood/Com2.

Figures

Figures reproduced from arXiv: 2506.07064 by the authors.

Figure 1
Figure 1. (a) An one-step reasoning question. (b) Users [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Three examples of (a) causal graph, (b) intervention, and (c) counterfactual, respectively. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The whole workflow of Com2 construction: (1) Event Proposal employs LLMs to propose concrete and abstract events; (2) Causal Chain Proposal synthesizes causal chains based on the proposed events; (3) Causal Graph Proposal adopts causal theory to generate various causal graphs based the synthesized causal chains; (4) Com2 Synthesis creates multi-choice and multi-select questions with slow thinking, which is guided by… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Overall performance of trained LLMs (Ours) [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: The relationships between LLMs performance [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: The overall performance of LLaMA and Qwen [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

129 extracted references · 77 canonical work pages

  1. [1]

    the causal chain contains 5 events and 4 causal relationships

  2. [2]

    the causal chain should be common in real life

  3. [3]

    Here are the given event: {} You should follow the format like: {} -> EVENT -> EVENT -> EVENT -> EVENT Causal Chain Proposal: Intervention You are an expert in causality

    the causal chain should has a proper time gap to make users cannot infer tail event just based on the given event. Here are the given event: {} You should follow the format like: {} -> EVENT -> EVENT -> EVENT -> EVENT Causal Chain Proposal: Intervention You are an expert in causality. Please create two causal chains based on and start from the given event...

  4. [4]

    each causal chain should has a proper time gap to make users cannot infer tail event just based on the given event

  5. [5]

    the tail event should be the same. Here are the given event: {} You should follow the format like: High Probability: -> EVENT1 -> EVENT2 -> EVENT3 -> SAME EVENT Low Probability: -> EVENT4 -> EVENT5 -> EVENT6 -> SAME EVENT Causal Chain Proposal: Counterfactual You are an expert in causality. Please create two causal chains based on the given event. The fol...

  6. [6]

    Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yu- taka Matsuo, and Yusuke Iwasawa

    Enhancing llm reasoning with reward-guided tree search.arXiv preprint arXiv:2411.11694. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yu- taka Matsuo, and Yusuke Iwasawa. 2022. Large lan- guage models are zero-shot reasoners.Advances in neural information processing systems, 35:22199– 22213. Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui...

    arXiv 2022

  7. [7]

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao

    Tree of thoughts: Deliberate problem solving with large language models.Advances in Neural Information Processing Systems, 36. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2023. React: Synergizing reasoning and acting in language models. InThe Eleventh International Conference on Learning Representations. J...

    arXiv 2023

  8. [9]

    you should make a **mistake** first, and conduct self-refinement to backtrack and reason correctly by the causal chain

Show all 129 references
  1. [10]

    Causal Graph Proposal and Com2 Synthesis: Intervention After understanding the causal chains, we need you to create a multiple-choice example

    the output format should be: Question: __\n\n Options: __\n\n Slow Thinking Process: __\n\n Answer: __. Causal Graph Proposal and Com2 Synthesis: Intervention After understanding the causal chains, we need you to create a multiple-choice example. The following are the requirements:

  2. [13]

    the second causal chain should have a lower probality to happend than the first chain

  3. [17]

    the first causal chain should be common in real life

  4. [18]

    the second causal chain should be a counterfactual scenario (science fiction or an event which is unable to intervene) of the first causal chain

  5. [19]

    each causal chain should has a proper time gap to make users cannot infer tail event just based on the given event. Here are the given event: {} You should follow the format like: Normal: {} -> EVENT -> EVENT -> EVENT -> EVENT Counterfactual: Counterfactual_of_given_event -> E...

  6. [20]

    each causal chain contains 5 events and 4 causal relationships, each chain should be reasonable

  7. [21]

    each chain would lead to bad outcomes or something unacceptable

  8. [22]

    the first causal chain should have a high probability to happen, which means it is common in real life

  9. [23]

    the second causal chain should have a much lower probality to happend than the first chain

  10. [24]

    each causal chain should has a proper time gap to make users cannot infer tail event just based on the given event. Here are the given event: {} You should follow the format like: High Probability: {} -> EVENT -> EVENT -> EVENT -> EVENT Low Probability: {} -> EVENT -> EVENT ->...

  11. [25]

    each causal chain contains 5 events and 4 causal relationships

  12. [26]

    the first causal chain should be reasonable without any causal transitive problems

  13. [27]

    the second causal chain should contain a scene drift problem

  14. [28]

    the thrid causal chain should contain a threshold effect problem

  15. [29]

    Take a causal chian A -> B -> C for example, here are the reference of the reason for the causal transitive problem:

    each causal chain should has a proper time gap to make users cannot infer tail event just based on the given event. Take a causal chian A -> B -> C for example, here are the reference of the reason for the causal transitive problem:

  16. [30]

    scene drift: A -> B and B -> C would not happen within the same specific scene

  17. [31]

    Here are the given event: {} Just give the chains only

    threshold effect: the influence of A on B is not enough for B to cause C. Here are the given event: {} Just give the chains only. You should follow the format like: Normal: -> EVENT -> EVENT -> EVENT -> EVENT With Scene Drift: -> EVENT -> EVENT -> EVENT -> EVENT With Threshold...

  18. [33]

    note that the causal chain is not a part of the final example, it is just used to help you design the example

  19. [34]

    The intermediate events in the causal chains cannot appear in the question

    in the question, you should create a suitable question. The intermediate events in the causal chains cannot appear in the question. The question is asking for a most plausible outcome

  20. [35]

    the final answer should be the tail event of the causal chain, and the other wrong options should be deceptive

  21. [36]

    -Method Reuse: For some problems that can be transformed into classic problems (such as the shortest path or knapsack problem), you can quickly reuse existing methods to solve them

    based on the causal chain, you should create a slowing thinking process, which consists of several actions (selecting from the following actions), actions can be repeated: -Systematic Analysis: Starting from the overall structure of the problem, first analyze the inputs and ou...

  22. [38]

    do not explicitly demonstrate which chain you are using, all the chains are used to guide the slowing thinking process, it will not be given to help with question answering

  23. [45]

    -in the second causal chain, the transit is NOT interrupted by the intervention

    you should conduct an intervention on the first causal chain to achieve the following goals: -in the first causal chain, the transit to the tail event is interrupted by the intervention. -in the second causal chain, the transit is NOT interrupted by the intervention

  24. [46]

    the question can only contain the first events of the causal chains and the intervention event

  25. [47]

    the final answer should be the tail event of the second causal chain, and the other options should be wrong

  26. [48]

    -Method Reuse: For some problems that can be transformed into classic problems (such as the shortest path or knapsack problem), you can quickly reuse existing methods to solve them

    based on the causal chains, you should create a slowing thinking process, which consists of several actions (selecting from the following actions), actions can be repeated: -Systematic Analysis: Starting from the overall structure of the problem, first analyze the inputs and o...

  27. [51]

    Causal Graph Proposal and Com2 Synthesis: Counterfactual After understanding the causal chains, we need you to create a multiple-choice example

    the output format should be: Question: __\n\n Options: __\n\n Slow Thinking Process: __\n\n Answer: __. Causal Graph Proposal and Com2 Synthesis: Counterfactual After understanding the causal chains, we need you to create a multiple-choice example. The following are the requirements:

  28. [53]

    in the question, the intermediate events in each causal chain cannot appear in the question

  29. [55]

    If A leads to B not C, what will happen?)

    you should use the counterfactual chain to create a counterfactual question (e.g. If A leads to B not C, what will happen?)

  30. [56]

    the final answer should be the tail event of the counterfactual chain, and the other wrong options should be deceptive

  31. [57]

    -Method Reuse: For some problems that can be transformed into classic problems (such as the shortest path or knapsack problem), you can quickly reuse existing methods to solve them

    based on the causal chains, you should simulate first-person thinking and create a slowing thinking process, which consists of several actions (selecting from the following actions), actions can be repeated: -Systematic Analysis: Starting from the overall structure of the prob...

  32. [59]

    the counterfactual must be conducted in the question, which cannot appear in the thinking process

  33. [60]

    you should make a **mistake** first, and conduct self-refinement to backtrack and reason correctly

  34. [61]

    Causal Graph Proposal and Com2 Synthesis: Decision After understanding the causal chains carefully, we need you to create a multiple-select example

    the output format should be: Question: __\n\n Options: __\n\n Slow Thinking Process: __\n\n Answer: __. Causal Graph Proposal and Com2 Synthesis: Decision After understanding the causal chains carefully, we need you to create a multiple-select example. The following are the re...

  35. [62]

    the example should contain a question, a slow thinking process, several options, and answers

  36. [63]

    note that the causal chains are not a part of the final example, they are just used to help you design the example

  37. [64]

    the question mainly investigates how to severe results, which means what interventions we can do to break the transition to bad results

  38. [65]

    the answer choices should be interventions applied to the intermediate events of the given causal chains

  39. [66]

    in the options, besides the answers choices, you should provide several distractors, which are wrong but deceptive

  40. [67]

    the intermediate events in the causal chains cannot appear in the question

  41. [68]

    -Method Reuse: For some problems that can be transformed into classic problems (such as the shortest path or knapsack problem), you can quickly reuse existing methods to solve them

    based on the causal chains and question, you should create a slowing thinking process, which consists of several actions (selecting from the following actions), actions can be repeated: -Systematic Analysis: Starting from the overall structure of the problem, first analyze the...

  42. [70]

    the created question should be as concise as possible, while the slow thinking process should be as detailed and complex as possible

  43. [71]

    Causal Graph Proposal and Com2 Synthesis: Transition After understanding the causal chains and the scenario, we need you to create a multiple-choice example

    the output format should be: Question: __\n\n Options: __\n\n Slow Thinking Process: __\n\n Answer: __. Causal Graph Proposal and Com2 Synthesis: Transition After understanding the causal chains and the scenario, we need you to create a multiple-choice example. The following a...

  44. [72]

    the example should contain a question, a slow thinking process, some options, and an answer

  45. [73]

    note that the causal chains and scenario are not a part of the final example, they are just used to help you design the example

  46. [74]

    the intermediate events in each causal chain cannot appear in the question

  47. [75]

    -threshold effect: the influence of A on B is not enough for B to cause C

    the second has the causal transitive problem of , take A -> B -> C for example, here are the definitions: -scene drift: A -> B and B -> C would not happen within the same specific scene. -threshold effect: the influence of A on B is not enough for B to cause C

  48. [76]

    the question can only contain the first events of the causal chains

  49. [77]

    the final answer should be the tail event of the first causal chain, and the other options should be wrong

  50. [78]

    -Method Reuse: For some problems that can be transformed into classic problems (such as the shortest path or knapsack problem), you can quickly reuse existing methods to solve them

    based on the causal chains and scenario, you should create a slowing thinking process, which consists of several actions (selecting from the following actions), actions can be repeated: -Systematic Analysis: Starting from the overall structure of the problem, first analyze the...

  51. [79]

    you should conduct reasoning via the second causal chain to make a **mistake** and conduct self-refinement to backtrack and reason by the second causal chain

  52. [80]

    the whole slow thinking process should simulate first-person thinking

  53. [81]

    the created question should be as concise as possible, and the slow thinking process should be as detailed and complex as possible

  54. [82]

    None of the above

    the output format should be: Question: __\n\n Options: __\n\n Slow Thinking Process: __\n\n Answer: __. Detective Proposal and Com2 Synthesis: Intervention criminal {}\n——-\nWho is(are) the criminal(s)? Just output the name(s). puzzle This is a detective story, can you generat...

  55. [83]

    Ignoring health issues typically leads to worsening conditions

    Context Identification: I need to analyze the situation where Sarah ignored her symptoms. Ignoring health issues typically leads to worsening conditions. I need to determine the long-term consequences of ignoring symptoms rather than seeking help

  56. [84]

    One possibility is that she could start feeling better due to the body’s natural healing process

    Systematic Analysis: Let’s break down the potential outcomes of ignoring symptoms. One possibility is that she could start feeling better due to the body’s natural healing process. Another option could be that she decides to just cope with the pain without any medical intervention

  57. [85]

    Option A suggests she would feel better, which often isn’t realistic with persistent symptoms

    Comparison: I’ll compare all the options. Option A suggests she would feel better, which often isn’t realistic with persistent symptoms. Option B indicates she would schedule a follow-up appointment, but if she’s ignoring the symptoms, this doesn’t align with her behavior. Opt...

  58. [86]

    If she continues to ignore symptoms, it’s likely her situation will worsen instead of improving

    Divide: Breaking down the outcomes, I realize that not addressing health issues often leads to escalation. If she continues to ignore symptoms, it’s likely her situation will worsen instead of improving

  59. [87]

    By ignoring the symptoms, one possible severe consequence is a sudden health crisis necessitating emergency intervention

    Conquer: I need to construct a logical conclusion about the most serious possibility. By ignoring the symptoms, one possible severe consequence is a sudden health crisis necessitating emergency intervention

  60. [88]

    Considering realistic scenarios, ignoring serious symptoms often leads to drastic health declines rather than recovery

    Self-Refinement: Wait, I almost concluded that she might just feel better (Option A), but that’s an optimistic assumption. Considering realistic scenarios, ignoring serious symptoms often leads to drastic health declines rather than recovery. I need to reassess

  61. [89]

    Decison in Com2-main Question What interventions can prevent severe consequences during a hot air ballooning experience that could lead to disastrous outcomes? Options A

    Final Assessment: Conclusively, experiencing a sudden health crisis is the most likely negative outcome of ignoring persistent health issues. Decison in Com2-main Question What interventions can prevent severe consequences during a hot air ballooning experience that could lead...

  62. [90]

    I need to focus on preventing issues related to equipment failure and extreme weather

    Context Identification: I first recognize that the question revolves around potential interventions that could mitigate severe negative outcomes during a hot air ballooning experience. I need to focus on preventing issues related to equipment failure and extreme weather

  63. [91]

    Option A refers to equipment checks and could help avert malfunctions, while Option C emphasizes pilot training during severe conditions

    Systematic Analysis: I review each option in light of their potential to prevent accidents. Option A refers to equipment checks and could help avert malfunctions, while Option C emphasizes pilot training during severe conditions

  64. [92]

    Both suggest ignoring critical safety precautions and could lead to disastrous outcomes

    Comparison: I analyze options B and F. Both suggest ignoring critical safety precautions and could lead to disastrous outcomes. Option D seems reckless, as it suggests letting passengers take control without proper training

  65. [93]

    Grouping this way highlights which options can truly help prevent severe consequences

    Divide: Next, I break down the options into two groups: safety interventions and unsafe practices. Grouping this way highlights which options can truly help prevent severe consequences

  66. [94]

    Conquer: I take the valid interventions from the safe group (A, C, E) and recognize their importance in preventing negative outcomes associated with the hazards of hot air ballooning

  67. [95]

    Self-Refinement: I reassess my thought process, making certain that I haven’t overlooked any relevant options or context, confirming that my focus remains on preventing equipment failure and addressing pilot preparedness

  68. [96]

    Emphasizing Constraints: I remember that the question explicitly seeks interventions that will reduce severe outcomes, which validates the importance of option E about establishing no-fly zones

  69. [97]

    Method Reuse: I recall existing safety protocols in aviation, reaffirming that rigorous checks and pilot training are part of best practices in preventing accidents. Transition in Com2-main Question What is the first step to build a bookshelf from scratch? Options A) Paint or ...

  70. [98]

    In this case, the typical behavior of the coin changes significantly compared to a normal environment

    Systematic Analysis: I need to understand the scenario where the coin is in a zero-gravity environment. In this case, the typical behavior of the coin changes significantly compared to a normal environment. The inputs are the conditions of zero gravity, and the output will be ...

  71. [99]

    Option A suggests the coin lands on a table, which seems unlikely in zero gravity

    Comparison: I will compare the options provided. Option A suggests the coin lands on a table, which seems unlikely in zero gravity. Option C implies the coin rolls back into the person’s hand, which also doesn’t fit the zero-gravity context. Option D states the coin falls to t...

  72. [100]

    Divide: I can break down the options into two categories: those that align with zero gravity (B) and those that do not (A, C, D)

  73. [101]

    This aligns with the idea that the coin would continue to drift until it encounters something

    Conquer: Since options A, C, and D are not plausible in a zero-gravity scenario, I focus on option B, which suggests a collision with an experimental device. This aligns with the idea that the coin would continue to drift until it encounters something

  74. [102]

    I need to correct my reasoning

    Self-Refinement: Wait, I initially thought option A could be possible, but upon further analysis, I realize that in zero gravity, the coin cannot land on a table. I need to correct my reasoning

  75. [103]

    Therefore, the only logical outcome is that the coin would drift and potentially collide with something

    Context Identification: The context here is crucial; in a zero-gravity environment, objects do not fall but float. Therefore, the only logical outcome is that the coin would drift and potentially collide with something. Decision in Com2-hard Crime During the wedding breakfast ...

  76. [104]

    Simon was seen in good spirits before the wedding but became agitated during the ceremony

    Lady St. Simon was seen in good spirits before the wedding but became agitated during the ceremony

  77. [105]

    A woman named Flora Millar attempted to disrupt the wedding, claiming to have a connection to Lord St. Simon

  78. [106]

    Simon spoke to her maid, Alice, and mentioned ¨jumping a claim,ïndicating a prior relationship

    After the ceremony, Lady St. Simon spoke to her maid, Alice, and mentioned ¨jumping a claim,ïndicating a prior relationship

  79. [107]

    Simon left the breakfast table shortly after the meal began, dressed in her wedding attire, and was later seen leaving with Francis H

    Lady St. Simon left the breakfast table shortly after the meal began, dressed in her wedding attire, and was later seen leaving with Francis H. Moulton

  80. [108]

    Simon’s wedding dress and belongings in the Serpentine, indicating she had left in a hurry

    The police found Lady St. Simon’s wedding dress and belongings in the Serpentine, indicating she had left in a hurry. Question What actions could have been taken to prevent Lady St. Simon’s disappearance and the subsequent elopement? Options A) Conduct a thorough background ch...

  81. [109]

    elopement)

    Systematic Analysis: Analyze the situation by identifying the inputs (guests, bride, groom, and their relationships) and outputs (successful wedding vs. elopement). The constraints include the emotional state of the bride and the presence of Flora Millar

  82. [110]

    Options A, B, and D seem proactive, while C could limit the social aspect, and E is dismissive

    Comparison: Evaluate each option based on its potential effectiveness in preventing the crime. Options A, B, and D seem proactive, while C could limit the social aspect, and E is dismissive

  83. [111]

    Divide: Break down the problem into subproblems: the bride’s emotional state, the influence of Flora Millar, and the wedding environment

  84. [112]

    For example, ensuring the bride’s emotional well-being could prevent her from feeling the need to elope

    Conquer: Consider how addressing each subproblem could lead to a solution. For example, ensuring the bride’s emotional well-being could prevent her from feeling the need to elope

  85. [113]

    Self-Refinement: Reflect on the reasoning process to ensure all aspects of the situation are considered, particularly the influence of past relationships

  86. [114]

    Intervention in Com2-hard Crime A valuable coronet, containing thirty-nine beryls, was stolen from Alexander Holder’s home

    Context Identification: Summarize the context of the wedding, the bride’s background, and the presence of Flora Millar to understand the dynamics at play. Intervention in Com2-hard Crime A valuable coronet, containing thirty-nine beryls, was stolen from Alexander Holder’s home...

  87. [115]

    Alexander Holder saw his son, Arthur, holding the coronet in his hands when he entered the dressing room

  88. [116]

    Arthur was dressed only in his shirt and trousers, indicating he had been in bed prior to the incident

  89. [117]

    Mary Holder, Arthur’s cousin, was seen at the side window of the hall just before the incident occurred

  90. [118]

    Lucy Parr, a maid, was reported to have left the house shortly before the crime and returned through the back door

  91. [119]

    A note was found from Mary Holder, indicating she had left the house and would not return

  92. [120]

    Sir George Burnwell had been a frequent visitor to the Holder household and was known to have a questionable reputation

  93. [121]

    Arthur had previously expressed distress over his financial situation and had asked his father for money the night before the incident. Question Based on the facts and actions presented, who is (are) the criminal(s) involved in the theft of the coronet? Options A) Arthur Holde...

  94. [122]

    John Straker was last seen leaving his home to check on the horses late at night

  95. [123]

    A stable-boy, Ned Hunter, was drugged with opium-laced curried mutton, which was delivered to him by a maid

  96. [124]

    Straker’s overcoat was found on a furze-bush near the scene of the crime

  97. [125]

    Straker was found dead with a knife in his hand, which was identified as a cataract knife, not suitable for self-defense

  98. [126]

    A cravat belonging to Fitzroy Simpson was found in Straker’s hand, indicating a struggle

  99. [127]

    Fitzroy Simpson had a motive to harm Silver Blaze, as he had placed large bets against the horse

  100. [128]

    The police arrested Fitzroy Simpson based on circumstantial evidence, but he had no visible injuries from the struggle

  101. [129]

    Straker Correct Answer B) Fitzroy Simpson Slow Thinking

    The dog in the stables did not bark during the incident, suggesting the intruder was known to it.Question Based on the facts and actions presented, who is most likely to be the criminal responsible for John Straker’s death and the disappearance of Silver Blaze? Options A) John...

  102. [130]

    The inputs are the actions of the individuals involved, and the output is the identification of the criminal

    Systematic Analysis: The problem involves determining the criminal based on the actions and facts surrounding the murder of John Straker and the disappearance of Silver Blaze. The inputs are the actions of the individuals involved, and the output is the identification of the criminal

  103. [131]

    Method Reuse: The situation can be analyzed using classic detective reasoning, focusing on motives, means, and opportunities

  104. [132]

    - B) Fitzroy Simpson: He had a motive to harm Silver Blaze and was present at the scene, but he has no injuries and was arrested based on circumstantial evidence

    Comparison: - A) John Straker: He is the victim, so he cannot be the criminal. - B) Fitzroy Simpson: He had a motive to harm Silver Blaze and was present at the scene, but he has no injuries and was arrested based on circumstantial evidence. - C) The stable-boy, Ned Hunter: He...

  105. [133]

    Divide: The analysis can be divided into examining motives (financial gain), means (the knife and drugging), and opportunity (who was present at the scene)

  106. [134]

    Conquer: By evaluating each suspect’s actions and the evidence against them, we can determine who had the most opportunity and motive

  107. [135]

    Self-Refinement: The reasoning process indicates that Fitzroy Simpson had the strongest motive and opportunity, but the lack of direct evidence against him raises questions

  108. [136]

    Context Identification: The context involves a horse racing environment where financial stakes are high, and trainers may resort to unethical actions to secure wins

  109. [137]

    D Prompts for Evaluation Prompt for Com2-main: Direct, Intervention & Transition You are given a multi-choice question and a set of plausible options

    Emphasizing Constraints: The evidence must be strictly based on the actions and facts presented, without inferring beyond what is known. D Prompts for Evaluation Prompt for Com2-main: Direct, Intervention & Transition You are given a multi-choice question and a set of plausibl...

  110. [138]

    Abrupt changes can lead to severe economic disruptions and civil unrest

    Context Identification: I need to understand the implications of abrupt energy policy changes. Abrupt changes can lead to severe economic disruptions and civil unrest. I should consider the potential consequences of these disruptions

  111. [139]

    I need to identify which options could effectively prevent severe economic disruptions and civil unrest

    Systematic Analysis: I will analyze the options provided. I need to identify which options could effectively prevent severe economic disruptions and civil unrest

  112. [140]

    This could provide a stable energy supply during the transition, which seems beneficial

    Comparison: - Option A suggests maintaining and upgrading fossil fuel infrastructure. This could provide a stable energy supply during the transition, which seems beneficial. - Option B promotes gradual shifts in energy policy with continuous public engagement. This could help...

  113. [141]

    Divide: I will categorize the options into two groups: those that could prevent disruptions (A, B, D, F) and those that could exacerbate them (C, E)

  114. [142]

    Options A, B, D, and F seem to offer proactive measures to mitigate risks

    Conquer: I will focus on the options that could prevent disruptions. Options A, B, D, and F seem to offer proactive measures to mitigate risks

  115. [143]

    I will double-check the implications of each option

    Self-Refinement: I need to ensure that my reasoning aligns with the goal of preventing severe economic disruptions and civil unrest. I will double-check the implications of each option

  116. [144]

    Emphasizing Constraints: I must remember that the goal is to prevent disruptions, so I should prioritize options that address the immediate and long-term effects of energy policy changes. Answer: A), B), D), F) LLaMA-Vanilla Analysis: To prevent severe economic disruptions and...

  117. [2019]

    Li Du, Xiao Ding, Kai Xiong, Ting Liu, and Bing Qin

    Elg: an event logic graph.arXiv preprint arXiv:1907.08015. Li Du, Xiao Ding, Kai Xiong, Ting Liu, and Bing Qin

    1907 arXiv

  118. [2020]

    InProceedings of the 29th ACM in- ternational conference on information & knowledge management, pages 3023–3030

    Causenet: Towards a causality graph extracted from the web. InProceedings of the 29th ACM in- ternational conference on information & knowledge management, pages 3023–3030. Jinhao Jiang, Zhipeng Chen, Yingqian Min, Jie Chen, Xiaoxue Cheng, Jiapeng Wang, Yiru Tang, Haoxiang Sun...

  119. [2021]

    Excar: Event graph knowledge enhanced ex- plainable causal reasoning. InProceedings of the 59th Annual Meeting of the Association for Compu- tational Linguistics and the 11th International Joint Conference on Natural Language Processing (Vol- ume 1: Long Papers), pages 2354–23...

  120. [2022]

    InProceedings of the 60th Annual Meeting of the Association for Computational Lin- guistics (Volume 1: Long Papers), pages 432–446

    e-care: a new dataset for exploring explainable causal reasoning. InProceedings of the 60th Annual Meeting of the Association for Computational Lin- guistics (Volume 1: Long Papers), pages 432–446. Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenen- baum, and Igor Mordatch....

    2024 arXiv

  121. [2024]

    Journal of Machine Learning Research, 25(70):1–53

    Scaling instruction-finetuned language models. Journal of Machine Learning Research, 25(70):1–53. Xiao Ding, Zhongyang Li, Ting Liu, and Kuo Liao

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.