arXiv AI August 20, 2026 By Xuyao Feng, Antonis Bikakis

Pairwise Logical Selection of Enthymeme Completions under Semantic-Link Uncertainty

The paper presents a neuro‑symbolic approach for selecting the correct omitted component in enthymemes, extending prior work from missing‑premise to missing‑claim selection. It replaces binary entailment with logical‑resistance scores and introduces the Possible‑World Atom‑Link Formalization (PWAL), which marginalizes over alternative semantic‑link configurations while keeping translated formulae fixed. Experiments on five tasks show that PWAL improves strict accuracy by up to 30.86 percentage points and reduces tie rates significantly, while also providing a transparent trace of each comparison.

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arXiv Computation and Language

Oct 15

Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI

arXiv:2610.15158v2 Announce Type: replace Abstract: We study neuro-symbolic natural language inference (NLI) with independently translated propositional representations of the premise and hypothesis....

By Xuyao Feng, Antonis Bikakis

rag

arXiv AI

Jul 31

Making Implicit Premises Explicit in Logical Understanding of Enthymemes

arXiv:2603. 06114v2 Announce Type: replace-cross Abstract: Real-world arguments in text and dialogues are normally enthymemes (i.

By Xuyao Feng, Anthony Hunter

llms nlp

arXiv AI

Oct 15

VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning

arXiv:2601.20055v3 Announce Type: replace-cross Abstract: Despite the syntactic fluency of Large Language Models (LLMs), ensuring their logical correctness in high-stakes domains remains a fundamenta...

By Vikash Singh, Darion Cassel, Nathaniel Weir, Nick Feng, Sam Bayless

llms benchmarks safety

arXiv AI

Jun 3

Quantifying Faithful Confidence Expression in Large Reasoning Models

arXiv:2606. 03969v1 Announce Type: cross Abstract: Reliable uncertainty communication is critical to the trustworthiness of LLMs, yet faithful calibration (FC)--the alignment between models' intrinsic and (linguistically) expressed confidence--is a persistent failure mode.

By Areeb Gani, Asal Meskin, Gabrielle Kaili-May Liu, Arman Cohan

llms safety

arXiv AI

Oct 2

Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI

The paper introduces a pipeline that automatically creates ontology‑grounded multiple‑choice question benchmarks for evaluating large language models (LLMs) on logical reasoning tasks in scientific AI. By using OWL 2 ontologies, correct answers are guaranteed by design and distractors are generated and formally verified as incorrect through an OWL reasoner. Experiments on three ontologies—Pizza, PMDco, and DOID—yielded 112, 2,491, and 15,216 MCQs, respectively, with high natural‑language quality and challenging zero‑shot performance for six LLMs.

By Nishtha N. Vaidya, Stephan Grimm, Thomas Hubauer, Thomas A. Runkler

llms rag nlp benchmarks

arXiv AI

Oct 15

Identification of Entailment and Contradiction Relations for Natural Language Argumentation: A Neurosymbolic Approach

arXiv:2405.01259v2 Announce Type: replace Abstract: Argumentation is an important cognitive activity that involves the scrutiny of arguments and counterarguments. When given an argument, it is desira...

By Xuyao Feng, Anthony Hunter

benchmarks

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Related stories

Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI

Making Implicit Premises Explicit in Logical Understanding of Enthymemes

VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning

Quantifying Faithful Confidence Expression in Large Reasoning Models

Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI

Identification of Entailment and Contradiction Relations for Natural Language Argumentation: A Neurosymbolic Approach