Blockchain-based cryptocurrency markets present unique analytical challenges due to their decentralized nature, continuous operation, and extreme volatility. Traditional price prediction models often struggle with the binary trade execution problem in these markets. This study introduces a confidence-based classification framework that separates directional prediction from execution decisions in cryptocurrency trading. We develop a neural network system that processes multi-scale market data, combining daily macroeconomic indicators with a high-frequency order book microstructure. The model trains exclusively on directional movements (up versus down) and uses prediction confidence levels to determine trade execution. We evaluate the framework across 11 major cryptocurrency pairs over 12 months. Experimental results demonstrate 82.68% direction accuracy on executed trades with 151.11-basis point average net profit per trade at 11.99% market coverage. Order book features dominate predictive importance (81.3% of selected features), validating the critical role of blockchain microstructure data for short-term price prediction. The confidence-based execution strategy achieves superior risk-adjusted returns compared to traditional classification approaches while providing natural risk management capabilities through selective trade execution. These findings contribute to blockchain technology applications in financial markets by demonstrating how a decentralized market microstructure can be leveraged for systematic trading strategies. The methodology offers practical implementation guidelines for cryptocurrency algorithmic trading while advancing the understanding of machine learning applications in blockchain-based financial systems.

Evaluating large language models (LLMs) in psychologically sensitive and human-centered domains faces two persistent challenges. First, conventional benchmarks capture instrumental capabilities but often fail to represent model behavior in open-ended dialogue where emotional context, conflict, ambiguity, and user safety define quality. Second, LLM outputs can be unstable across re-runs and highly sensitive to prompt phrasing, undermining reproducibility and cross-model comparisons [1]. This article introduces an applied framework of psychoactive triggers: standardized textual stimuli designed to evoke systematic shifts in response style, narrative coherence, explanatory stance, empathy calibration, and risk regulation. Psychoactive triggers are treated as an analogue of psychometric items adapted to LLMs: each trigger carries a controlled psychological load (e.g., threat, shame, guilt, control, intimacy, autonomy), allowing measurement of stable behavioral patterns rather than binary correctness. The framework is illustrated using the PersonaMatrix ecosystem, where trigger batteries are applied in multiple measurement waves. A four-class metric taxonomy is proposed, with this paper focusing on Class I metrics—reproducibility and stability (RSI/IDS/RCS)—using a single PersonaMatrix test, “What Is My Character Type?” (TestPersona). Written at the intersection of LLM research and clinical psychology, the article provides clinical rationale and ethical constraints for safe deployment of psychologically loaded evaluations.

This article presents a comparative analysis of the legal regulation of mediation in Ukraine and the United States between 2021 and 2026 in light of an emerging challenge: the use of artificial intelligence tools in mediation practice. The study applies the comparative legal method, doctrinal analysis of primary sources (the Law of Ukraine “On Mediation,” the Uniform Mediation Act, Regulation (EU) 2024/1689, and the institutional rules of ADR providers), and a critical review of the scholarly literature. The analysis shows that neither jurisdiction has adopted a binding rule defining the limits of a mediator's use of AI, disclosure duties toward the parties, or the allocation of responsibility for errors in AI-generated content. At the same time, the article develops the author's position that the very nature of mediation calls into question the need for strict statutory regulation of AI in this field. A mediator renders no decision; legal force attaches to the parties' settlement agreement, and state enforcement secures that agreement rather than any act of the neutral. This reasoning finds support in EU law: under Recital 61 of Regulation (EU) 2024/1689 and the European Commission's draft guidelines of May 19, 2026, the high-risk classification of AI systems in ADR turns on whether the outcome of the procedure produces legal effects for the parties. The article proposes a differentiated approach: instead of regulating the technology, the proper object of regulation is the mediator's personal responsibility for verifying AI-generated content, with possible exceptions for particular categories of disputes.

Purpose: This article undertakes a systematic comparative analysis of artificial intelligence regulation in legal practice across two jurisdictions — Ukraine and the United States — during the period 2023–2026. The study examines how each legal system has responded to the integration of generative AI into attorney practice, with particular attention to professional ethics obligations, judicial precedent, executive action, and harmonization trajectories. Methodology: The study employs comparative legal analysis, doctrinal examination of primary sources (legislation, professional conduct rules, formal ethics opinions), case law review, and systematic analysis of regulatory instruments across both legal systems and EU law. Findings: Neither jurisdiction has enacted comprehensive AI-specific legislation for legal professionals as of 2025. The United States has developed more specific professional standards through ABA Formal Opinion 512 (2024) and a body of judicial precedent anchored by Mata v. Avianca (2023). The Executive Order of June 2, 2026, introduced a voluntary federal AI oversight mechanism while affirming the administration's preference for industry self-regulation. Ukraine is pursuing a bottom-up regulatory approach aligned with EU AI Act obligations. A shared regulatory gap in both systems concerns AI in mediation and ADR. Conclusions: Both systems rely on general professional ethics principles as their primary regulatory instruments. They diverge significantly in the specificity of guidance, existence of judicial precedent, and regulatory harmonization trajectory. The article formulates concrete recommendations for NAUB, Ukrainian legislators, and US bar associations.

The rapid convergence of virtual and augmented reality technologies, distributed ledgers, wearable sensors, and generative artificial intelligence is shaping a new sociotechnical space — the Metaverse, which, according to analyst agencies, is capable of growing from tens of billions of dollars in 2021-2023. to several trillion dollars in the mid-2030s. Simultaneously with the economic potential, this convergence gives rise to qualitatively new cybersecurity challenges that go beyond traditional models of information protection, as they relate not only to data, but also to the bodily experience, identity and psychological integrity of the user. Based on a systematic analysis of specialized scientific literature, industry reports and empirical studies, the article classifies cybersecurity threats in the Metaverse environment in five areas: technical and infrastructure, identification and authentication, data privacy, socio-behavioral and algorithmic-legal. The specifics of threats in the healthcare segment are analyzed, empirical data on the public perception of the responsibility of automated agents for the damage caused are considered, and a comparative legal analysis of the approaches of the European Union and Ukraine to the regulation of high-risk artificial intelligence systems is carried out. Based on the results of the study, a set of areas for improving national legislation is proposed, including the adaptation of a risk-based approach, the development of a three-class model of legal personality of artificial intelligence systems and the introduction of cross-border digital jurisdiction mechanisms.