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arxiv: 2601.16504 · v3 · submitted 2026-01-23 · 💻 cs.CL · cs.AI

LOGICAL-COMMONSENSEQA: A Benchmark for Logical Commonsense Reasoning

Pith reviewed 2026-05-16 12:24 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords commonsense reasoninglogical compositionnegation handlinglanguage model evaluationplausibility judgmentscompositional reasoningAI benchmarks
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The pith

A new benchmark shows models handle conjunction in commonsense reasoning but degrade sharply on negation.

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

The paper introduces LOGICAL-COMMONSENSEQA, a benchmark that reframes commonsense reasoning as logical composition of atomic statements using operators AND for jointly plausible, OR for at least one plausible, and NEITHER/NOR for jointly implausible. This setup tests whether models can evaluate multiple interpretations together rather than selecting a single answer. Evaluations of instruction-tuned, reasoning-specialized, and fine-tuned models under zero-shot, few-shot, and chain-of-thought prompting reveal reasonable performance on conjunctive questions, moderate performance on disjunctive ones, and sharp degradation on negation-based questions. The work matters because everyday commonsense often requires judging compatibility, exclusivity, or joint implausibility among statements instead of isolated facts. It supplies a controlled testbed to diagnose these gaps and drive progress in compositional reasoning.

Core claim

The paper presents LOGICAL-COMMONSENSEQA as a benchmark that evaluates commonsense reasoning through logical composition over pairs of atomic statements using plausibility-level operators AND, OR, and NEITHER/NOR. Testing across model types and prompting methods shows reasonable performance on conjunctive reasoning, moderate performance on disjunctive reasoning, and sharp degradation on negation-based questions, thereby exposing fundamental reasoning limitations in current systems.

What carries the argument

LOGICAL-COMMONSENSEQA benchmark, which applies AND, OR, and NEITHER/NOR operators to pairs of atomic statements to test joint plausibility judgments.

If this is right

  • Models need specific improvements for negation handling to match their conjunctive performance.
  • Chain-of-thought prompting provides only limited mitigation for the observed degradation on negation tasks.
  • The benchmark enables tracking of progress on compositional commonsense capabilities over time.
  • Fine-tuning on logical operator compositions may be required to address the performance gap.
  • Multi-label evaluation of plausibility exposes weaknesses that single-answer benchmarks hide.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training data likely contains fewer negated commonsense statements, contributing to the sharp performance drop.
  • Human performance baselines on the same questions would quantify the remaining gap to human-level reasoning.
  • The operator-based framing could extend to other reasoning domains such as causal or temporal inference.
  • Longer chains of logical compositions beyond pairs might reveal additional model limitations.

Load-bearing premise

The logical operators AND, OR, and NEITHER/NOR applied to pairs of atomic statements accurately capture the structure of commonsense reasoning and plausibility judgments in natural language.

What would settle it

A model achieving comparable accuracy on negation-based questions to its accuracy on conjunctive questions, or human raters assigning different plausibility labels to the statement pairs than the logical operators do.

read the original abstract

Commonsense reasoning often involves evaluating multiple plausible interpretations rather than selecting a single atomic answer, yet most benchmarks rely on single-label evaluation, obscuring whether statements are jointly plausible, mutually exclusive, or jointly implausible. We introduce LOGICAL-COMMONSENSEQA, a benchmark that reframes commonsense reasoning as logical composition over pairs of atomic statements using plausibility-level operators (AND, OR and NEITHER/NOR). Evaluating instruction-tuned, reasoning-specialized, and fine-tuned models under zero-shot, few-shot, and chain-of-thought prompting, we find that while models perform reasonably on conjunctive and moderately on disjunctive reasoning, performance degrades sharply on negation-based questions. LOGICAL-COMMONSENSEQA exposes fundamental reasoning limitations and provides a controlled framework for advancing compositional commonsense reasoning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces LOGICAL-COMMONSENSEQA, a benchmark that reframes commonsense reasoning as logical composition over pairs of atomic plausibility statements using the operators AND, OR, and NEITHER/NOR. It evaluates instruction-tuned, reasoning-specialized, and fine-tuned models under zero-shot, few-shot, and chain-of-thought prompting, reporting reasonable performance on conjunctive reasoning, moderate performance on disjunctive reasoning, and sharp degradation on negation-based questions.

Significance. If the benchmark construction and results are substantiated, the work provides a controlled framework for diagnosing compositional limitations in language models, particularly with negation, which could guide future improvements in reasoning capabilities. The explicit operationalization via logical operators on atomic statements is a clear strength over single-label benchmarks.

major comments (2)
  1. [Dataset construction] Dataset construction section: no statistics on the total number of questions, distribution across logical operators, selection criteria for atomic statements, or validation process (e.g., human plausibility ratings or inter-annotator agreement) are provided, which directly affects whether the reported performance patterns can be trusted as measuring the intended reasoning capabilities.
  2. [Results] Results section: performance degradation on negation-based questions is described as 'sharp' without error bars, standard deviations, confidence intervals, or statistical significance tests comparing operators, making it impossible to determine if the difference is robust or could arise from sampling variance.
minor comments (1)
  1. [Abstract] The abstract and introduction would benefit from a brief concrete example illustrating how NEITHER/NOR is applied to a pair of atomic statements.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our benchmark paper. The comments highlight important areas for improving clarity and rigor in dataset documentation and results reporting. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses
  1. Referee: [Dataset construction] Dataset construction section: no statistics on the total number of questions, distribution across logical operators, selection criteria for atomic statements, or validation process (e.g., human plausibility ratings or inter-annotator agreement) are provided, which directly affects whether the reported performance patterns can be trusted as measuring the intended reasoning capabilities.

    Authors: We agree that the main Dataset construction section would benefit from explicit inclusion of these details to support reproducibility and interpretation of results. In the revised manuscript, we will expand this section to report the total number of questions, the distribution across logical operators, the selection criteria for atomic statements, and the validation process including human plausibility ratings and inter-annotator agreement. These additions will be presented in the main text with a summary table for accessibility. revision: yes

  2. Referee: [Results] Results section: performance degradation on negation-based questions is described as 'sharp' without error bars, standard deviations, confidence intervals, or statistical significance tests comparing operators, making it impossible to determine if the difference is robust or could arise from sampling variance.

    Authors: We concur that statistical support is necessary to substantiate the performance differences. In the revised manuscript, we will update the Results section to include error bars on all performance figures, report standard deviations from repeated evaluations, provide confidence intervals, and include statistical significance tests (e.g., paired t-tests or McNemar's test) comparing performance across the AND, OR, and NEITHER/NOR operators. This will allow readers to assess the robustness of the observed degradation on negation-based questions. revision: yes

Circularity Check

0 steps flagged

No significant circularity: benchmark definition and empirical evaluation are independent

full rationale

The paper introduces LOGICAL-COMMONSENSEQA as a new benchmark that composes atomic plausibility judgments under AND/OR/NEITHER-NOR operators and reports direct empirical results on existing models under zero/few-shot and CoT prompting. No equations, fitted parameters, derivations, or self-citations are present that would reduce any claim to prior outputs by construction. The performance degradation on negation is a measured outcome on the new dataset, not a prediction derived from the benchmark definition itself. This is a standard benchmark paper with self-contained empirical content.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that logical operators applied to statement pairs faithfully represent commonsense plausibility judgments; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Logical operators AND, OR, and NEITHER/NOR applied to pairs of atomic statements can be used to evaluate commonsense reasoning.
    This framing is the core of the benchmark definition in the abstract.

pith-pipeline@v0.9.0 · 5429 in / 1163 out tokens · 29106 ms · 2026-05-16T12:24:07.146599+00:00 · methodology

discussion (0)

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