arxiv: 2603.17432 · v3 · submitted 2026-03-18 · 💻 cs.CL

Recognition: 2 theorem links

· Lean Theorem

Argument Reconstruction as Supervision for Critical Thinking in LLMs

Hyun Ryu , Gyouk Chu , Gregor Betz , Eunho Yang , Carolyn Rose , Sean Welleck

Authors on Pith no claims yet

Pith reviewed 2026-05-15 10:22 UTC · model grok-4.3

classification 💻 cs.CL

keywords argument reconstructioncritical thinkinglarge language modelsreasoning supervisionsynthetic datasetsGAAR engineinference making

0 comments

The pith

Training LLMs to reconstruct arguments improves their performance on critical thinking tasks.

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

The paper asks whether LLMs become better at critical thinking when they are explicitly trained to reconstruct arguments, the way human students learn to make hidden inferences explicit. It presents an automatic engine that turns any argument into a clear reconstruction and uses that engine to build a new dataset of such reconstructions. When LLMs are trained on the reconstructions, they outperform models trained without them on seven separate critical thinking benchmarks, and the gains are largest when the new dataset is used. The work therefore treats argument reconstruction itself as a form of supervision that strengthens reasoning ability.

Core claim

Models trained to reconstruct arguments with the GAAR engine and the resulting Arguinas dataset outperform models that receive no such training across seven critical thinking tasks, with the largest gains coming from the new dataset.

What carries the argument

The GAAR engine, an automatic system that reconstructs arbitrary arguments by surfacing their underlying inferences.

If this is right

Argument reconstruction can serve as a single training signal that transfers to multiple downstream reasoning tasks.
Synthetic datasets built automatically from existing arguments can provide high-quality supervision for reasoning skills.
Explicit reconstruction training may reduce reliance on surface patterns in favor of deeper inference steps.
The same engine that creates training data can also be used to inspect or debug model outputs on new arguments.

Where Pith is reading between the lines

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

If reconstruction training works, future models might be improved by inserting reconstruction steps into their inference pipelines rather than only at training time.
The approach suggests that many reasoning failures in LLMs stem from unstated inferences that become visible once reconstruction is required.
Similar reconstruction-based supervision could be tested on tasks outside the seven studied here, such as legal or scientific argument evaluation.

Load-bearing premise

The arguments reconstructed by the GAAR engine accurately capture the original inferences without introducing systematic errors or biases.

What would settle it

A new set of critical thinking tasks on which models trained with GAAR reconstructions show no improvement or perform worse than models trained without them would falsify the claim.

Figures

Figures reproduced from arXiv: 2603.17432 by Carolyn Rose, Eunho Yang, Gregor Betz, Gyouk Chu, Hyun Ryu, Sean Welleck.

**Figure 2.** Figure 2: An overview of Generalized Automatic Argument Reconstruction (GAAR). [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Impact of downstream data size in pre-adaptive finetuning compared to direct [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Average downstream task performance with different Arguinas data composition [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: Average downstream task performance with different argument reconstruction quality under the pre-adaptive finetuning. Impact of Argument Reconstruction Quality. To verify that improvements in argument reconstruction quality contribute to downstream gains, we construct two lower-quality argument reconstruction datasets and compare their downstream effects with our method ( [PITH_FULL_IMAGE:figures/full_… view at source ↗

**Figure 6.** Figure 6: General Argument Types and Their Reconstructions. [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

**Figure 7.** Figure 7: Specific Argument Types. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗

**Figure 8.** Figure 8: Specific Argument Types. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗

**Figure 9.** Figure 9: Specific Argument Types and Their Reconstructions. [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗

**Figure 10.** Figure 10: Topics covered by arguments in the Arguinas dataset. [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗

**Figure 11.** Figure 11: Argument from Pros-and-cons-1950 and its reconstruction. For better under [PITH_FULL_IMAGE:figures/full_fig_p024_11.png] view at source ↗

**Figure 12.** Figure 12: Argument from Pros-and-cons-2010 and its reconstruction. For better under [PITH_FULL_IMAGE:figures/full_fig_p025_12.png] view at source ↗

**Figure 13.** Figure 13: Argument from NYT-room-for-debate and its reconstruction. For better un [PITH_FULL_IMAGE:figures/full_fig_p026_13.png] view at source ↗

**Figure 14.** Figure 14: Argument with informal fallacy and its reconstruction. For better understanding, [PITH_FULL_IMAGE:figures/full_fig_p027_14.png] view at source ↗

**Figure 15.** Figure 15: Argument with formal fallacy and its reconstruction. For better understanding, [PITH_FULL_IMAGE:figures/full_fig_p028_15.png] view at source ↗

**Figure 16.** Figure 16: Prompt used for reconstructing an argument and formalizing the reconstruction. [PITH_FULL_IMAGE:figures/full_fig_p034_16.png] view at source ↗

**Figure 17.** Figure 17: Prompt used for validity judgment and premise pruning. [PITH_FULL_IMAGE:figures/full_fig_p035_17.png] view at source ↗

**Figure 18.** Figure 18: Python code snippet for validity judgment and premise pruning. [PITH_FULL_IMAGE:figures/full_fig_p036_18.png] view at source ↗

**Figure 19.** Figure 19: Prompt used for streamlining. 37 [PITH_FULL_IMAGE:figures/full_fig_p037_19.png] view at source ↗

**Figure 20.** Figure 20: Prompt used for faithfulness judgment. 38 [PITH_FULL_IMAGE:figures/full_fig_p038_20.png] view at source ↗

**Figure 21.** Figure 21: Prompt used for reconstructing an argument (LLM Prompting). [PITH_FULL_IMAGE:figures/full_fig_p039_21.png] view at source ↗

**Figure 22.** Figure 22: Prompt used for evaluating validity (LLM Prompting). [PITH_FULL_IMAGE:figures/full_fig_p040_22.png] view at source ↗

**Figure 23.** Figure 23: Prompt used for pairwise faithfulness judgment (1/2). [PITH_FULL_IMAGE:figures/full_fig_p041_23.png] view at source ↗

**Figure 24.** Figure 24: Prompt used for pairwise faithfulness judgment (2/2). [PITH_FULL_IMAGE:figures/full_fig_p042_24.png] view at source ↗

read the original abstract

To think critically about arguments, human learners are trained to identify, reconstruct, and evaluate arguments. Argument reconstruction is especially important because it makes an argument's underlying inferences explicit. However, it remains unclear whether LLMs can similarly enhance their critical thinking ability by learning to reconstruct arguments. To address this question, we introduce a holistic framework with three contributions. We (1) propose an engine that automatically reconstructs arbitrary arguments (GAAR), (2) synthesize a new high-quality argument reconstruction dataset (Arguinas) using the GAAR engine, and (3) investigate whether learning argument reconstruction benefits downstream critical thinking tasks. Our experimental results show that, across seven critical thinking tasks, models trained to learn argument reconstruction outperform models that do not, with the largest performance gains observed when training on the proposed Arguinas dataset.

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.

Desk Editor's Note private letter to a colleague

The paper's core idea of using automatic argument reconstruction as LLM supervision is worth testing, but the results hinge on unvalidated synthetic data with no fidelity checks.

read the letter

The main point is that training on argument reconstruction via their GAAR engine and the new Arguinas dataset leads to better results on seven critical thinking tasks, with the biggest lifts coming from the synthetic data. This frames reconstruction as a scalable supervision signal rather than just an analysis tool, which is a reasonable extension of prior argument mining work. The setup makes sense on paper: humans improve at critical thinking by making inferences explicit, so why not try the same with models. They deliver a concrete engine, a generated dataset, and some empirical comparisons, which is more than just a conceptual proposal. That part earns credit for turning the idea into something testable. The soft spots sit right at the center though. The abstract reports outperformance but gives no experimental details on baselines, statistical controls, or how the seven tasks were chosen and measured. More importantly, there is no reported human validation or inter-annotator agreement on the GAAR reconstructions themselves. If the engine systematically drops implicit premises or introduces its own regularities, the downstream gains could simply reflect models learning those artifacts instead of genuine inference structure. That assumption is load-bearing and currently unsupported. The work is aimed at researchers who build reasoning pipelines or supervision methods for LLMs. Someone already working on argument mining or synthetic data for reasoning would find the framing useful as a starting point. It deserves peer review because the question is substantive and the artifacts are new, but any referee would need to see proper validation of the reconstructions and clearer controls before the claims can be taken at face value.

Referee Report

2 major / 2 minor

Summary. The paper introduces GAAR, an automatic engine for reconstructing arbitrary arguments, uses it to synthesize the Arguinas dataset, and reports that LLMs trained to perform argument reconstruction outperform models without such training across seven critical thinking tasks, with the largest gains when using Arguinas.

Significance. If the reconstructions prove faithful and the experiments are properly controlled, the work could establish argument reconstruction as a useful supervision signal for improving LLMs' critical thinking. The new engine and dataset would then constitute concrete resources for the community.

major comments (2)

[Abstract] Abstract: the central claim of outperformance on seven tasks with largest gains on Arguinas is stated without any description of experimental setup, baselines, statistical tests, or controls for dataset quality, leaving the result with limited verifiable support.
[Dataset creation] Dataset creation section: no fidelity metrics, inter-annotator agreement scores, or comparison against expert gold reconstructions are supplied for the GAAR-generated Arguinas data; without these, it is impossible to rule out that downstream gains arise from dataset artifacts rather than genuine inference learning.

minor comments (2)

Clarify the precise architecture and prompting strategy of the GAAR engine, including any hyperparameters or few-shot examples used.
List the seven critical thinking tasks explicitly and indicate which metrics are used for each.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important areas for improving the clarity and rigor of our work. We address each major comment below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim of outperformance on seven tasks with largest gains on Arguinas is stated without any description of experimental setup, baselines, statistical tests, or controls for dataset quality, leaving the result with limited verifiable support.

Authors: We agree that the abstract is overly concise and does not sufficiently detail the experimental setup to support the claims. In the revised manuscript, we will expand the abstract to briefly describe the seven critical thinking tasks, the baselines (models trained without argument reconstruction), the use of statistical tests for significance, and reference the dataset quality controls discussed in the main text. revision: yes
Referee: [Dataset creation] Dataset creation section: no fidelity metrics, inter-annotator agreement scores, or comparison against expert gold reconstructions are supplied for the GAAR-generated Arguinas data; without these, it is impossible to rule out that downstream gains arise from dataset artifacts rather than genuine inference learning.

Authors: This is a valid observation. The current manuscript does not report these metrics, which limits the ability to fully rule out artifacts. We will revise the Dataset creation section to include fidelity metrics for GAAR (e.g., accuracy on held-out expert-annotated arguments), inter-annotator agreement scores from a human evaluation of a sample of Arguinas, and direct comparisons to expert gold reconstructions. These additions will provide stronger evidence that performance gains reflect genuine inference learning. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical results rest on independent evaluation

full rationale

The paper introduces GAAR and Arguinas then measures downstream gains via standard supervised fine-tuning and task-specific benchmarks across seven held-out critical thinking tasks. No equations, fitted parameters, or self-citations are invoked to derive the performance improvements by construction; the central claim is an empirical observation that remains falsifiable by external replication or human validation of the reconstructions. The derivation chain is therefore self-contained and does not reduce to its inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on the domain assumption that explicit argument reconstruction improves critical thinking transfer and that the GAAR engine generates faithful reconstructions. No free parameters are described. Two new entities are introduced without independent evidence outside the paper.

axioms (1)

domain assumption Argument reconstruction makes an argument's underlying inferences explicit and thereby enhances critical thinking ability.
Stated as the motivation and core premise in the abstract.

invented entities (2)

GAAR engine no independent evidence
purpose: Automatically reconstructs arbitrary arguments to generate training data
Newly proposed component for synthesizing the dataset.
Arguinas dataset no independent evidence
purpose: High-quality argument reconstruction examples for LLM training
Synthesized using GAAR for the experiments.

pith-pipeline@v0.9.0 · 5443 in / 1305 out tokens · 50929 ms · 2026-05-15T10:22:45.897583+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We (1) propose an engine that automatically reconstructs arbitrary arguments (GAAR), (2) synthesize a new high-quality argument reconstruction dataset (Arguinas) using the GAAR engine, and (3) investigate whether learning argument reconstruction benefits downstream critical thinking tasks.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

GAAR outperforms all baseline methods, including AAR and LLM prompting, on argument reconstruction.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

78 extracted references · 78 canonical work pages

[1]

Deductive Reasoning: A form of argument where the truth of the premises necessitates the truth of the conclusion; the conclusion cannot but be true if the premises are true

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[2]

**Formalization** Premise 1: Evidence 1 Premise 2: Evidence 2

Inductive Reasoning: A form of ampliative argument where observations about past in- stances and regularities lead to generalizations about future instances and universal princi- ples. **Formalization** Premise 1: Evidence 1 Premise 2: Evidence 2 ... Premise N: Evidence N Premise N+1: If Evidence 1, Evidence 2, ..., and Evidence N, then Generalization ∴Co...

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[3]

Analogical Reasoning: A form of argument based on similarity relations, where if the source domain and target domain are similar in certain known respects, and the source do- main possesses a further feature, then the target domain is inferred to also have that feature or a similar counterpart. **Formalization** Premise 1: Source S has a property P1 Premi...

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[4]

**Formalization** Premise 1: Observation 1 Premise 2: Observation 2

Abductive Reasoning: A form of ampliative argument that involves inference to the best explanation, where a conclusion is drawn as to what could plausibly explain the occurrence of observed facts. **Formalization** Premise 1: Observation 1 Premise 2: Observation 2 ... Premise N: Observation N Premise N+1: Explanation H explains Observation 1, Observation ...

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[5]

Argument from Position to Know

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[6]

Argument from Expert Opinion

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[7]

Argument from Witness Testimony

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[8]

Argument from Popular Opinion: 4.1

Argument from Popular Opinion Subtypes of 4. Argument from Popular Opinion: 4.1. Pop Scheme 4.2. Position-to-Know Ad Populum Argument 4.3. Expert Opinion Ad Populum Argument 4.4. Deliberation Ad Populum Argument 4.5. Moral Justification Ad Populum Argument 4.6. Moral Justification (Excuse Subtype) Ad Populum Argument 4.7. Snob Appeal Ad Populum Argument 4...

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[9]

Argument from Popular Practice

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[10]

Argument from Example 6.2

Argument from Example 6.1. Argument from Example 6.2. Argument from Illustration 6.3. Argument from Model 6.4. Argument from Anti-Model

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[11]

Argument from Analogy

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[12]

Positive Schema 8.2

Practical Reasoning from Analogy 8.1. Positive Schema 8.2. Negative Schema

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[13]

Generic Composition 9.2

Argument from Composition 9.1. Generic Composition 9.2. Inclusion of the Part in the Whole

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[14]

Generic Division 10.2

Argument from Division 10.1. Generic Division 10.2. Division of the Whole into its Parts

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[15]

Descriptive Schemes 11.2

Argument from Oppositions 11.1. Descriptive Schemes 11.2. Normative Schemes

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[16]

Normative Schemes 12.2

Rhetorical Argument from Oppositions 12.1. Normative Schemes 12.2. Descriptive Schemes

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[17]

Cognitive Schemes 13.2

Argument from Alternatives 13.1. Cognitive Schemes 13.2. Normative Schemes

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[18]

Argument from Verbal Classification

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[19]

Argument from Definition to Verbal Classification

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[20]

Argument from Vagueness of a Verbal Classification

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[21]

Argument from Arbitrariness of a Verbal Classification

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[22]

Variant 1 18.2

Argumentation from Interaction of Act and Person 18.1. Variant 1 18.2. Variant 2

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[23]

Variant 1: Positive Value 19.2

Argumentation from Values 19.1. Variant 1: Positive Value 19.2. Variant 2: Negative Value

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[24]

Argumentation from Sacrifice

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[25]

Variant 1 21.2

Argumentation from the Group and Its Members 21.1. Variant 1 21.2. Variant 2

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[26]

Practical Inference 22.2

Practical Reasoning 22.1. Practical Inference 22.2. Necessary Condition Schema 22.3. Sufficient Condition Schema 22.4. Value-Based Practical Reasoning 22.5. Argument from Goal Figure 7: Specific Argument Types. 15 Specific Argument Types 22.6. Argumentation from Ends and Means

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[27]

Two-Person Practical Reasoning

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[28]

Argument from Sunk Costs

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[29]

Negative Reasoning from Normal Expectations 26.2

Argument from Ignorance 26.1. Negative Reasoning from Normal Expectations 26.2. Negative Practical Reasoning

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[30]

Epistemic Argument from Ignorance

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[31]

Argument from Cause to Effect

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[32]

Argument from Correlation to Cause

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[33]

Backward Argumentation Scheme 31.2

Abductive Argumentation Scheme 31.1. Backward Argumentation Scheme 31.2. Forward Argumentation Scheme 31.3. Abductive Scheme for Argument from Action to Character 31.4. Scheme for Argument from Character to Action (Predictive) 31.5. Retroductive Scheme for Identifying an Agent from a Past Action

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[34]

Argument from Verification 32.2

Argument from Evidence to a Hypothesis 32.1. Argument from Verification 32.2. Argument from Falsification

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[35]

Argument from Positive Consequences 33.2

Argument from Consequences 33.1. Argument from Positive Consequences 33.2. Argument from Negative Consequences 33.3. Reasoning from Negative Consequences 33.4. Argument from Negative Consequences (Prudential Inference)

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[36]

Pragmatic Argument from Alternatives

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[37]

Argument from Disjunctive Ad Baculum Threat

Argument from Threat 35.1. Argument from Disjunctive Ad Baculum Threat

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[38]

Argument from Fear Appeal

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[39]

Argument from Danger Appeal

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[40]

Argument from Need for Help

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[41]

Argument from Distress

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[42]

Argument from Commitment

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[43]

Pragmatic Inconsistency

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[44]

Argument from Inconsistent Commitment

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[45]

Circumstantial Ad Hominem

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[46]

Argument from Gradualism

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[47]

Slippery Slope Argument

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[48]

Precedent Slippery Slope Argument

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[49]

Sorites Slippery Slope Argument

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[50]

Verbal Slippery Slope Argument

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[51]

Full Slippery Slope Argument

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[52]

Physical World Premise Version 1 54.2

Argument for Constitutive-Rule Claims 54.1. Physical World Premise Version 1 54.2. Physical World Premise Version 2 54.3. Mental World Premise

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[53]

From Established Rule 55.2

Argument from Rules 55.1. From Established Rule 55.2. From Rules 55.3. Regulative-Rule Premise Obligation Claim

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[54]

Argument for an Exceptional Case

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[55]

Argument from Precedent

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[56]

Argument from Plea for Excuse

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[57]

Argument from Perception 59.2

Argument from Perception 59.1. Argument from Perception 59.2. Argument from Appearance

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[58]

16 Specific Argument Types

Argument from Memory Figure 8: Specific Argument Types. 16 Specific Argument Types

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[59]

Minor Premise: a asserts that A is true (false)

Argument from Position to Know Major Premise: Source a is in position to know about things in a certain subject domain S containing proposition A. Minor Premise: a asserts that A is true (false). Conclusion: A is true (false)

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[60]

Minor Premise: E asserts that proposition A is true (false)

Argument from Expert Opinion Major Premise: Source E is an expert in subject domain S containing proposition A. Minor Premise: E asserts that proposition A is true (false). Conclusion: A is true (false)

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[61]

Truth Telling Premise: Witness W is telling the truth (as W knows it)

Argument from Witness Testimony Position to Know Premise: Witness W is in a position to know whether A is true or not. Truth Telling Premise: Witness W is telling the truth (as W knows it). Statement Premise: Witness W states that A is true (false). Conclusion: A may be plausibly taken to be true (false)

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[62]

Presumption Premise: If A is generally accepted as true, that gives a reason in favor of A

Argument from Popular Opinion General Acceptance Premise: A is generally accepted as true. Presumption Premise: If A is generally accepted as true, that gives a reason in favor of A. Conclusion: There is a reason in favor of A

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[63]

Minor Premise: If A is a popular practice among those familiar with what is acceptable or not with regard to A, that gives a reason to think that A is acceptable

Argument from Popular Practice Major Premise: A is a popular practice among those who are familiar with what is acceptable or not in regard to A. Minor Premise: If A is a popular practice among those familiar with what is acceptable or not with regard to A, that gives a reason to think that A is acceptable. Conclusion: Therefore, A is acceptable in this case

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[64]

Conclusion: Therefore, generally, if x has property F, then it also has property G

Argument from Example Premise: In this particular case, the individual a has property F and also property G. Conclusion: Therefore, generally, if x has property F, then it also has property G

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[65]

Base Premise: A is true (false) in case C1

Argument from Analogy Similarity Premise: Generally, case C1 is similar to case C2. Base Premise: A is true (false) in case C1. Conclusion: A is true (false) in case C2

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[66]

Similarity Premise: S2 is similar to S1

Practical Reasoning from Analogy (Positive Schema) Base Premise: The right thing to do in S1 was to carry out action x. Similarity Premise: S2 is similar to S1. Conclusion: Therefore, the right thing to do in S2 is carry out x

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[67]

Conclusion: Therefore, X has property Y

Argument from Composition (Generic Composition) Premise: All the parts of X have property Y. Conclusion: Therefore, X has property Y

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[68]

Conclusion: Therefore, all the parts of X have property Y

Argument from Division (Generic Division) Premise: X has property Y. Conclusion: Therefore, all the parts of X have property Y. Figure 9: Specific Argument Types and Their Reconstructions. 17 B Detailed Description of GAAR Stages In Section 3.2, we describe GAAR by how it resolves existing issues of AAR. To those who are not familiar with AAR, we prepare ...

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[69]

prevent the development of a potential human being

P4 and P5 are over-generalized. For example, this formulation would also apply to abstinence or celibacy – all of which “prevent the development of a potential human being” but are clearly not what the original argument intends

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[70]

We allow abortion

The conclusion “We allow abortion” is descriptive, but the original argument’s conclusion appears to be normative: “We should allow abortion.” Stage 2-2. Reconstruction P1: We allow contraception. P2: Contraception prevents the development of a potential human being. P3: Abortion prevents the development of a potential human being. P4: (Implicit) If contr...

work page 1950
[71]

A, A →C” and “B, B →C

Add any missing formalized premises that are necessary to prove the conclusion but can- not be derived from the formalized premises. 2. Keep all formalized premises that contribute to proving the conclusion through ANY valid reasoning path, even if there are multiple inde- pendent paths to the same conclusion. For example, if both “A, A →C” and “B, B →C” ...

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[72]

You should format these premises into a python dictionary where keys and values are python strings

Remove only those formalized premises that are completely irrelevant and do not con- tribute to proving the conclusion through any reasoning path. You should format these premises into a python dictionary where keys and values are python strings. Second, write a python program using z3 that inputs the necessary formalized premises and formalized conclusio...

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[74]

[Symbol of a premise]

All necessary formalized premises that appear in at least one minimal valid reasoning path (i.e., the union of all minimal sets), formatted as a python list of keys of the python dictionary of the necessary formalized premises. You should therefore print two things (a python string and a python list) separately. Please use the below python code snippet. {...

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[75]

valid” or “invalid

Their validity, formatted as a python string of either “valid” or “invalid”

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[76]

You should therefore print a python string

All necessary formalized premises that appear in at least one minimal valid reasoning path (i.e., the union of all minimal sets), formatted as a python list of keys of the python dictionary of the necessary formalized premises. You should therefore print a python string. Please use the below python code snippet. {Code snippet for validity judgment and pre...

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[77]

fixing” or “improving

Accuracy - Assess whether the reconstruction accurately represents the original argument’s actual rea- soning path, including any inferential leaps, gaps, or logical fallacies, without misrepresenta- tion. - Misrepresentation includes both distorting what was said AND artificially strengthening weak or fallacious reasoning. - Do NOT reward a reconstructio...

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[78]

- If the original argument has logical gaps, a complete reconstruction captures those gaps rather than filling them

Completeness - Assess whether all essential or core premises required to reconstruct the original argument are included. - If the original argument has logical gaps, a complete reconstruction captures those gaps rather than filling them. - If both reconstructions include all essential or core premises required to reconstruct the origi- nal argument, the r...

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[79]

TIE”. Examples: “accuracy

Parsimony - Assess whether the reconstruction avoids including premises that are unnecessary for repre- senting the original argument’s actual reasoning. - Do NOT judge the reconstruction as more parsimonious simply because it has less number of premises. As long as premises are necessary, the number of premises does not matter. - Premises that introduce ...

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