Ronald Doku's "The Confidence Gate Theorem: When Should Ranked Decision Systems Abstain?" (arXiv:2603.09947, March 2026) addresses a practical but undertheorized problem: in ranked decision systems — recommenders, ad auctions, clinical triage — when does it help to withhold a prediction rather than fire it? Doku proposes two formal conditions — rank-alignment and absence of inversion zones — un...
This article describes the development and deployment of the World Stability Intelligence (WSI) system — a machine learning-driven geopolitical risk monitoring platform that continuously tracks 87 countries across three risk dimensions: war risk (45%), political risk (35%), and economic risk (20%). Drawing on an ML-enhanced heuristic prediction framework (HPF-P), the system generates normalized...
A personal commentary on an unexpected Medium citation of research on AI infrastructure ROI. Clarifying the nuance between measured economic analysis and pessimistic interpretations, with a reflection on AGI proximity and a thank you to the author who sparked the conversation.
The story starts in a classroom, as most research stories do — though this particular classroom was unofficial. Around 2019, Oleh Ivchenko began running supplementary IT courses at Odessa National Polytechnic University. Not because the institution asked him to, but because the gap between what students were being taught and what the industry actually needed had become too large to ignore. He r...
Large language model (LLM) based agents are increasingly deployed as autonomous report-generation systems — producing research summaries, analytical outputs, and monitoring digests across extended time horizons without continuous human supervision. This paper examines the fundamental challenges of longitudinal consistency in such systems: context window exhaustion, semantic drift, hallucination...
The most consequential question in applied artificial intelligence is not whether a model achieves state-of-the-art on a leaderboard. It is whether the model does something useful when connected to reality — to messy data, constrained infrastructure, and users who need answers rather than probabilities. This article examines what AI actually looks like when it crosses that boundary. Drawing on ...
This paper presents the Stabilarity Research Platform — an open, API-accessible research infrastructure exposing validated machine learning models, geopolitical risk datasets, and decision optimization tools to the global research community at no cost. The platform implements FAIR data principles (Wilkinson et al., 2016), providing composable, versioned endpoints for: (1) medical imaging classi...
We can already observe the development and implementation of artificial intelligence in various spheres of human activity. And, strange as it may seem, Ukraine's success in using advanced technologies, particularly in the military sphere, is logically and predictably driven by its need to survive in a challenging war against a powerful adversary. While the use of artificial intelligence in othe...
On February 28, 2026, the United States and Israel launched coordinated military strikes on Iran, marking the most significant Middle Eastern conflict escalation since the Iraq War. Our Stabilarity War Prediction Model had been tracking Iran's conflict probability for weeks, showing a 49.7% conflict probability with an increasing trend — a warning that materialized into reality within hours of ...
A new AI system published in Nature has achieved what many thought impossible: diagnosing rare diseases more accurately than experienced physicians. DeepRare, developed by researchers led by Zhao et al., demonstrates 64.4% top-1 diagnostic accuracy compared to 54.6% for human experts with over a decade of clinical experience. Tested across 6,401 cases spanning 2,919 diseases, the system provide...