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arxiv: 2604.02733 · v1 · submitted 2026-04-03 · 💻 cs.AI

Recognition: 2 theorem links

· Lean Theorem

DeltaLogic: Minimal Premise Edits Reveal Belief-Revision Failures in Logical Reasoning Models

Amit Dhanda
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Pith reviewed 2026-05-13 20:21 UTC · model grok-4.3

classification 💻 cs.AI
keywords belief revisionlogical reasoningminimal premise editsDeltaLogicinertiamodel evaluationdynamic reasoning
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The pith

Logical competence on fixed premises does not guarantee correct belief revision after minimal evidence changes.

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

The paper introduces DeltaLogic as a way to turn existing reasoning benchmarks into short episodes that test revision. Each episode starts with a conclusion drawn from a premise set, applies one minimal change to those premises, and then checks whether the model keeps or updates its conclusion. Evaluations on models including Qwen variants and Phi-4 show that higher accuracy on the initial question does not produce higher accuracy on the revision step. Many models instead display inertia, keeping the old conclusion even when the gold label requires change, or fall into abstention. The results indicate that static logical inference and disciplined updating are separate capabilities.

Core claim

DeltaLogic converts natural-language reasoning examples into revision episodes: first derive a conclusion from premises P, then receive a minimal edit δ(P), and finally decide whether the conclusion should remain stable or be revised. On a 30-episode subset drawn from FOLIO and ProofWriter, Qwen3-1.7B reaches 0.667 initial accuracy yet only 0.467 revision accuracy with 0.600 inertia on change-required cases; similar inertial patterns appear in Qwen3-4B, while Phi-4-mini-instruct reaches 0.850 revision accuracy but still shows non-trivial abstention. The central observation is that logical competence under fixed premises does not imply disciplined belief revision after local evidence edits.

What carries the argument

DeltaLogic protocol that converts fixed-premise examples into minimal-edit revision episodes.

Load-bearing premise

The minimal premise edits produced by the protocol are objectively minimal and the gold revision labels are correct without further validation.

What would settle it

A large-scale human-validated collection of DeltaLogic episodes in which a model achieves comparably high accuracy on both initial conclusions and required revisions would undermine the decoupling claim.

Figures

Figures reproduced from arXiv: 2604.02733 by Amit Dhanda.

Figure 1
Figure 1. Figure 1: DeltaLogic construction pipeline. A standard reasoning item is turned into a minimally [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Failure-mode comparison across completed runs. The Qwen family remains inertia [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
read the original abstract

Reasoning benchmarks typically evaluate whether a model derives the correct answer from a fixed premise set, but they under-measure a closely related capability that matters in dynamic environments: belief revision under minimal evidence change. We introduce DeltaLogic, a benchmark transformation protocol that converts natural-language reasoning examples into short revision episodes. Each episode first asks for an initial conclusion under premises P, then applies a minimal edit {\delta}(P), and finally asks whether the previous conclusion should remain stable or be revised. We instantiate DeltaLogic from FOLIO and ProofWriter and evaluate small causal language models with constrained label scoring. On a completed 30-episode Qwen evaluation subset, stronger initial reasoning still does not imply stronger revision behavior: Qwen3-1.7B reaches 0.667 initial accuracy but only 0.467 revision accuracy, with inertia rising to 0.600 on episodes where the gold label should change, while Qwen3-0.6B collapses into near universal abstention. There, Qwen3-4B preserves the same inertial failure pattern (0.650 initial, 0.450 revised, 0.600 inertia), whereas Phi-4-mini-instruct is substantially stronger (0.950 initial, 0.850 revised) but still exhibits non-trivial abstention and control instability. These results suggest that logical competence under fixed premises does not imply disciplined belief revision after local evidence edits. DeltaLogic therefore targets a distinct and practically important reasoning capability that complements existing logical inference and belief-updating benchmarks.

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 DeltaLogic, a benchmark transformation protocol that converts static reasoning examples from FOLIO and ProofWriter into short revision episodes: an initial conclusion is derived from premises P, followed by a minimal edit δ(P), after which the model must decide whether the prior conclusion remains stable or requires revision. On a 30-episode subset, small causal LMs are evaluated with constrained label scoring; results show that higher initial accuracy (e.g., Qwen3-1.7B at 0.667) does not imply stronger revision performance (0.467), with elevated inertia (0.600) on cases where the gold label requires change. The central claim is that logical competence under fixed premises does not entail disciplined belief revision after local evidence edits.

Significance. If the DeltaLogic protocol can be shown to produce objectively minimal edits with unambiguous gold labels, the work would usefully identify a distinct capability gap in current models that static reasoning benchmarks miss, with practical relevance for dynamic environments. The empirical pattern across model scales (including Phi-4-mini-instruct's stronger but still imperfect revision) provides a concrete starting point for future work on belief-updating training objectives.

major comments (2)
  1. [DeltaLogic protocol and evaluation setup] The manuscript provides no details on the edit-generation procedure, human validation of minimality, or inter-annotator agreement for the gold revision labels in the DeltaLogic protocol (abstract and evaluation description). This is load-bearing for the central claim, because the reported inertia (0.600 on change cases for Qwen3-1.7B) and accuracy drops could reflect ambiguous or non-minimal edits rather than intrinsic model limitations.
  2. [Results on 30-episode Qwen evaluation subset] The evaluation uses only a 30-episode subset with no reported error bars, statistical significance tests, or confidence intervals for the accuracy figures (e.g., 0.667 initial vs. 0.467 revision). This small sample size limits the strength of the conclusion that initial competence does not imply revision competence.
minor comments (1)
  1. [Evaluation methodology] The term 'constrained label scoring' is used without an explicit definition or pseudocode in the abstract; a brief description of the allowed output format and scoring rule would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. The comments highlight important areas for improving the clarity and robustness of the DeltaLogic protocol and evaluation. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: The manuscript provides no details on the edit-generation procedure, human validation of minimality, or inter-annotator agreement for the gold revision labels in the DeltaLogic protocol (abstract and evaluation description). This is load-bearing for the central claim, because the reported inertia (0.600 on change cases for Qwen3-1.7B) and accuracy drops could reflect ambiguous or non-minimal edits rather than intrinsic model limitations.

    Authors: We agree that the submitted version does not elaborate the protocol construction sufficiently in the main text. The full manuscript contains an appendix describing the transformation from FOLIO and ProofWriter, but this was not cross-referenced clearly. In revision we will add a dedicated subsection in the main body that specifies the edit-generation procedure (minimal premise substitutions that flip the gold conclusion while preserving surface similarity), the human validation protocol (three annotators independently confirming minimality and label correctness), and inter-annotator agreement (Fleiss' kappa). This will directly address the concern that observed inertia might stem from ambiguous edits. revision: yes

  2. Referee: The evaluation uses only a 30-episode subset with no reported error bars, statistical significance tests, or confidence intervals for the accuracy figures (e.g., 0.667 initial vs. 0.467 revision). This small sample size limits the strength of the conclusion that initial competence does not imply revision competence.

    Authors: We acknowledge that the 30-episode subset is small and that the current manuscript omits uncertainty estimates and significance testing. In the revised version we will report bootstrap confidence intervals and standard errors for all accuracy and inertia figures, apply McNemar's test to compare initial versus revision performance, and explicitly frame the results as preliminary while noting the sample-size limitation. We will also indicate plans for scaling the evaluation in future work. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical evaluation against external gold labels

full rationale

The paper introduces DeltaLogic as a transformation protocol applied to existing external datasets (FOLIO and ProofWriter) to create revision episodes, then reports direct empirical accuracies (e.g., Qwen3-1.7B initial 0.667 vs. revision 0.467) by comparing model outputs to gold labels supplied by those source datasets. No load-bearing step involves a derivation, equation, fitted parameter, or self-citation that reduces the central claim to its own inputs by construction. The protocol defines minimal edits and stability/revision labels procedurally from the source data without internal fitting or renaming of prior author results, making the evaluation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that the generated minimal edits are faithful to the original reasoning task and that the constrained label scoring accurately reflects model belief revision.

axioms (1)
  • domain assumption Minimal premise edits can be defined such that they preserve the original reasoning structure while changing the correct conclusion
    Invoked when the protocol converts FOLIO and ProofWriter examples into revision episodes
invented entities (1)
  • DeltaLogic benchmark episodes no independent evidence
    purpose: To measure belief revision under minimal evidence change
    Newly constructed test items whose validity depends on the transformation protocol

pith-pipeline@v0.9.0 · 5573 in / 1160 out tokens · 31110 ms · 2026-05-13T20:21:46.588736+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

6 extracted references · 6 canonical work pages

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    LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models

    Parmar, M., Patel, N., Varshney, N., Nakamura, M., Luo, M., Mashetty, S., Mitra, A., and Baral, C. LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models. ACL 2024. https://aclanthology.org/2024.acl-long.739/

    work page 2024

  2. [2]

    Belief Revision: The Adaptability of Large Language Models Reasoning

    Wilie, B., Cahyawijaya, S., Ishii, E., He, J., and Fung, P. Belief Revision: The Adaptability of Large Language Models Reasoning. EMNLP 2024. https://aclanthology.org/2024.emnlp-main.586/

    work page 2024

  3. [3]

    LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models

    Gui, J., Liu, Y., Cheng, J., Gu, X., Liu, X., Wang, H., Dong, Y., Tang, J., and Huang, M. LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models. Findings of ACL 2025. https://aclanthology.org/2025.findings-acl.77/

    work page 2025

  4. [4]

    ReviseQA: A Benchmark for Belief Revision in Question Answering

    Yan, Y., et al. ReviseQA: A Benchmark for Belief Revision in Question Answering. ICML 2025

    work page 2025

  5. [5]

    FOLIO: Natural Language Reasoning with First-Order Logic

    Han, S., et al. FOLIO: Natural Language Reasoning with First-Order Logic. 2022. https://arxiv.org/abs/2209.00840

    work page arXiv 2022

  6. [6]

    ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language

    Tafjord, O., Dalvi, B., and Clark, P. ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language. EMNLP 2021

    work page 2021