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.
Soda: An Object-Oriented Functional Language for Specifying Human-Centered Problems
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arXiv AI
Jun 19
FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning
arXiv:2604. 11556v2 Announce Type: replace-cross Abstract: LLM-assisted software development has become increasingly prevalent, and can generate large-scale systems, such as compilers.
arXiv AI
Jul 21
Composable Verification Pipelines for Multi-Agent Systems
arXiv:2607. 16266v1 Announce Type: cross Abstract: Existing approaches for reasoning about action and change provide expressive semantics for modeling dynamic systems, in most cases built on top of logic programming systems.
arXiv AI
Jul 24
VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification
arXiv:2607. 20474v1 Announce Type: new Abstract: Natural language interfaces can greatly benefit the accessibility and usability of optimization modeling, and recent advances in large language models (LLMs) show promise in automatically translating textual problem descriptions into executable solver formulations.
Hugging Face Trending Papers
Jul 28
Model-Driven Requirements Configuration with Three-Valued Uncertainty Scoring
Context: Large Language Models (LLMs) offer natural-language flexibility for automated requirements elicitation but frequently generate structurally invalid requirements and logical inconsistencies, lacking formal correctness guarantees. Objectives: This study aims to eliminate logical inconsistencies and enforce structural conformance in LLM-generated requirements while quantifying the LLM's pre-validation decision uncertainty within a formal domain model.
arXiv AI
Jul 24
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.
arXiv AI
Jul 29
Specula: Scaling formal specifications for autonomous model checking of system code
arXiv:2607. 25333v1 Announce Type: cross Abstract: Specula is a push-button agentic system that generates high-quality formal specifications for large, complex system code and uses the specifications for highly effective model checking and bug finding.
arXiv AI
Jun 17
PromptMN: Pseudo Prompting Language
arXiv:2606. 17164v1 Announce Type: cross Abstract: Prompting has become the primary interface between humans and generative AI, yet many natural language prompts remain fragile: roles, goals, constraints, and expected outputs are often buried in prose or left implicit.
arXiv AI
Sep 10
HoarePrompt: Structural Reasoning About Program Correctness in Natural Language
HoarePrompt is a new method that applies program verification concepts to natural language requirements, using large language models to generate step‑by‑step natural language descriptions of program states. It incorporates a few‑shot k‑induction technique to handle loops and then evaluates whether the annotated program satisfies the requirements. On the CoCoClaNeL dataset, HoarePrompt raises the Matthews correlation coefficient by 61% over zero‑shot chain‑of‑thought prompts and by 106% over test‑generation classifiers, with the inductive reasoning component adding a 26% MCC improvement.
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 Machine Learning
Aug 3
Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies
arXiv:2607. 29389v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated a strong ability to generate syntactically correct code from natural-language specifications.
arXiv Computation and Language
4d ago
SemOPT: Fixing Semantic Errors in LLM-based Optimization Modeling via Reward-Guided Search
arXiv:2609.37361v1 Announce Type: new Abstract: Operations research supports decision-making in domains such as energy, economics, and healthcare. Solving operations research problems typically begin...