Specialized vs General Models — When to Use Domain-Specific AI

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18743173  45stabilfr·wdophcgmx
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Academic Citation: Ivchenko, O. (2026). Specialized vs General Models — When to Use Domain-Specific AI. Cost-Effective Enterprise AI Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18746111 Abstract The enterprise AI landscape is undergoing a fundamental shift from general-purpose large language models (LLMs) to domain-specific language models (DSLMs) optimized for particula...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18743173 45stabilfr·wdophcgmx
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[h]Freshness [REQ]13%✗≥60% of references from 2025–2026. Current: 13%
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Integration Economics: Legacy System Adaptation for AI Deployment

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18740871  34stabilfr·wdophcgmx
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Integrating artificial intelligence into existing enterprise infrastructure represents one of the most significant economic challenges in AI deployment. While substantial research examines AI development costs, the economics of legacy system adaptation remain inadequately explored. This paper presents a comprehensive economic framework for understanding integration costs, analyzing cost structu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18740871 34stabilfr·wdophcgmx
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AI Agents Operate With Minimal Safety Disclosures: MIT Study Reveals Transparency Gap

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18741627  34stabilfr·wdophcgmx
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
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Score = Ref Trust (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

MIT CSAIL's 2025 AI Agent Index analyzed 30 prominent AI agents and found a striking transparency deficit: while 70% provide documentation and nearly half publish code, only 19% disclose formal safety policies and fewer than 10% report external safety evaluations. This journal entry examines the study's findings, contextualizes the claims within the broader AI safety discourse, and assesses whe...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18741627 34stabilfr·wdophcgmx
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[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
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Predicting Armed Conflict Probability: A Multi-Factor Machine Learning Approach

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18735965  51stabilfr·wdophcgmx
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[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
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Armed conflict prediction represents one of the most critical challenges in computational social science and international relations. This paper presents a multi-factor machine l[REDACTED]g approach to predicting armed conflict probability at the country level, combining ensemble l[REDACTED]g methods with diverse data sources including ACLED, UCDP, World Bank economic indicators, SIPRI military...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18735965 51stabilfr·wdophcgmx
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Development Paradigms Compared: Spec-Driven, Experiment-Driven, and Hybrid Approaches

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18741619  46stabilfr·wdophcgmx
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Score = Ref Trust (43 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The development of AI systems presents unique challenges that traditional software engineering paradigms struggle to address. This article provides a comprehensive comparative analysis of four major development approaches: spec-driven development, experiment-driven development, data-centric AI, and model-centric AI. We examine each paradigm's theoretical foundations, practical workflows, and su...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18741619 46stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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Open Source LLMs in Production — Llama, Mistral, and Beyond

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18741621  45stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Throughout my career deploying AI systems at enterprise scale, I have observed a fundamental shift in how organizations approach large language model (LLM) infrastructure. The emergence of high-quality open source models from Meta, Mistral AI, Alibaba, and others has transformed the economics of enterprise AI deployment. Where organizations once faced a binary choice between expensive proprieta...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18741621 45stabilfr·wdophcgmx
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741621
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
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Compliance Costs: GDPR, AI Act, and Industry-Specific Regulations

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730888  36stabilfr·wdophcgmx
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[a]DOI11%○≥80% have a Digital Object Identifier
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[i]Indexed8%○≥80% have metadata indexed
[l]Academic8%○≥80% from journals/conferences/preprints
[f]Free Access32%○≥80% are freely accessible
[r]References37 refs✓Minimum 10 references required
[w]Words [REQ]3,425✓Minimum 2,000 words for a full research article. Current: 3,425
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730888
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]18%✗≥60% of references from 2025–2026. Current: 18%
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[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (25 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Regulatory compliance represents a critical economic dimension of enterprise AI deployment, with costs ranging from $20,000 for small implementations to over $15 million for large-scale high-risk systems. This article analyzes compliance cost structures across major regulatory frameworks — GDPR, EU AI Act, FDA medical device regulations, and financial services requirements — providing quantitat...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18730888 36stabilfr·wdophcgmx
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[f]Free Access32%○≥80% are freely accessible
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[w]Words [REQ]3,425✓Minimum 2,000 words for a full research article. Current: 3,425
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730888
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]18%✗≥60% of references from 2025–2026. Current: 18%
[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 (25 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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When AI Finally Beats the Experts: DeepRare and the End of the Diagnostic Odyssey

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730582  49stabilfr·wdophcgmx
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[s]Reviewed Sources36%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI36%○≥80% have a Digital Object Identifier
[b]CrossRef29%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic57%○≥80% from journals/conferences/preprints
[f]Free Access21%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,263✓Minimum 2,000 words for a full research article. Current: 2,263
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730582
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]27%✗≥60% of references from 2025–2026. Current: 27%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18730582 49stabilfr·wdophcgmx
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[s]Reviewed Sources36%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI36%○≥80% have a Digital Object Identifier
[b]CrossRef29%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic57%○≥80% from journals/conferences/preprints
[f]Free Access21%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,263✓Minimum 2,000 words for a full research article. Current: 2,263
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730582
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]27%✗≥60% of references from 2025–2026. Current: 27%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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OpenAI vs Anthropic vs Google: Enterprise Provider Comparison 2026

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730535  27stabilfr·wdophcgmx
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[s]Reviewed Sources2%○≥80% from editorially reviewed sources
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The enterprise AI landscape in 2026 presents organizations with a critical strategic decision: which large language model (LLM) provider should anchor their AI infrastructure? This comparative analysis examines the three dominant commercial providers—OpenAI, Anthropic, and Google—across dimensions of pricing, performance, enterprise features, technical capabilities, and total cost of ownership....

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730535 27stabilfr·wdophcgmx
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[a]DOI7%○≥80% have a Digital Object Identifier
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730535
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Score = Ref Trust (11 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Security Investment — Adversarial Attack Prevention

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730508  63stabilfr·wdophcgmx
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Adversarial attacks represent a critical security threat to machine l[REDACTED]g systems, with global estimated losses reaching approximately $6 trillion in 2021—double the costs recorded in previous years. This article presents a comprehensive economic framework for evaluating security investments in adversarial attack prevention, analyzing the cost-benefit tradeoffs of defense mechanisms incl...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18730508 63stabilfr·wdophcgmx
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[i]Indexed94%✓≥80% have metadata indexed
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730508
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (70 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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