embed validity checks into registry and workflow operations

Existing conformance checking approaches between process models and reference models suffer from limited semantic expressiveness and insufficient automation, hindering fine-grained compliance verification. This paper proposes a semantic consistency checking method grounded in causal dependency analysis of tasks and events, transcending traditional trajectory-based dependency modeling by formally encoding causal constraints at the semantic level. We establish a unified framework integrating causal dependency modeling, semantic representation, and formal verification, and design an automated conformance checking algorithm implemented in a prototype tool. Empirical evaluation demonstrates that our approach significantly outperforms state-of-the-art techniques in both accuracy and flexibility, achieving— for the first time—the fully automated, high-expressivity semantic conformance verification of process models against reference models.

Large language models (LLMs) lack verifiability and regulatory alignment when generating compliance-critical artifacts in safety-sensitive domains. Method: We propose Constraint-Guided Verifiable Generation (CVG), a framework featuring a Unified Meta-Model (UMM) for harmonizing heterogeneous regulatory texts; an Integrated Constraint Model (ICM) enabling dual-layer validation—structural (via GBNF/DFA) and semantic (via SHACL/SMT); and a synergistic prefix-safe decoding mechanism coupled with runtime automata and post-generation validators to embed auditable, traceable regulatory evidence chains. Contribution/Results: CVG innovatively integrates machine-verifiable certificates and violation-driven audit-and-repair directly into the generation pipeline. Evaluated on AUTOSAR automotive software and cross-border judicial workflows, CVG achieves 100% structural conformance, reduces manual correction effort by 72%, and seamlessly interoperates with existing Model-Driven Engineering (MDE) toolchains—delivering, for the first time, high-assurance, auditable, end-to-end compliant LLM-generated artifacts.

Ad hoc SQL development lacks engineering rigor, leading to data silos, logical redundancy, and ineffective data governance. Method: This paper proposes a DataOps-driven CI/CD framework for analytical SQL warehouses, featuring a novel five-stage automated pipeline—Lint, Optimize, Parse, Validate, Observe—that embeds quality assurance and enables end-to-end lifecycle governance. Contribution/Results: We introduce the DataOps Controls Scorecard and a requirements traceability matrix, explicitly mapping 12 governance criteria to CI/CD stages to ensure control completeness and scalability. The framework integrates Agile, Lean, and DevOps principles with static analysis, syntactic parsing, optimization recommendations, validation testing, and observability. Empirical evaluation demonstrates significant improvements in data quality, development transparency, and cross-functional collaboration, providing a sustainable, production-ready pathway for large-scale analytical systems.

Current RESTful API design quality assessment relies heavily on manual inspection, lacking early, automated validation mechanisms for non-functional requirements—particularly interoperability, modularity, and maintainability. Method: This paper proposes an OpenAPI-based static analysis approach that implements a configurable rule engine. It formalizes 75 design principles derived from scholarly literature and industry standards into structured, machine-checkable constraints, enabling customizable rule activation/deactivation and traceable feedback to align requirements engineering with architectural governance. Contribution/Results: Following the design science research paradigm, we developed and evaluated a prototype tool. Empirical evaluation and expert review demonstrate that the method significantly improves API design compliance and consistency, achieving 82% automation coverage. It effectively supports continuous architectural governance in agile development environments, bridging the gap between design-time assurance and operational API lifecycle management.

To address the high proof-generation overhead and inefficient constraint-system mapping in zkVMs, this paper proposes the first ISA specifically designed to optimize zk-SNARK proof efficiency. Our approach employs deterministic semantic modeling and a lightweight register architecture to ensure unambiguous semantics and formal verifiability; the instruction set is intrinsically proof-friendly, enabling direct, low-overhead compilation into arithmetic circuits. The v1.0 specification defines 32 core instructions that fully capture memory access and control-flow semantics. Evaluation on the Halo2 backend demonstrates over 40% reduction in per-instruction proof-generation cost, significantly improving end-to-end zkVM proving efficiency. This work establishes a formally verifiable, foundational ISA specification—enabling both high-performance zkVM implementations and trustworthy compiler toolchains for zero-knowledge applications.

This study addresses the limitations of existing SysML verification approaches, which are often tool-dependent and restricted to performance properties, lacking support for automated validation of behavioral and interface requirements. To overcome these shortcomings, this work proposes a tool-agnostic, automated verification workflow driven by SysML test cases, integrating UML Testing Profile and behavioral diagram constructs to enable unified validation of multidimensional attributes—including behavior, timing, and state responses. The methodology was developed through a mixed-methods research strategy combining literature review and stakeholder interviews, and its efficacy was empirically validated across two independent SysML toolchains. The approach not only transcends the constraints of conventional parametric methods but also enables automatic traceability of verification results back to the original model elements.

This study addresses widespread compliance issues in GitHub Actions workflows, such as excessive permissions and weak secret management. It proposes the first documentation-driven compliance checking framework, which derives a 30-item checklist from official documentation and implements a hybrid auditing pipeline combining large language models (LLMs) with expert oversight. The authors automatically evaluate 95 real-world Java workflows using four open-source LLMs, employ GPT-5 as a conflict arbitrator, and integrate manual review into a multi-tiered validation system. Experimental results reveal an overall compliance rate of only 28%, with permission control as low as 4%. The proposed approach reduces manual verification effort by 81% while achieving 87% agreement with expert judgments, significantly enhancing audit efficiency and reproducibility.

This work addresses the vulnerability of cognitive workflow executors on the BEAM virtual machine to adversarial bypass attacks, demonstrating that reliance solely on static module import graphs is insufficient to enforce purity constraints. To resolve this, the authors propose a novel mechanism integrating structural restrictions with cryptographic proofs, shifting governance from runtime conventions to structural capability boundaries. By introducing purity certificates, a restricted WebAssembly compilation target, runtime validation gating, and cross-organizational remote attestation, the approach eliminates all five known classes of BEAM bypass paths for the first time and formally verifies four core security properties. Experiments across four executors show that purity verification incurs only 39–42 microseconds of latency, full workflow cycles remain under 400 microseconds, runtime overhead amounts to less than 0.4% of a 100-millisecond HTTP request, and execution results are fully deterministic.

This work addresses the problem of global inconsistency in multi-component intelligent agent releases, where local validation passes but cross-component relational integrity fails due to the absence of holistic consistency guarantees. To tackle this, we propose the Schema-SIP Relational Consistency (SIP-RC) framework—the first systematic approach to formally define and mitigate relational inconsistency faults in multi-component deployments. SIP-RC models release packages as graph structures and integrates schema documentation with product contract principles to enable cross-component relational verification. Key mechanisms include declarative–evidential linkage, decision authority scoping, provenance tracking of derived components, and byte-level consistency checks. Preliminary experiments demonstrate the feasibility of the proposed framework, offering a practical and actionable paradigm for ensuring relational consistency in intelligent agent releases.