Unreported income among small and medium enterprises (SMEs) remains a critical challenge for tax authorities globally, with recent studies indicating that undeclared income accounts for 15-25% of the tax gap in European OECD nations. This article addresses the gap in current tax compliance tools by developing and evaluating machine l[REDACTED]g models specifically designed to detect anomalous c...
The rapid evolution of large language models (LLMs) has e[REDACTED]sed scalability bottlenecks inherent in the Transformer architecture, particularly its quadratic complexity in attention computation. Recent advances propose alternative paradigms—state‑space models (SSMs) such as Mamba and RWKV, as well as hybrid architectures that blend linear attention with selective state propagation—as viab...
Property-based testing (PBT) has emerged as a systematic method for uncovering edge-case failures in complex software systems [1]. Recent extensions to nondeterministic domains, particularly large language models (LLMs), enable the definition of invariants that must hold across varying model outputs [2]. This article introduces a framework for applying PBT to LLM-powered systems, focusing on hy...
Speculative decoding is an inference acceleration technique that leverages a lightweight draft model to propose tokens which are subsequently verified by a target model. This abstract outlines a production-focused benchmark of three speculative decoding implementations — Medusa, Eagle, and SpecTr — evaluated across a diverse set of real-world workloads. We quantify throughput improvements, late...
Enterprise adoption of large language models (LLMs) has progressed from experimental pilots to core production workloads, yet most organizations continue to compute return on investment (ROI) using GPU‐hour pricing as the sole cost driver. This narrow view systematically underestimates the true economic burden of LLMs, omitting fine‑tuning expenses, retrieval‑augmented generation (RAG) infrastr...
The proliferation of AI-driven geopolitical risk forecasting has transformed conflict prediction methodologies, yet systematic validation against real-world outcomes remains incomplete. This study conducts a retrospective evaluation of five major forecasting platforms—including PredictIt, Metaculus, and three commercial vendors—against 47 documented conflict escalations between January 2024 and...
Enterprise adoption of artificial intelligence suffers from a structural mismatch between procurement timelines and model release frequencies. This article quantifies the misalignment between 18‑24 month enterprise buying cycles and 6‑month AI model deprecation cycles, identifies established mitigation strategies, and evaluates their effectiveness using empirical metrics. We formulate three res...
Citation hallucination — the generation of fabricated bibliographic references by large language models (LLMs) — poses a critical reproducibility risk for AI‑driven scholarly output. This article benchmarks citation hallucination rates across ten leading LLMs released between 2023 and 2025, measuring the prevalence of fabricated citations in response to standardized research‑question prompts. W...
This article investigates the regulatory landscape surrounding explanation quality monitoring for financial artificial intelligence systems deployed in credit scoring and algorithmic trading environments. Recent regulatory initiatives have begun to mandate transparent explanatory mechanisms for AI-driven financial decisions, aiming to enhance consumer protection and systemic risk mitigation. Ho...
Enterprises increasingly rely on artificial intelligence (AI) to gain competitive advantage, yet many hesitate to adopt open source AI solutions despite their technical promise and cost efficiency. This hesitation stems from a growing trust gap—a mismatch between the expectations of corporate stakeholders and the capabilities, governance, and reliability of community‑driven AI projects. Bridgin...