arXiv AI
Jun 11
An XAI View on Explainable ASP: Methods, Systems, and Perspectives
arXiv:2601. 14764v2 Announce Type: replace Abstract: Answer Set Programming (ASP) is a popular declarative reasoning and problem solving approach in symbolic AI.
EZASP - Facilitating the Usage of ASP
arXiv:2603. 26863v2 Announce Type: replace-cross Abstract: Answer Set Programming (ASP) is a declarative programming language used for modeling and solving complex combinatorial problems.
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arXiv AI
Jul 28
Imprompt: A Language Framework for Prompt Programming
arXiv:2607. 22683v1 Announce Type: new Abstract: With the unprecedented success of Language Models (LMs), the science of Prompt Engineering has evolved the powerful idea of Prompt Programming, where prompts are treated as a programmable control surface for describing complex tasks and leveraging LM capabilities.
arXiv AI
1d ago
Revisiting the Performance of Generative Artificial Intelligence on Introductory Object-Oriented Programming Assessments: Insights from 2026
arXiv:2608. 16318v1 Announce Type: cross Abstract: Recent advances in Generative Artificial Intelligence (GenAI) have substantially improved the ability of large language models (LLMs) to generate and explain source code.
arXiv AI
Jul 16
EZSMT Version 3, Matured
arXiv:2607. 13344v1 Announce Type: new Abstract: Constraint Answer Set Programming (CASP) is a hybrid reasoning paradigm that combines Answer Set Programming (ASP) with Constraint Processing and Satisfiability Modulo Theories (SMT), enabling powerful declarative encodings of complex combinatorial search problems.
arXiv AI
Jul 14
Commenting with Copilot: A Taxonomy and Multi-Year Analysis of Student Code-Generation Specifications
arXiv:2607. 10674v1 Announce Type: cross Abstract: As AI code tools become integrated into programming environments, students increasingly describe intended behavior in natural language and rely on these tools to generate code, shifting emphasis from code writing to specification.
arXiv AI
Aug 7
BlockPython: A Process-Aware Agent-Supported Platform for the Transition from Block-Based to Python Programming
arXiv:2608. 05716v1 Announce Type: new Abstract: The transition from block-based to text-based programming requires learners to convert visible program structures into abstract textual expressions, which may create a cognitive gap between understanding computational concepts and expressing them in Python syntax.
arXiv AI
Aug 3
RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment
arXiv:2507. 22580v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) seeks to automatically correct software bugs without requiring human intervention.
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.
arXiv AI
Jun 30
Code Reasoning for Software Engineering Tasks: A Survey and A Call to Action
arXiv:2506. 13932v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has led to dramatic improvements across a wide range of natural language tasks.
arXiv AI
Jul 24
Bound-Founded Semantics for Answer Set Programming with Difference Constraints: Preliminary Report
arXiv:2607. 21201v1 Announce Type: new Abstract: While the integration of linear constraints has significantly expanded the reach of Answer Set Programming (ASP), existing hybrid solvers often rely on disparate semantic underpinnings that lack a unified logical foundation.
arXiv AI
Jun 30
CLMASP: Coupling Large Language Models with Answer Set Programming for Robotic Task Planning
arXiv:2406. 03367v2 Announce Type: replace Abstract: Large Language Models (LLMs) possess extensive foundational knowledge and moderate reasoning abilities, making them suitable for general task planning in open-world scenarios.
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.