Pith. sign in

REVIEW 4 major objections 5 minor 17 references

Reasoning is about giving reasons

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read Natural-language arguments can be reduced to a logical skeleton, and then reasoning is deterministic satisfiability.

desk verdict A clear, honest position paper on extracting task-specific logical structure from NL arguments; the extraction results are solid on template data, but the headline claims about general reasoning are under-supported and the RuleTakers numbers come from a filtered subset. read the letter →

arxiv 2508.14488 v1 pith:T4UI73HM submitted 2025-08-20 cs.CL

Krunal Shah , Dan Roth This is my paper
classification cs.CL
keywords naturallanguagereasoninglogicalstructureextractionrepresentationofsymbolicrule-baseddeductionabductioncontradictiondetection
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

The paper argues that the hard part of natural-language reasoning is not inference but representation: an argument made in text contains a 'local' logical structure, and once that structure is made explicit, deciding whether a query follows is ordinary, deterministic satisfiability. To carry this claim, the paper defines the Representation of Logical Structure (RLS), a task-dependent encoding of an argument's logical atoms and the rules connecting them, and trains a text-to-text transformer to extract RLS from individual sentences. Across three benchmarks the extraction reaches 95.9% exact match on CLUTRR and over 99% on Leap-of-Thought, and feeding extracted RLS to a symbolic solver gives competitive accuracy on RuleTakers, with 100% on the depth-5 split even though the extractor was trained on depth-3 sentences. The payoff, if the claim holds, is that explanation generation, abduction, contradiction detection, arbitrary-depth deduction, and interactive correction all become by-products of the same extracted structure rather than separate trained behaviors.

What carries the argument

The RLS is a tuple of ground logical literals—(entity, relation, property, polarity) for attribute rules, (person, relation, person) for kinship—joined by conjunction for facts and implication for rules. It is task-dependent rather than a full semantic parse, which is what lets the paper bypass the claim that natural language is too expressive to map into logic: only the information relevant to the reasoning task is encoded, so information loss that does not affect the argument is acceptable. In the pipeline, a text-to-text transformer is trained to predict an encoded form of the RLS from each sentence, and a deterministic satisfiability engine performs inference over the extracted theory. T

What would settle it

Run the trained extractor on a held-out set of natural-language arguments, assemble each sentence's RLS into a theory, and ask the symbolic solver for the truth value of the query. Then check the theory by hand: if any premise or rule that appears in the text is missing from the RLS, appears with the wrong polarity, or is conflated with a different atom, and the solver's answer changes as a result, the faithfulness of the RLS is falsified. A more targeted version: take two paraphrases that human readers judge to be logically equivalent and verify that their extracted theories produce identical

Watch

Extended reading notes

Core claim

The central discovery is that the logical content of a natural-language argument can be separated from the rest of the text and written as a small propositional theory: ground literals such as (Harry, is, young, +) connected by conjunction, and rules of the form antecedent atoms imply a consequent atom. The paper calls this extracted theory the RLS, and claims that once it is available every form of reasoning that depends on an argument's logical structure—deduction, abduction, contradiction detection, implication enumeration—is deterministic and easy to compute. The experimental contribution is to show that a sequence-to-sequence transformer can produce RLS from natural-language sentences a

Load-bearing premise

The claim collapses if a natural-language argument does not have a local propositional logical structure that can be extracted into ground atom tuples, conjunction, and implication without losing the information needed to answer the query.

Editorial extensions

If this is right

  • A system trained on shallow rule-based arguments can answer deeper queries (depth 5) without retraining, since the symbolic solver, not the extractor, carries the chain length.
  • Contradiction detection and abduction become evaluations of satisfiability and entailment over the same extracted RLS, so no new training data or model is needed for those tasks.
  • If the solver's unification is weak rather than exact, implicit equivalences between phrases (for instance 'has gold' and 'has a heart of gold') become visible as explicit proof steps, making hidden assumptions inspectable.
  • An observed extraction error can be corrected by editing the theory and re-running the solver, giving an interactive mechanism that end-to-end models lack.

Reading between the lines

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

  • A natural next stress test is to extend RLS extraction from single sentences to whole multi-sentence arguments; the paper leaves context-level extraction to future work, and that transition is where the faithfulness assumption is most likely to be strained.
  • Because RLS is task-dependent, two reasoning tasks over the same text may require different RLS formulations; whether one extractor can serve both, or must be retrained per task, is a testable question the paper's efficiency condition leaves open.
  • The propositional restriction suggests a concrete boundary: adding quantifiers to the arguments would force the extractor to emit variables and scope, and the solver to move from SAT to theorem proving, so the current approach's reach is limited to arguments that can be faithfully propositionalized.
  • If extraction accuracy holds up on longer, more natural prose, the pipeline could be used as an auditing tool where a human checks only the extracted atoms and the solver's proof, rather than rereading the full text.
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 / 5 minor

Summary. The paper proposes an intermediate representation, the Representation of Logical Structure (RLS), which maps natural-language statements in a reasoning task to propositional logical atoms (tuples of words/phrases with polarity) and rules. Reasoning is then delegated to a deterministic symbolic engine (ProbLog). RLS is extracted with a T5 sequence-to-sequence model. Experiments report 95.9% exact-match accuracy on CLUTRR and 99.6/99.8% on LEAP-of-Thought, and competitive or perfect answer accuracies on RuleTakers ParaRules, D5, and Birds-Electricity. The paper claims the approach supports deterministic reasoning, explanations, abduction, contradiction detection, and interactive correction.

Significance. The central idea—separating logical-structure extraction from deterministic symbolic reasoning—is a valuable and still timely framing, and the paper explicitly formalizes RLS as a task-dependent representation that only needs to preserve inference-relevant content. The extraction results on CLUTRR and LEAP-of-Thought are strong, and the pipeline is transparent: once the representation is extracted, the solver's proof is faithful by construction. The honest discussion of failure cases (Figure 3) and the acknowledgment that the work is an unpublished 2021 manuscript are assets. However, the paper as written does not establish the load-bearing claim that the extracted RLS is a faithful and complete encoding of natural-language arguments beyond the template/benchmark-specific canonicalizations. The abstract's broader promises—abduction, contradictions, interactive discussion, arbitrary depths on general NL—are not supported by experiments. A careful scope revision or additional validation is needed before the central claim can be considered established.

major comments (4)
  1. [§3.1, §4.2, Table 1, Table 2] The RLS definition requires that replacing a statement by its RLS preserves the result of every inference chain (semantic adequacy). This condition is never directly verified. Instead, the RLS targets in Table 1 are 'easily generated from the accompanying metadata' (Section 4.2), so the 95–99% exact-match accuracies in Table 2 show that T5 can reproduce the dataset generators' annotations, not that the extracted tuples are complete, faithful encodings of the NL argument. The paper needs an independent evaluation of RLS adequacy, e.g., human-annotated RLS on held-out natural paraphrases, or at least a demonstration that errors in extraction align with errors in downstream reasoning across a broad sample.
  2. [§4.3, Figure 3, §2.1] The symbolic reasoner uses exact matching on surface-form atoms. Figure 3 explicitly shows a failure on Birds because 'can fly' and 'is airborne' are not recognized as equivalent, and the text admits that a 'smarter unification operator' would be needed. The weak-unification idea in Section 2.1 is not implemented or evaluated. Consequently, the claim that 'once RLS is extracted, reasoning is deterministic and easy' holds only for the canonicalized language of the benchmarks, not for general natural-language arguments with lexical paraphrase. This is a load-bearing gap: a valid NL argument expressing the same property with different wording would yield different RLS atoms and the solver would fail.
  3. [Tables 3–5, footnotes 4–5] The RuleTakers results are reported on the subset of the test set where ProbLog did not throw an exception on the corresponding gold theories. This filtering removes data points and can bias comparisons with ProofWriter and PRover. The perfect 100% result on D5 (Table 4) is on this filtered, template-generated subset. The paper should report results on the full test set or quantify the fraction and type of excluded instances, and discuss how the filtering affects the comparison.
  4. [§2.3.1, §5.2.2, Abstract] The paper claims support for abduction, contradiction detection, on-the-fly mistake rectification, and interactive discussion without additional training. No experiments implement or evaluate these tasks. Section 5.2.2 says perfect extraction 'highlights how the system can be easily extended' to abduction, but this is an extrapolation. These capabilities are central to the paper's motivation and appear in the abstract; either implement them on the RuleTakers-style datasets or explicitly scope the contributions to deductive rule-based reasoning and explanation generation.
minor comments (5)
  1. [§3] The section heading contains a typo: 'Represention of Logical Structure' should be 'Representation of Logical Structure'.
  2. [Table 3 caption] Typo: 'SToA' should likely read 'SOTA'.
  3. [§5.2.2] The claim that perfect answer accuracy on D5 implies perfect representation extraction is too quick: exact-match extraction accuracy is not reported for RuleTakers, and answer accuracy alone does not imply the extracted proof structure is exactly the intended one.
  4. [§4.2] The RuleTakers section describes D3/D5/ParaRules/Birds-Electricity but does not give dataset sizes or the train/test split for D3; adding these details would improve reproducibility.
  5. [Footnote 2] The note that the paper was written in 2021 and never published is useful context, but the informal wording ('It suggests...') is out of place in a formal submission.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: answers are computed by a deterministic external solver from RLS extracted by a model supervised on dataset metadata (not on query answers); the flagged caveats (unverified adequacy condition, exact-string atoms) are representational limits, not circular steps.

full rationale

The paper's derivation chain is NL sentence -> RLS (T5, supervised on dataset metadata per Table 1 and Section 4.2) -> answer (ProbLog, a fixed external solver, Section 4.3). Query answers are never used as training targets for the extractor, so the end-task accuracies in Tables 3-5 are compositional consequences of extraction quality rather than fitted predictions. The paper explicitly concedes that 'Once the logical structure of an argument is given, the computational problem is deterministic and easy' (Section 1; Section 2.2), so the empirical load rests on extraction accuracy, which is honestly reported as exact match against gold RLS generated from the datasets' own annotations. There are no self-citations (the reference list contains no work by Shah or Roth) and no imported uniqueness theorems; the weak-unification remedy is explicitly deferred to future work (Section 2.3, Figure 3). The flagged weaknesses are representational rather than circular: the adequacy condition defining RLS (Section 3.1, 'for any inference/reasoning chain... evaluates to the same result as with S') is never directly tested, and because the RLS is read off the same generator metadata used to build the datasets, the 95-99% exact-match scores demonstrate that T5 can invert the generators, not that the RLS formulation is uniquely faithful for arbitrary NL arguments. The exact-string atoms produce a documented failure on 'can fly' vs 'is airborne' paraphrase (Figure 3), and Table 3 reports on the ProbLog-parseable subset only (footnote 4). These limit the generality of the central claim but do not make the derivation equivalent to its inputs.

Assumptions & free parameters 2 free parameters · 5 assumptions · 1 invented entities

The central claim rests on the learned RLS extractor and the assumption that the hand-designed logical vocabulary preserves all reasoning-relevant information. The model parameters are fitted to the datasets, and the evaluation depends on the metadata-derived gold annotations.

free parameters (2)
  • T5 encoder-decoder weights = learned on each dataset split (not released)
    The RLS extraction model is a trained transformer; all reported extraction and reasoning accuracies depend on these fitted parameters.
  • Hand-designed RLS encoding format and token vocabulary = chosen by authors per dataset (Table 1)
    The choice of which tuples, polarities, and connective symbols constitute the logical structure is a modeling decision that determines what the model can output and what counts as exact match.
assumptions (5)
  • ad hoc to paper A 'local' logical structure exists in natural language arguments and can be extracted without full semantic parsing
    Section 2.1 states the method relies on this weaker assumption instead of attempting complete NL-to-formal mapping.
  • domain assumption Propositional logic suffices for the targeted reasoning tasks; quantifiers and higher-order structure are excluded
    Footnote 1 and Section 3.2 restrict the formalism to ground literals, conjunction, and implication.
  • domain assumption Dataset metadata and templates provide faithful gold RLS without annotation effort
    Section 4.2 says RLS is generated from accompanying metadata; if these annotations are not faithful, the accuracy numbers are not meaningful.
  • domain assumption Closed world assumption for query answering
    Section 3.3.2 defines the task under the closed world assumption.
  • standard math SAT solving / ProbLog is sound and complete over the extracted propositional theories
    Section 2.2 treats propositional reasoning as a solved problem with efficient solvers.
invented entities (1)
  • Representation of Logical Structure (RLS) independent evidence
    purpose: Intermediate logical form for NL reasoning sentences
    RLS is evaluated by exact match against gold representations derived from dataset metadata, giving an observable benchmark; however, it has no independently validated semantics outside the datasets.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Reasoning is about giving reasons." pith.science (2026). https://pith.science/paper/T4UI73HM

@misc{pith2026250814488,
  author       = {Pith},
  title        = {Pith review of: Reasoning is about giving reasons},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T4UI73HM}},
  note         = {Machine review of arXiv:2508.14488}
}
read the original abstract

Convincing someone of the truth value of a premise requires understanding and articulating the core logical structure of the argument which proves or disproves the premise. Understanding the logical structure of an argument refers to understanding the underlying "reasons" which make up the proof or disproof of the premise - as a function of the "logical atoms" in the argument. While it has been shown that transformers can "chain" rules to derive simple arguments, the challenge of articulating the "reasons" remains. Not only do current approaches to chaining rules suffer in terms of their interpretability, they are also quite constrained in their ability to accommodate extensions to theoretically equivalent reasoning tasks - a model trained to chain rules cannot support abduction or identify contradictions. In this work we suggest addressing these shortcomings by identifying an intermediate representation (which we call the Representation of the Logical Structure (RLS) of the argument) that possesses an understanding of the logical structure of a natural language argument - the logical atoms in the argument and the rules incorporating them. Given the logical structure, reasoning is deterministic and easy to compute. Therefore, our approach supports all forms of reasoning that depend on the logical structure of the natural language argument, including arbitrary depths of reasoning, on-the-fly mistake rectification and interactive discussion with respect to an argument. We show that we can identify and extract the logical structure of natural language arguments in three popular reasoning datasets with high accuracies, thus supporting explanation generation and extending the reasoning capabilities significantly.

Figures

Figures reproduced from arXiv: 2508.14488 by the authors.

Figure 1
Figure 1. An example of an argument from the RULE￾TAKERS (Clark et al., 2020) dataset. The goal is to determine the truth value of the query given some facts and rules (that all together constitute an argument). The logical structure of the argument at the bottom conveys a deeper understanding of the reasoning process, simpli￾fies the verification process, and supports reasoning on “What if Harry was young but not nice?" and … view at source ↗
Figure 2
Figure 2. In the above inference, if the weak unification [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. An example mistake of our method on the Birds dataset, highlighting a shortcoming of our current approach. However, we note that this specific mistake can be addressed by using a smarter unification operator as part of the symbolic reasoner which can determine whether two phrases are effectively equivalent. 5.2.3 Zero shot generalization To demonstrate the generalization ability of our deductive reasoning method to … view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

17 extracted references · 13 canonical work pages

  1. [1]

    Eugene Charniak. 1973. Jack and janet in search of a theory of knowledge. In IJCAI

    work page 1973

  2. [2]

    Peter Clark, Oyvind Tafjord, and Kyle Richardson. 2020. https://doi.org/10.24963/ijcai.2020/537 Transformers as soft reasoners over language . In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20 , pages 3882--3890. International Joint Conferences on Artificial Intelligence Organization. Main track

    doi:10.24963/ijcai.2020/537 2020

  3. [3]

    Luc De Raedt, Angelika Kimmig, and Hannu Toivonen. 2007. Problog: A probabilistic prolog and its application in link discovery. In Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI'07, page 2468–2473, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc

    work page 2007

  4. [4]

    Gomes, Henry A

    C. Gomes, Henry A. Kautz, Ashish Sabharwal, and B. Selman. 2008. Satisfiability solvers. In Handbook of Knowledge Representation

    work page 2008

  5. [5]

    J. Gu, P. W. Purdom, J. Franco, and B. Wah. 1996. Algorithms for the satisfiability (sat) problem: A survey. In Satisfiability Problem: Theory and Applications

    work page 1996

  6. [6]

    P. Hayes. 1977. In defense of logic. In IJCAI

    work page 1977

  7. [7]

    J. R. Hobbs, M. Stickel, P. Martin, and D. Edwards. 1988. Interpretation as abduction. In Proceedings of the 26th Annual Meeting of the Association for Computational Linguistics (ACL)

    work page 1988

  8. [8]

    Mike Lewis and Mark Steedman. 2013. https://doi.org/10.1162/tacl_a_00219 Combined distributional and logical semantics . Transactions of the Association for Computational Linguistics, 1:179--192

    work page doi:10.1162/tacl_a_00219 2013

Show all 17 references
  1. [9]

    John McCarthy. 1963. Programs with common sense . Defense Technical Information Center

    1963

  2. [10]

    John McCarthy. 1976. An example for natural language understanding and the AI problems it raises . Formalizing Common Sense: Papers by John McCarthy, 355

    1976

  3. [11]

    John McCarthy. 1986. https://doi.org/10.1016/0004-3702(86)90032-9 Applications of circumscription to formalizing common-sense knowledge . Artif. Intell., 28(1):89–116

    1986 doi

  4. [12]

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. http://jmlr.org/papers/v21/20-074.html Exploring the limits of transfer learning with a unified text-to-text transformer . Journal of Machine Lea...

    2020

  5. [13]

    Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, and Mohit Bansal. 2020. https://doi.org/10.18653/v1/2020.emnlp-main.9 PR over: Proof generation for interpretable reasoning over rules . In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing...

    2020 doi

  6. [14]

    Hamilton

    Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L. Hamilton. 2019. https://doi.org/10.18653/v1/D19-1458 CLUTRR : A diagnostic benchmark for inductive reasoning from text . In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Proces...

    2019 doi

  7. [15]

    Oyvind Tafjord, Bhavana Dalvi Mishra, and Peter Clark. 2020. http://arxiv.org/abs/2012.13048 Proofwriter: Generating implications, proofs, and abductive statements over natural language . CoRR, abs/2012.13048

    2020 arXiv

  8. [16]

    Alon Talmor, Oyvind Tafjord, Peter Clark, Yoav Goldberg, and Jonathan Berant. 2020. https://proceedings.neurips.cc/paper/2020/file/e992111e4ab9985366e806733383bd8c-Paper.pdf Leap-of-thought: Teaching pre-trained models to systematically reason over implicit knowledge . In Adva...

    2020

  9. [17]

    u nchmeyer, Ulf Leser, and Tim Rockt \

    Leon Weber, Pasquale Minervini, Jannes M \"u nchmeyer, Ulf Leser, and Tim Rockt \"a schel. 2019. https://doi.org/10.18653/v1/P19-1618 NLP rolog: Reasoning with weak unification for question answering in natural language . In Proceedings of the 57th Annual Meeting of the Associ...

    2019 doi

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.