arXiv AI July 23, 2026 By Mario Alviano, Lorenzo Grillo, Nicola Leone, Fabrizio Lo Scudo

Logic-Guided Data Extraction with Answer Set Programming and Large Language Models

arXiv:2607. 19365v1 Announce Type: new Abstract: When Large Language Models (LLMs) are used for semantic data extraction from unstructured text, producing candidate relational facts from natural language, they may remain unreliable for tasks requiring complex combinatorial reasoning and global consistency.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 14

ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm

arXiv:2607. 11207v1 Announce Type: cross Abstract: Table-based reasoning with large language models (LLMs), which requires reasoning based on natural language questions and structured tabular data, has gained widespread attention.

By Pei Guo, Enjie Liu, Yunzhi Tan, Mochi Gao, Jianxin Zhang, Ruichao Zhong, Juntao Li, Bo Hu, Zang Li

llms data-engineering

arXiv AI

Jul 3

ADVENT: LLM-Driven Automatic Predicate Invention for ILP

arXiv:2607. 01585v1 Announce Type: cross Abstract: Predicate invention (PI), the creation of new predicates to extend the hypothesis space, remains a critical bottleneck in Inductive Logic Programming (ILP).

By Tingting Yu, Pei-Cing Huang, Chan Hsu, Chan-Tung Ku, Yihuang Kang

llms

arXiv AI

Jun 12

LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic Reasoning

arXiv:2604. 27960v2 Announce Type: replace Abstract: Recent large language models (LLMs) have achieved impressive reasoning milestones but continue to struggle with high computational costs, logical inconsistencies, and sharp performance degradation on high-complexity problems.

By Adam Ishay, Joohyung Lee

llms benchmarks

arXiv AI

Jun 3

Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs

arXiv:2606. 03705v1 Announce Type: new Abstract: Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations.

By Weiwei Ding, Zixuan Li, Long Bai, Zhuo Chen, Kun Su, Fei Wang, Xiaolong Jin, Jin Zhang, Jiafeng Guo, Xueqi Cheng

llms benchmarks safety

arXiv AI

Jun 3

Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering

arXiv:2606. 03269v1 Announce Type: new Abstract: Visual Question Answering (VQA) is the task of answering questions about images, requiring the integration of multimodal input and reasoning.

By Thomas Eiter, Nelson Higuera Ruiz, Johannes Oetsch

llms nlp efficiency multimodal safety

arXiv AI

Jun 19

QMFOL: Benchmarking Large Language Model Reasoning via Quantifiable Monadic First-Order Logic Test Case Generation

arXiv:2606. 20227v1 Announce Type: new Abstract: Large Language Models (LLMs) have made significant progress in reasoning, particularly in deductive reasoning, which is crucial for high-stakes decision-making.

By Xinyi Zheng, Ling Shi, Tianlong Yu, Yongxin Zhao, Lorenz Goette, Kailong Wang

llms benchmarks

Related stories

ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm

ADVENT: LLM-Driven Automatic Predicate Invention for ILP

LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic Reasoning

Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs

Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering

QMFOL: Benchmarking Large Language Model Reasoning via Quantifiable Monadic First-Order Logic Test Case Generation