Flash Attention has become the foundational kernel technology enabling memory-efficient inference in large language models (LLMs), transforming how attention computation interacts with GPU memory hierarchies. This article investigates three research questions: (1) how does Flash Attention's tiling strategy reduce peak memory consumption compared to standard attention, and what are the theoretic...
As large language models (LLMs) scale to context windows exceeding one million tokens, the key-value (KV) cache grows linearly and becomes the dominant memory bottleneck during autoregressive inference. Sliding window attention and compressive caching represent two complementary families of techniques that bound memory usage while preserving access to long-range context. This article investigat...
As large language models (LLMs) scale to billions of parameters and context windows stretch beyond 128K tokens, the key-value (KV) cache becomes the dominant memory bottleneck during inference. Cross-layer KV-cache sharing represents a family of techniques that exploit redundancy in key and value representations across transformer layers to reduce cache memory without retraining. This article i...
The value-added tax (VAT) compliance gap represents the difference between theoretical VAT liability and actual VAT revenue collected, serving as a primary quantitative indicator of tax evasion and shadow economic activity. This article examines the methodological landscape for VAT gap estimation, applies a comparative framework to Ukraine's fiscal context, and benchmarks Ukrainian VAT performa...
Pharmaceutical portfolio management operates under persistent uncertainty from supply chain disruptions, regulatory shifts, and demand volatility. While the HPF-P framework provides Decision Readiness Index (DRI) and Decision Readiness Level (DRL) metrics for portfolio assessment, their behavior under extreme stress conditions remains uncharacterized. This article develops a multi-scenario stre...
This article investigates token pruning and attention sparsity as complementary strategies for reducing KV-cache memory consumption during large language model inference. Building on our series analysis of semantic prompt caching, we examine how selective token removal and sparse attention patterns can achieve 50-80% memory reduction while preserving generation quality. Three research questions...
Open-source benchmark frameworks have become the backbone of AI model evaluation, yet none provides simultaneous coverage of multidimensional intelligence measurement, inference cost normalization, and cryptographic reproducibility certification. This article presents the architecture and design rationale for the Universal Intelligence Benchmark (UIB) open-source suite, a modular evaluation fra...
Ukraine's shadow economy constitutes one of the most persistent structural challenges to its fiscal sustainability and governance reform agenda. While national-level estimates have been widely studied, the regional dimension — how shadow activity distributes across Ukraine's 25 oblasts — remains underexplored in quantitative literature. This article presents an oblasts-level analysis of shadow ...
The open-source education technology landscape has undergone rapid transformation in early 2026, driven by the convergence of large language model capabilities with established pedagogical frameworks. This article surveys emerging open-source repositories created within the past 60 days that address AI-powered tutoring, automated assessment, and multi-agent classroom simulation. We evaluate fiv...
This article examines the relationship between digital payment adoption and shadow economy reduction in Ukraine, with particular focus on the Diia government services platform as a digitalization catalyst. Drawing on National Bank of Ukraine transaction data (2015–2025), cross-country panel evidence, and sector-level informality estimates, we investigate whether cashless payment penetration cau...