Standardized Observability Taxonomies for Multi-Agent AI Systems in Decentralized Networks

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21812614  59stabilfr·wuedophcgmx
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[a]DOI39%○≥80% have a Digital Object Identifier
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[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access61%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]1,631✓Minimum 1,000 words of article body. Current: 1,631
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21812614
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[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (41 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

The rapid proliferation of autonomous AI agents operating across decentralized infrastructures has intensified the need for coherent observability frameworks that can consistently capture, categorize, and report on system states. Existing approaches vary widely in scope, granularity, and semantic alignment, leading to fragmented reporting practices that hinder cross-agent collaboration and long...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.21812614 59stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted61%○≥80% from verified, high-quality sources
[a]DOI39%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access61%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]1,631✓Minimum 1,000 words of article body. Current: 1,631
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]15✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 15
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21812614
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness60%✓≥60% of references from 2025–2026. Current: 60%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (41 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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The Capability Adoption Stack: A Framework for Diagnosing Enterprise AI Readiness

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21715745  62stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI3%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic90%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]1,541✓Minimum 1,000 words of article body. Current: 1,541
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]26✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 26
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21715745
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[h]Freshness90%✓≥60% of references from 2025–2026. Current: 90%
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[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 (3/7 × 10%)

Enterprise AI adoption remains fragmented, with organizations struggling to diagnose their readiness across technical, human, and governance dimensions[1]. This article introduces the Capability Adoption Stack (CAS), a layered diagnostic model that maps data infrastructure, talent, governance, and integration maturity into a coherent framework[2]. By operationalizing these dimensions into measu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21715745 62stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI3%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic90%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]1,541✓Minimum 1,000 words of article body. Current: 1,541
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]26✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 26
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21715745
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness90%✓≥60% of references from 2025–2026. Current: 90%
[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 (3/7 × 10%)
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Foundation Model Commoditization: How API Parity Is Reshaping the AI Stack in 2025

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21550109  74stabilfr·wuedophcgmx
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[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI84%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]1,949✓Minimum 1,000 words of article body. Current: 1,949
[u]Original [REQ]97%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 97% unique
[e]Evidence [REQ]15✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 15
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21550109
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[h]Freshness89%✓≥60% of references from 2025–2026. Current: 89%
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[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

In this article we analyze the commoditization of foundation model APIs, showing how parity in capability metrics is reshaping competitive dynamics across the AI stack in 2025. Using a mixed-methods approach combining benchmark analysis, cost modeling, and market trend evaluation, we identify three dominant research questions: (RQ1) How have benchmark scores converged across leading frontier mo...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21550109 74stabilfr·wuedophcgmx
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[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI84%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]1,949✓Minimum 1,000 words of article body. Current: 1,949
[u]Original [REQ]97%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 97% unique
[e]Evidence [REQ]15✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 15
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21550109
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness89%✓≥60% of references from 2025–2026. Current: 89%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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Rare Earth Geopolitics and the AI Supply Chain: Modeling Mineral Concentration Risk

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21510558  71stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted85%✓≥80% from verified, high-quality sources
[a]DOI77%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic85%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]1,535✓Minimum 1,000 words of article body. Current: 1,535
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21510558
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness50%○≥60% of references from 2025–2026. Current: 50%
[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 (63 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

The global AI hardware boom has intensified dependence on rare earth elements (REEs) for high‑performance GPUs, TPUs, and specialized accelerators. While the United States and Europe have increased mining exploration budgets by 42% in 2025, China continues to control over 60% of rare earth processing capacity, creating a critical bottleneck for AI infrastructure scaling. This article quantifies...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.21510558 71stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted85%✓≥80% from verified, high-quality sources
[a]DOI77%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic85%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]1,535✓Minimum 1,000 words of article body. Current: 1,535
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21510558
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness50%○≥60% of references from 2025–2026. Current: 50%
[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 (63 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Opportunity Cost of AI Waiting: Economic Modeling of Delayed Enterprise AI Adoption

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21505176  74stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted91%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,605✓Minimum 1,000 words of article body. Current: 1,605
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21505176
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (66 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

Enterprises that delay AI adoption face a measurable competitive disadvantage that can be quantified in economic terms. This article answers three research questions: (RQ1) What is the average competitive disadvantage cost for enterprises that delay AI adoption by 12–24 months relative to early adopters? (RQ2) Which industry sectors exhibit the highest cost differentials? (RQ3) What are the mac...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21505176 74stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted91%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,605✓Minimum 1,000 words of article body. Current: 1,605
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21505176
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (66 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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Specification Coverage Metrics for AI Systems: Adapting MC/DC and Branch Coverage

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Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21385452  74stabilfr·wuedophcgmx
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[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References24 refs✓Minimum 10 references required
[w]Words [REQ]2,043✓Minimum 1,000 words of article body. Current: 2,043
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]18✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 18
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21385452
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

The rapid integration of artificial intelligence (AI) into Safety‑Critical and High‑Performance Computing (HPC) domains demands formally verifiable assurance techniques that can certify model behavior against formally expressed specifications. Traditional software engineering employs code‑coverage criteria such as Modified Condition/Decision Coverage (MC/DC) and branch coverage to demonstrate t...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.21385452 74stabilfr·wuedophcgmx
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[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References24 refs✓Minimum 10 references required
[w]Words [REQ]2,043✓Minimum 1,000 words of article body. Current: 2,043
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]18✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 18
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21385452
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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Cash Flow Anomaly Detection: AI Models for Identifying Undeclared Income in SME Tax Returns

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.23486189  56stabilfr·wuedophcgmx
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[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]Indexed0%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,649✓Minimum 1,000 words of article body. Current: 1,649
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]1✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 1
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486189
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness25%○≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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 learning models specifically designed to detect anomalous cash ...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.23486189 56stabilfr·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]Indexed0%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,649✓Minimum 1,000 words of article body. Current: 1,649
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]1✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 1
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486189
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness25%○≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Total Cost of Ownership for Enterprise LLMs: A 2025 Framework Beyond GPU Cost

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21186552  64stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI57%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic71%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,788✓Minimum 1,000 words of article body. Current: 1,788
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]15✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 15
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21186552
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness67%✓≥60% of references from 2025–2026. Current: 67%
[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 (50 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

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...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21186552 64stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI57%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic71%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,788✓Minimum 1,000 words of article body. Current: 1,788
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]15✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 15
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21186552
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness67%✓≥60% of references from 2025–2026. Current: 67%
[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 (50 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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Citation Hallucination Rates in LLM-Generated Research: A 2025 Benchmark Across 10 Models

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21131031  57stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]1,632✓Minimum 1,000 words of article body. Current: 1,632
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]2✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 2
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21131031
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness25%○≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

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...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.21131031 57stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]1,632✓Minimum 1,000 words of article body. Current: 1,632
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]2✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 2
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21131031
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness25%○≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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