arXiv AI
Aug 11
AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems
arXiv:2604. 02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data.
Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs
SysML v2's textual syntax enables compiler-based validation of model structure and language conformance. However, semantic mistakes that preserve syntactic validity but violate domain rules cannot be detected through compilers.
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arXiv Computation and Language
Sep 1
SkillForge: Compositional Skill Synthesis with Verification-in-the-Loop for Generating Formally Verified Dafny Programs
SkillForge is a framework that breaks down formal code synthesis into reusable atomic skills, each handling a specific subtask such as specification inference, body synthesis, invariant generation, error diagnosis, or repair. A verification-driven harness coordinates these skills by submitting candidates to the Dafny verifier, diagnosing failures, and routing them deterministically to the appropriate repair skill until correctness is achieved or a budget is reached. On a curated benchmark, SkillForge outperforms state‑of‑the‑art agentic and iterative baselines, requiring fewer tokens and lower latency, with ablation studies showing each skill’s measurable contribution and rapid convergence.
arXiv AI
Sep 15
Natural-Language to SysMLv2 Translation via Conformance-Driven Iterative Refinement
The paper introduces a framework that translates natural‑language descriptions into SysMLv2 models using a generate‑check‑repair loop driven by a SysMLv2 conformance checker. By embedding the checker as an oracle, the system iteratively repairs generated models until they achieve zero conformance errors, ensuring they are deployable in industrial modeling environments. Evaluation on 151 prompts across four large language models shows the approach raises production‑conformance acceptance from 51.16% to 100%.
arXiv AI
Jul 22
One Rewrite to Fix Them All? Type-Aware Repair Allocation for Text-to-Image Prompt Optimization
arXiv:2607. 18724v1 Announce Type: new Abstract: Text-to-image (T2I) generators often fail to follow their prompts faithfully, producing wrong counts, swapped attributes, ambiguous relations, and illegible text.
arXiv AI
Jun 17
EngTrace: A Symbolic Benchmark for Verifiable Process Supervision of Engineering Reasoning
arXiv:2511. 01650v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly entering specialized, safety-critical engineering workflows governed by strict quantitative standards and immutable physical laws, making rigorous evaluation of their reasoning capabilities imperative.
arXiv AI
Sep 3
Dictionary-Guided Mutation Operators for Automated HDL Repair
The paper introduces a dictionary-guided HDL repair system that uses ANTLR-derived mutation vocabularies and a simulation-divergence fault localization module to generate syntactically valid Verilog mutations. The mutation operator performs token substitutions, insertions, and deletions via regex matching, while the fault localization scores source lines based on proximity to diverging output wires, guiding the search. Evaluated on the CirFix benchmark, the approach achieves correct repairs on 14 bug variants, including a multi-bug case, and outperforms CirFix with an 18x speedup on a two-edit benchmark.
arXiv AI
Jul 1
Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models
arXiv:2606. 31614v1 Announce Type: cross Abstract: Engineering specifications such as interlocks, alarm rationalization tables, and cause-and-effect (C&E) matrices remain central to process control and safety, yet their creation is still predominantly manual, document-driven, and prone to inconsistency.
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
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 Machine Learning
Jun 16
Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification
arXiv:2601. 22642v2 Announce Type: replace Abstract: Large Language Models (LLMs) show remarkable capabilities, yet their stochastic next-token prediction creates logical inconsistencies and reward hacking that formal symbolic systems avoid.
arXiv AI
Sep 15
NeuroProlog: Multi-Task Fine-Tuning for Neurosymbolic Mathematical Reasoning via the Cocktail Effect
arXiv:2603.02504v3 Announce Type: replace Abstract: Large Language Models (LLMs) achieve strong performance on natural language tasks but remain unreliable in mathematical reasoning, frequently gener...
arXiv AI
Sep 11
Grounded Evaluation and Repair for NL-to-PDDL Problem Generation
The paper presents an end‑to‑end pipeline for translating natural language planning descriptions into PDDL problem instances using large language models. It incorporates multiple checks—syntactic parsing, planner success, domain conformance, an LLM critic, and iterative repair—to ensure faithfulness to the original task. Experiments on Planetarium, AutoPlanBench, and curated PDDL~2.1 problems reveal that operational success can diverge from benchmark‑reference reconstruction, and that structured repair improves outcomes while PDDL~2.1 remains challenging for reference reconstruction.