Mixture of Experts (MoE) architectures have emerged as a pivotal scaling strategy for large language models, promising superior capacity efficiency and specialized functional modularity. This article interrogates the scaling behaviors of MoE systems—specifically Mixtral, DeepSeek‑MoE, and Grok‑1—relative to conventional dense models, aiming to elucidate predictive patterns for frontier model de...
Mobile payment ecosystems generate rich behavioral signals during user interaction, including typing patterns, device motion, and transaction graph topology. This article investigates the efficacy of fusing these behavioral biometrics with graph-based anomaly detection to achieve real-time fraud detection in mobile payment platforms. We address three core research questions: (RQ1) How do behavi...
Behavior-Driven Development (BDD) offers a promising paradigm for structuring specifications of LLM system behavior, yet its adoption faces critical stability challenges when underlying models evolve. This article introduces a robust framework for adapting BDD to specify and test LLM system behavior, focusing on creating human-readable behavioral specifications that maintain integrity across mo...
Coordinated inauthentic behavior (CIB) on social media presents a growing challenge for platform integrity, public discourse, and democratic processes[1]. Recent empirical investigations have demonstrated that malicious actors can amplify false narratives through synchronized account activity, content amplification, and strategic narrative framing[2][3]. While detection frameworks based on netw...
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...