arXiv:2607. 06748v1 Announce Type: cross Abstract: Smart home automation platforms increasingly rely on user-authored YAML configuration files to define device behaviors, but these files are prone to syntax, formatting, and semantic logic errors that can cause automation failures and safety risks.
arXiv:2608. 05060v1 Announce Type: cross Abstract: Structured input files such as JSON, DOT, OBJ, INI, S-expression, and TinyC are widely used in software systems, but small corruptions can cause parsers to reject otherwise useful data.
RegoMender is a multi‑agent framework that automates the repair of Rego policies, a key component of Policy‑as‑Code in cloud‑native systems. It uses a shared blackboard and atomic toolkit to dynamically apply patching, rewriting, or rollback actions, achieving high syntactic (82.9%) and semantic (77.9%) success rates on the newly introduced RegoBench benchmark of 1,159 real‑world defects. The system also generalizes to other languages such as HCL and SQL, repairing 86.4% of 132 real‑world CVE vulnerabilities, thereby demonstrating robustness and practical security value.
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.
arXiv:2606. 06212v1 Announce Type: new Abstract: Misconfigurations in computer networks remain a major source of critical Internet outages.
arXiv:2604.10800v2 Announce Type: replace-cross Abstract: Learned classifiers deployed in agentic pipelines face a fundamental reliability problem: predictions are probabilistic inferences, not verif...
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.
The paper introduces FlowAgent, an AI agent deployed at Google to automatically repair test failures in the pre-submit continuous integration workflow. FlowAgent uses a ReAct-style generate-and-validate loop with strict latency and quality filters, and was evaluated on 195 real-world failures with a 67.18% accuracy rate. After deployment, it suggested fixes on 295,508 changes, with developers previewing 65,069 and applying 28,554, and received positive feedback from interviews.
The paper introduces a roundtrip verification method for ensuring that large language models (LLMs) produce faithful formalizations of natural language statements. By formalizing a statement, translating it back to natural language, re-formalizing, and checking logical equivalence with a formal tool, the approach detects inconsistencies without needing ground-truth annotations. When inconsistencies are found, a diagnosis localizes the error to a specific translation step, and a scoped repair operator attempts to correct it. The framework is evaluated on the Texas Transportation Code and Texas Parks and Wildlife Code using Claude Opus and GPT-5, showing that diagnosis-guided scoped repair is most effective and that rules failing the equivalence check exhibit significantly more natural language inference drift.
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:2607. 16388v1 Announce Type: cross Abstract: Large-scale AI datacenter platforms comprise thousands of heterogeneous hardware components whose validation requires comprehensive fault injection test plans.
SkillScriptBench is a 350‑task benchmark that evaluates how well large language models can self‑evolve executable agent skill packages by distinguishing between documentation repair, script repair, and preservation. The benchmark draws from 100 real GitHub skill packages, creating 150 repair tasks with injected script faults and a controlled track of 200 tasks that test clean, documentation‑faulty, script‑faulty, and combined‑faulty states. Experiments show that AST‑Guided Skill Revision—using abstract syntax trees to target relevant code locations—improves repair success by up to 27.7% and yields more consistent outcomes across repeated runs. whyItMatters":"The benchmark provides a systematic way to measure and improve the reliability of automated skill package maintenance, which is essential for deploying robust LLM agents in real‑world applications."
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.