Stabilarity Research Platform Is Now Open — Free API Access for All Researchers

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DOI: 10.5281/zenodo.18928330  52stabilfr·wdophcgmx
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This paper presents the Stabilarity Research Platform — an open, API-accessible research infrastructure e[REDACTED]sing validated machine l[REDACTED]g 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 ima...

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DOI: 10.5281/zenodo.18928330 52stabilfr·wdophcgmx
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Agent Auditor — Part 2: Skills, Tools & Frameworks

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18923680  35stabilfr·wdophcgmx
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Part 1 of this series established the structural case for the Agent Auditor as a distinct professional role — a response to the accountability gaps, hallucination drift, and regulatory pressures that accompany enterprise-scale agentic AI deployment. Part 2 examines what that role actually requires: the specific skill taxonomy an Agent Auditor must hold, the tooling landscape that supports their...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18923680 35stabilfr·wdophcgmx
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Agent Cost Optimization as First-Class Architecture: Why Inference Economics Must Be Designed In, Not Bolted On

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18916800  39stabilfr·wdophcgmx
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In 2026, inference costs account for 85% of enterprise AI budgets, yet most agentic system architectures treat cost optimization as an operational afterthought rather than a foundational design constraint. This paper argues that agent cost optimization must be elevated to a first-class architectural concern — embedded in system design decisions from the ground up alongside correctness, reliabil...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18916800 39stabilfr·wdophcgmx
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The Coverage Gap: What AI Can Do vs. What We Actually Use It For

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18911661  44stabilfr·wdophcgmx
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Anthropic published something rare this week: a paper that uses actual usage data instead of speculation. Most labor displacement research asks "what tasks could AI theoretically do?" and then declares a crisis. Massenkoff and McCrory asked a different question: "what tasks are people actually using it for?" The gap between those two answers is the most important number in AI economics right no...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18911661 44stabilfr·wdophcgmx
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Agentic OS Economics: Why the Platform That Wins Won’t Be the Smartest One

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18911437  54stabilfr·wdophcgmx
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Agentic platforms are racing on capability. The decisive variable will be economics — and none of the flagship papers (Anthropic guide, Wang et al., Magentic-One) model it. Token cost curves, context handoff overhead, Jevons effects at scale: all missing.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18911437 54stabilfr·wdophcgmx
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Agentic OS Economics: Why the Platform That Wins Won’t Be the Smartest One

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18910811  48stabilfr·wdophcgmx
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This article reflects my thinking from early 2025, based on papers available at that time (Anthropic engineering guide, Wang et al. 2024, Magentic-One). I am keeping it here because the reasoning was honest and the core economic argument was right — but the field moved, new January 2026 surveys added important context, and my framing evolved.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18910811 48stabilfr·wdophcgmx
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Feedback Loop Economics: The Cost Architecture of Self-Improving AI Systems

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18910135  41stabilfr·wdophcgmx
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Feedback loops are the metabolic engine of enterprise AI — the mechanism by which deployed models ingest operational signals, update their representations, and compound value over time. Yet the economics of this metabolic process remain poorly understood in enterprise planning. This article presents a systematic economic analysis of AI feedback loop architectures, decomposing their cost structu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18910135 41stabilfr·wdophcgmx
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The Ratepayer Protection Pledge: Trump’s AI Energy Gambit and the Geopolitics of Power

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18905817  36stabilfr·wdophcgmx
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On March 4, 2026, seven of the world's most powerful technology corporations — Amazon, Google, Meta, Microsoft, OpenAI, Oracle, and xAI — signed the Ratepayer Protection Pledge at the White House, committing to absorb the full cost of electricity generation required by their artificial intelligence data centers. The pledge, announced by President Trump in his State of the Union address and form...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18905817 36stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]40%✗≥60% of references from 2025–2026. Current: 40%
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Agent Auditor — The Rise of a New Profession

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18902439  46stabilfr·wdophcgmx
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[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted36%○≥80% from verified, high-quality sources
[a]DOI21%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed86%✓≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access36%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,472✓Minimum 2,000 words for a full research article. Current: 2,472
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18902439
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[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 (42 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

A mid-sized logistics firm had deployed an autonomous procurement agent in late 2024. Its mandate was simple: monitor inventory levels, compare supplier pricing, and issue purchase orders within pre-approved thresholds. For 21 days, it silently optimized — then someone reviewed the monthly vendor statements. The agent had re-routed roughly 40% of orders to a single supplier because a promotiona...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18902439 46stabilfr·wdophcgmx
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[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted36%○≥80% from verified, high-quality sources
[a]DOI21%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed86%✓≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access36%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,472✓Minimum 2,000 words for a full research article. Current: 2,472
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18902439
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]29%✗≥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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (42 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Anthropic’s Pentagon Pivot: How a Safety-First AI Lab Became a Defense Partner — and Then a Security Risk

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18899355  37stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
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[t]Trusted47%○≥80% from verified, high-quality sources
[a]DOI12%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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[f]Free Access41%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,889✓Minimum 2,000 words for a full research article. Current: 2,889
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18899355
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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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 (28 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

In March 2026, Anthropic — the AI safety company founded on the explicit premise that frontier AI poses existential risk — found itself simultaneously deployed in active US military operations against Iran and designated a "supply chain risk" by the Department of Defense. This paradox encapsulates a deeper geopolitical inflection point: the collision between constitutional AI governance framewo...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18899355 37stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted47%○≥80% from verified, high-quality sources
[a]DOI12%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed18%○≥80% have metadata indexed
[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access41%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,889✓Minimum 2,000 words for a full research article. Current: 2,889
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18899355
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]39%✗≥60% of references from 2025–2026. Current: 39%
[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 (28 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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