The rapid proliferation of AI-generated technical content demands reliable automated quality indicators that can be computed at scale. This article investigates two such indicators—claim density and evidence ratio—and evaluates their effectiveness as proxies for expert-perceived quality. We define claim density as the proportion of sentences that contain testable assertions within a technical p...
Observability in federated l[REDACTED]g environments suffers from fragmented taxonomy of artifacts, hindering coherent reporting and cross‑agent integration. This article introduces a standardized taxonomy that classifies observability artifacts across distributed agent ecosystems, enabling interoperable metadata exchange and unified analytics. We formalize eight core artifact categories, defin...
The rapid proliferation of autonomous AI agents operating across decentralized infrastructures has intensified the need for coherent observability frameworks that can consistently capture, categorize, and report on system states. Existing approaches vary widely in scope, granularity, and semantic alignment, leading to fragmented reporting practices that hinder cross-agent collaboration and long...
Enterprise AI workloads increasingly rely on batch inference to amortize GPU costs and improve throughput. However, the proliferation of batching strategies—continuous batching, static batching, and priority-aware scheduling—introduces complexity in selecting an optimal approach for production environments. This article addresses three critical research questions: (1) How do continuous and stat...
Introduction The rapid diffusion of large language models (LLMs) across knowledge‑intensive industries has sparked intense debate about the extent to which specific tasks can be automated, augmented, or remain uniquely human. Existing surveys often aggregate diverse activities into broad categories, obscuring the nuanced dynamics that shape labor markets and wage trajectories. This article dise...
Artificial intelligence systems increasingly operate in dynamic environments where data distributions evolve and user feedback loops shape model behavior. Despite their adaptive design, many deployed models experience performance degradation over time, leading to unreliable predictions and potential business impact. This article investigates the mechanisms through which feedback loop failures m...
The rapid militarization of orbital space has created an urgent need for automated monitoring of satellite behavior. This article presents a comprehensive AI-driven framework for space domain awareness, focusing on satellite activity monitoring and anti-satellite threat detection. We introduce a multi-modal deep l[REDACTED]g architecture that integrates orbital telemetry, optical imaging, and r...
The rapid proliferation of AI-generated scholarly content has created an urgent need for reliable automated quality metrics that can supplement or replace human expert evaluation. This article investigates the calibration between expert evaluators and a suite of algorithmic metrics across a diverse corpus of synthetic articles. We examine three research questions: (RQ1) What is the correlation ...
Academic Citation: Ivchenko, Oleh, Ivchenko, Iryna (2026). Human‑in‑the‑Loop Auditing: Structured Interaction Patterns for Trust Calibration in High‑Stakes AI. Research article: Human‑in‑the‑Loop Auditing: Structured Interaction Patterns for Trust Calibration in High‑Stakes AI. Odessa National Polytechnic University, Department of Economic Cybernetics. DOI: 10.5281/zenodo.21753075 · ...
Quantized model formats have become central to cost-effective deployment of large AI models in enterprise environments. This article compares three prominent quantization formats—GGUF, ONNX Runtime with QDQ, and AWQ—focusing on accuracy–cost trade‑offs, integration complexity, and scalability in on‑premise settings. We formulate three research questions (RQ1, RQ2, RQ3) that guide the analysis: ...