consistency enforcement

Current AI systems rely heavily on manual auditing and documentation, which hinders scalable governance for automated services. This work proposes Ontological Knowledge Blocks (OKBs), a novel framework that formalizes regulatory obligations as quintuples comprising ontologies, SHACL rules, evidence requirements, and provenance links. By leveraging RDF/OWL modeling, PROV-O for provenance tracking, and an intermediate representation–driven deterministic compiler, the approach enables dynamic switching of governance configurations without modifying service code. Evaluation in an AI-assisted HPC scheduling scenario demonstrates that compliance checks are configuration-sensitive, violations accumulate strictly additively, SHACL validation incurs only 12.6–100.3 milliseconds of latency, and the Combined configuration provides the most comprehensive coverage.

This work addresses the limitations of existing AI trustworthiness assessment approaches, which are either too abstract to support full lifecycle monitoring or rely on single metrics insufficient for governance needs. The paper proposes a lightweight, auditable framework for dynamic trustworthiness management that integrates formal modeling with governance processes. By employing context-sensitive trustworthiness dimension protocols and interpretable rule learning based on decision trees, the framework enables end-to-end monitoring and documentation of AI systems—from design and deployment through re-evaluation. Novel diagnostic tools, including hierarchical transitions, margin-of-boundary analysis, and profile drift detection, are introduced alongside clearly accountable human-in-the-loop checkpoints. Experiments on synthetic AI lifecycle trajectories demonstrate the framework’s effectiveness in detecting performance degradation, abrupt perturbations, and impacts of system updates, thereby establishing a transparent, traceable, and contestable evidentiary basis for AI governance.

AI-augmented Data Processing Systems (DPS) suffer from low trustworthiness in critical applications due to the inherent unreliability of large language model (LLM) outputs, while existing constraint mechanisms are fragmented, imperative, and lack semantic-aware query execution support. Method: We propose Semantic Integrity Constraints (SICs)—a declarative abstraction embedded within relational models—extending classical database integrity constraints to the semantic layer for the first time. We introduce novel constraint classes (e.g., groundedness), unify active constraint decoding with passive verification-and-recovery execution, and extend relational algebra to enable query-aware optimization and execution. Contribution/Results: Our framework significantly improves DPS trustworthiness and performance, supports enterprise-scale deployment, and establishes foundational theory and systems infrastructure for constraint-driven semantic query optimization and adaptive execution.

This study addresses the “consistency trap” in traditional evaluation methods, which erroneously penalize rule-compliant decisions that deviate from historical human labels. To overcome this limitation, the authors propose a rule-based correctness evaluation framework that validates model decisions through hierarchical logical reasoning grounded in governance rules. They introduce two novel metrics—the Defeasibility Index (DI) and Ambiguity Index (AI)—to quantify assessment quality. Innovatively leveraging reasoning traces from large language models as governance signals, they formulate a Probabilistic Defeasibility Signal (PDS) to estimate inference stability without manual review. Experiments on over 193,000 Reddit moderation decisions reveal a 33–46.6 percentage-point gap between consistency-based metrics and rule-based correctness, with 79.8–80.6% of false negatives actually being compliant. A governance gating mechanism achieves 78.6% automation coverage while reducing risk by 64.9%.

In digital forensics, the atomicity and integrity of storage snapshots lack rigorous definitions that jointly guarantee both instantaneousness and causal ordering—undermining evidentiary admissibility in legal proceedings. To address this, we propose a novel atomicity definition grounded in causal consistency, overcoming the limitation of conventional time-based atomicity models. We further rectify conceptual flaws in existing integrity definitions and introduce a revised, theoretically sound yet engineering-practical integrity criterion—explicitly supporting copy-on-write (CoW) implementations. Our approach integrates causal modeling, formal snapshot semantics, CoW mechanism analysis, and formalization of forensic quality criteria, yielding a verifiable snapshot semantic framework. This work establishes the first theoretical foundation for forensic tool design that unifies causal ordering with instantaneous state capture, thereby significantly enhancing the forensic validity and judicial admissibility of live data acquisition.

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.

This study addresses the security risks arising from semantic mismatches in data that crosses trust boundaries, even when such data passes syntactic validation. It introduces the first systematic definition of the “Trust Boundary Semantic Gap” (TBSG) and proposes a Multidimensional Trust Boundary Semantic Gap (MDTBSG) model that characterizes TBSG along four dimensions: identity, space, time, and interpretation. Furthermore, the work develops the TBSAM framework for the design phase, integrating static specification analysis, semantic alignment modeling, gap provenance tracing, and architectural control mapping to identify, prioritize, and mitigate semantic gaps. Applied retrospectively to the SolarWinds/SUNBURST attack, the approach successfully pinpointed the root cause of critical semantic gaps, clarified assumptions in the receiving domain, and recommended effective architectural controls to disrupt the attack path.

This work addresses the challenge of maintaining consistent and auditable materialized views of world models in dynamic physical environments where sensory data is costly, heterogeneous, delayed, and unreliable. The paper introduces the first auditability-aware consistency contract for world models, combining commitment-level validity abstraction, consequence-condition-driven adaptive view maintenance, scoped transactional compensation, and an append-only provenance architecture to ensure consistency, enable efficient recovery, and support precise accountability. By integrating materialized view management, stream synchronization, Saga-style transactions, and freshness control, the proposed system demonstrates significant advantages over prior approaches on the Flood-SAR benchmark, particularly in data freshness, recovery success rate, verification overhead, and replay fidelity.

This study addresses the degradation of evidence quality in risk decision systems caused by delayed ground-truth labels, a challenge inadequately handled by existing drift detection methods. The work formalizes evidence sufficiency into four quantifiable dimensions—completeness, freshness, reliability, and representativeness—and introduces a monitoring framework based on a decision-readiness threshold and seven types of unlabeled proxy indicators. This enables unsupervised quantification and trajectory tracking of evidence quality degradation while characterizing detection blind spots under various drift types. Evaluated on the IEEE-CIS fraud detection dataset, the method achieves 100% detection of covariate and mixed drifts. Simulations further reveal that under concept drift, evidence sufficiency declines to 0.242 within 60 days—significantly faster than in drift-free scenarios.

Existing automated approaches for mapping cyber threat intelligence (CTI) to MITRE ATT&CK lack supporting evidence, provenance tracking, and validation history, making their credibility difficult to assess. This work proposes the first knowledge graph–driven framework for CTI governance that enables auditable management of TTP assertions through fine-grained evidence preservation, complete provenance chains, versioned trust decisions, and lossless revocation mechanisms. The framework integrates multi-extractor collaborative verification, assertion aggregation, consensus modeling, and policy-driven validation, all underpinned by versioned knowledge graph management. Evaluated on 65 CTI reports comprising 5,303 sentences, the approach achieves a precision of 90.6% under six-party consensus and efficiently supports seven categories of audit queries concerning provenance, trustworthiness, and versioning.