The Legal 15%: Liability Is Not a Technical Problem

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19015448  48stabilfr·wuedophcgmx
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The Anthropic Economic Index (Massenkoff & McCrory, 2026) reveals a persistent and structurally significant anomaly: legal occupations exhibit only 15% observed AI exposure despite theoretical automation potential that rivals software engineering. This article examines the economic architecture of that gap. Unlike healthcare, where clinical decision liability and FDA approval pathways create te...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19015448 48stabilfr·wuedophcgmx
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Tax Burden, Digitalization, and Shadow Economy in Ukraine: A Problem Landscape (2015–2025)

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.19008827  54stabilfr·wuedophcgmx
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Ukraine's shadow economy remains one of the largest in Europe, consistently estimated at 30–45% of official GDP over the past decade. This phenomenon directly undermines fiscal stability, distorts market competition, and complicates the country's path toward EU accession. The interaction between tax policy, digital governance, and informal economic activity forms a complex adaptive system where...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.19008827 54stabilfr·wuedophcgmx
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The Measurement Crisis: Saturation, Goodhart’s Law, and the End of AI Leaderboards

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Benchmark Research
Benchmark Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19007432  56stabilfr·wuedophcgmx
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The AI evaluation ecosystem is in crisis. Frontier models now exceed 90% accuracy on MMLU, 95% on HumanEval, and 93% on HellaSwag — scores that were considered unattainable three years ago. This saturation is not evidence of intelligence; it is evidence that our instruments have failed. We argue that three convergent forces have rendered current AI leaderboards meaningless: (1) benchmark satura...

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Benchmark Research by Oleh Ivchenko DOI: 10.5281/zenodo.19007432 56stabilfr·wuedophcgmx
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Buy vs Build in 2026: Why CIOs Are Choosing Integrated Agentic Ecosystems

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19005352  51stabilfr·wuedophcgmx
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The classic "build vs buy" dilemma in enterprise software has been resolved for most AI deployments in 2026 — not by a clear winner, but by a third option that renders the original question obsolete. As Gartner projects worldwide AI spending at $2.5 trillion in 2026, enterprises are abandoning bespoke AI moonshots in favour of orchestrated integration across incumbent vendor ecosystems. This ar...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19005352 51stabilfr·wuedophcgmx
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The Meta-Meta-Analysis: A Systematic Map of What 200 AI Benchmark Studies Actually Measured

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Benchmark Research
Benchmark Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19001033  53stabilfr·wuedophcgmx
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We present a meta-meta-analysis of 217 benchmark evaluation studies published between 2020 and 2026, examining not the benchmarks themselves but the systematic reviews that assess them. Our coverage matrix reveals a profound structural bias: 78.3% of surveyed studies evaluate text-based capabilities, while causal reasoning (4.1%), embodied intelligence (1.8%), and social cognition (0.9%) remain...

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Benchmark Research by Oleh Ivchenko DOI: 10.5281/zenodo.19001033 53stabilfr·wuedophcgmx
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Anticipatory Intelligence in 2026: What Changed, What Didn’t, and What We Got Wrong

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Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18998637  45stabilfr·wuedophcgmx
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Fifteen articles in, we promised a systematic map of gaps in anticipatory intelligence. Now the field has had a year to respond — or not. Foundation models claim temporal reasoning (GPT-5.4, Gemini via Groundsource). The EU AI Act's high-risk provisions hit enforcement. Distribution shift monitoring went from research curiosity to SaaS checkbox. This retrospective measures our predictions again...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18998637 45stabilfr·wuedophcgmx
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Score = Ref Trust (20 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Chapter 15: Data Analysis in the Age of Foundation Models — A 2026 Reassessment

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18998582  61stabilfr·wuedophcgmx
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Across Chapters 1 through 14 of this series, we built a careful taxonomy of data mining methods — classification trees, clustering algorithms, regression models, association rules, and dimensionality reduction techniques. Each method occupied a well-defined place. Each had known strengths, assumptions, and failure modes. That taxonomy served the field faithfully for over two decades.

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18998582 61stabilfr·wuedophcgmx
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[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]2,027✓Minimum 1,000 words of article body. Current: 2,027
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[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness17%○≥60% of references from 2025–2026. Current: 17%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Review: EcoAI-Resilience — When R² = 0.99 Should Make You Nervous, Not Confident

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18998542  64stabilfr·wuedophcgmx
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[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed47%○≥80% have metadata indexed
[l]Academic59%○≥80% from journals/conferences/preprints
[f]Free Access76%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]1,608✓Minimum 1,000 words of article body. Current: 1,608
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18998542
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (52 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

ALsobeh and Alkurdi introduce EcoAI-Resilience, a multi-objective optimization framework that simultaneously targets three goals: maximizing sustainability impact from AI deployment, enhancing economic resilience, and minimizing environmental costs. The framework is trained and validated on data from 53 countries across 14 sectors over the period 2015–2024. The authors report extraordinarily hi...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18998542 64stabilfr·wuedophcgmx
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[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed47%○≥80% have metadata indexed
[l]Academic59%○≥80% from journals/conferences/preprints
[f]Free Access76%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]1,608✓Minimum 1,000 words of article body. Current: 1,608
[u]Original [REQ]98%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 98% unique
[e]Evidence [REQ]11✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 11
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18998542
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness29%○≥60% of references from 2025–2026. Current: 29%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (52 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Technical Gaps Synthesis: Priority Matrix for Anticipatory Intelligence Systems

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Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18994007  69stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources38%○≥80% from editorially reviewed sources
[t]Trusted81%✓≥80% from verified, high-quality sources
[a]DOI31%○≥80% have a Digital Object Identifier
[b]CrossRef13%○≥80% indexed in CrossRef
[i]Indexed44%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access63%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]1,144✓Minimum 1,000 words of article body. Current: 1,144
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18994007
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (60 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Six gaps. $537 billion in annual friction. This synthesis constructs a priority matrix for the technical deficiencies identified across the Anticipatory Intelligence series. Using a three-dimensional scoring framework — economic impact, feasibility, and dependency structure — we rank which problems deserve immediate research investment and which will wait decades for breakthroughs that may neve...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18994007 69stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources38%○≥80% from editorially reviewed sources
[t]Trusted81%✓≥80% from verified, high-quality sources
[a]DOI31%○≥80% have a Digital Object Identifier
[b]CrossRef13%○≥80% indexed in CrossRef
[i]Indexed44%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access63%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]1,144✓Minimum 1,000 words of article body. Current: 1,144
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]9✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 9
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18994007
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness13%○≥60% of references from 2025–2026. Current: 13%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (60 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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The Monitor Shows What Nobody Wants to See: AI Is Here, It Is Eating Jobs, and We Can Only Watch

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Gap Research
Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18993208  62stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed57%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]2,882✓Minimum 1,000 words of article body. Current: 2,882
[u]Original [REQ]99%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 99% unique
[e]Evidence [REQ]20✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 20
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18993208
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (48 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Odesa National Polytechnic University, Department of Economic Cybernetics · PhD Candidate, ML in Pharma Economics

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.18993208 62stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed57%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]2,882✓Minimum 1,000 words of article body. Current: 2,882
[u]Original [REQ]99%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 99% unique
[e]Evidence [REQ]20✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 20
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18993208
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness20%○≥60% of references from 2025–2026. Current: 20%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (48 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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