Skip to content

Stabilarity Hub

Menu
  • Home
  • Research
    • Healthcare & Life Sciences
      • Medical ML Diagnosis
    • Enterprise & Economics
      • AI Economics
      • Cost-Effective AI
      • Spec-Driven AI
    • Geopolitics & Strategy
      • Anticipatory Intelligence
      • Future of AI
      • Geopolitical Risk Intelligence
    • AI & Future Signals
      • Capability–Adoption Gap
      • AI Observability
      • AI Intelligence Architecture
      • AI Memory
      • Trusted Open Source
    • Data Science & Methods
      • HPF-P Framework
      • Intellectual Data Analysis
      • Reference Evaluation
    • Publications
      • External Publications
    • Robotics & Engineering
      • Open Humanoid
      • Open Starship
    • Benchmarks & Measurement
      • Universal Intelligence Benchmark
      • Shadow Economy Dynamics
      • Article Quality Science
  • Tools
    • Healthcare & Life Sciences
      • ScanLab
      • AI Data Readiness Assessment
    • Enterprise Strategy
      • AI Use Case Classifier
      • ROI Calculator
      • Risk Calculator
      • Reference Trust Analyzer
    • Portfolio & Analytics
      • HPF Portfolio Optimizer
      • Adoption Gap Monitor
      • Data Mining Method Selector
    • Geopolitics & Prediction
      • War Prediction Model
      • Ukraine Crisis Prediction
      • Gap Analyzer
      • Geopolitical Stability Dashboard
    • Technical & Observability
      • OTel AI Inspector
    • Robotics & Engineering
      • Humanoid Simulation
    • Benchmarks
      • UIB Benchmark Tool
    • Article Evaluator
    • Open Starship Simulation
    • API Gateway
  • EKIT Department
  • About
    • Contributors
  • Contact
  • Join Community
  • Terms of Service
  • Login
  • Register
Menu

The Open Humanoid: Why We Are Building a Robot From First Principles

Posted on March 11, 2026March 12, 2026 by
Engineering Research
Engineering Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18946968  54stabilfr·wdophcgmx
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]Indexed71%○≥80% have metadata indexed
[l]Academic57%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]2,577✓Minimum 2,000 words for a full research article. Current: 2,577
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18946968
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]17%✗≥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 (55 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

In February 2026, Boston Dynamics announced that its electric Atlas humanoid had entered production and begun autonomous operation in commercial facilities. The robot stands approximately 1.5 meters tall, weighs 89 kilograms, features 28 degrees of freedom, and can perform dynamic movements that were science fiction a decade ago. Tesla claims its Optimus robot will achieve commercial deployment...

Show moreHide
Engineering Research by Oleh Ivchenko DOI: 10.5281/zenodo.18946968 54stabilfr·wdophcgmx
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]Indexed71%○≥80% have metadata indexed
[l]Academic57%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]2,577✓Minimum 2,000 words for a full research article. Current: 2,577
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18946968
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]17%✗≥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 (55 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Open HumanoidRead More
Read more

Why Companies Don’t Want You to Know the Real Cost of AI

Posted on March 10, 2026March 12, 2026 by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18944159  40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted39%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,796✓Minimum 2,000 words for a full research article. Current: 2,796
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18944159
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]28%✗≥60% of references from 2025–2026. Current: 28%
[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 (33 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The current landscape of artificial intelligence pricing operates on a fundamental deception: what consumers pay bears almost no relationship to what the technology actually costs. This paper explores the economic mechanics behind platform subsidisation, the strategic motivations for concealing true costs, and the implications for enterprises building AI-powered products. Drawing on platform ec...

Show moreHide
Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18944159 40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted39%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,796✓Minimum 2,000 words for a full research article. Current: 2,796
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18944159
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]28%✗≥60% of references from 2025–2026. Current: 28%
[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 (33 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Cost-Effective Ent…Read More
Read more

The Subsidised Intelligence Illusion: What AI Really Costs When the Platform Isn’t Paying

Posted on March 10, 2026March 11, 2026 by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18943388  32stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted25%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed38%○≥80% have metadata indexed
[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access44%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]1,738✗Minimum 2,000 words for a full research article. Current: 1,738
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18943388
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (29 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Enterprise AI adoption has accelerated dramatically, yet fundamental cost misperceptions persist. This paper demonstrates that consumer subscription plans for frontier AI models (Claude Max at $100/month, ChatGPT Plus at $20/month) represent heavily platform-subsidised pricing that bears no relation to actual inference economics. Through detailed token consumption analysis and API pricing calcu...

Show moreHide
Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18943388 32stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted25%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed38%○≥80% have metadata indexed
[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access44%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]1,738✗Minimum 2,000 words for a full research article. Current: 1,738
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18943388
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (29 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
Cost-Effective Ent…Read More
Read more

Agent Economy Investment Surge: VC Bets on Agentic Infrastructure

Posted on March 10, 2026March 14, 2026 by
AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18943141  29stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted10%○≥80% from verified, high-quality sources
[a]DOI10%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed10%○≥80% have metadata indexed
[l]Academic10%○≥80% from journals/conferences/preprints
[f]Free Access30%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]1,878✗Minimum 2,000 words for a full research article. Current: 1,878
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18943141
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]64%✓≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (14 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

February 2026 produced the largest monthly venture capital figure ever recorded: $189 billion, of which AI startups captured $171 billion — 90% of the total. Three companies (OpenAI, Anthropic, Waymo) accounted for 83% of that sum alone. But beneath the headline megadeals, a quieter structural shift is underway: seed and Series A funding is flowing specifically into agentic infrastructure — the...

Show moreHide
AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18943141 29stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted10%○≥80% from verified, high-quality sources
[a]DOI10%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed10%○≥80% have metadata indexed
[l]Academic10%○≥80% from journals/conferences/preprints
[f]Free Access30%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]1,878✗Minimum 2,000 words for a full research article. Current: 1,878
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18943141
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]64%✓≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (14 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
AI EconomicsRead More
Read more

Agent Auditor — Part 3: Career Landscape & Market Forecast

Posted on March 10, 2026March 10, 2026 by
Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18930666  37stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted36%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,307✓Minimum 2,000 words for a full research article. Current: 2,307
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18930666
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
[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 (28 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Parts 1 and 2 of this series established the structural case for the Agent Auditor as a distinct professional role and mapped the competency model required to fill it. This final instalment examines the market reality: where the demand is forming, what it pays, which sectors are driving adoption, and how the regulatory environment — in particular the EU AI Act — is accelerating the transition f...

Show moreHide
Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18930666 37stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted36%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,307✓Minimum 2,000 words for a full research article. Current: 2,307
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18930666
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
[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 (28 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Future of AIRead More
Read more

From a Destroyed City to a Research Hub: The Story Behind Stabilarity

Posted on March 9, 2026March 10, 2026 by Admin
DOI: 10.5281/zenodo.18930087  40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted40%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access60%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,099✓Minimum 2,000 words for a full research article. Current: 2,099
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18930087
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]23%✗≥60% of references from 2025–2026. Current: 23%
[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 (32 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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...

Show moreHide
DOI: 10.5281/zenodo.18930087 40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted40%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access60%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,099✓Minimum 2,000 words for a full research article. Current: 2,099
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18930087
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]23%✗≥60% of references from 2025–2026. Current: 23%
[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 (32 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Read More
Read more

Tattoo-Based Emergency Patient Identification: From Internal Research to Public Deployment

Posted on March 9, 2026March 9, 2026 by
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18929669  52stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources25%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI58%○≥80% have a Digital Object Identifier
[b]CrossRef25%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic58%○≥80% from journals/conferences/preprints
[f]Free Access75%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,976✗Minimum 2,000 words for a full research article. Current: 1,976
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18929669
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]10%✗≥60% of references from 2025–2026. Current: 10%
[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 (62 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

We describe the public release of a tattoo-based emergency patient identification framework whose conceptual roots trace to OTG-bot — a UNDP-grant-winning civic technology project developed in 2021 for Ukrainian territorial communities. That project received a $10,000 USD grant from the United Nations Development Programme at the Hack Locals 2.0 hackathon and included an automated missing-perso...

Show moreHide
Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18929669 52stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources25%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI58%○≥80% have a Digital Object Identifier
[b]CrossRef25%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic58%○≥80% from journals/conferences/preprints
[f]Free Access75%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,976✗Minimum 2,000 words for a full research article. Current: 1,976
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18929669
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]10%✗≥60% of references from 2025–2026. Current: 10%
[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 (62 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
Medical ML DiagnosisRead More
Read more

Longitudinal Report Generation with LLM-Based Agents: Architecture, Consistency Mechanisms, and Empirical Evidence

Posted on March 9, 2026March 10, 2026 by Admin
DOI: 10.5281/zenodo.18928461  64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI92%✓≥80% have a Digital Object Identifier
[b]CrossRef16%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References25 refs✓Minimum 10 references required
[w]Words [REQ]3,102✓Minimum 2,000 words for a full research article. Current: 3,102
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928461
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]38%✗≥60% of references from 2025–2026. Current: 38%
[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 (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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...

Show moreHide
DOI: 10.5281/zenodo.18928461 64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI92%✓≥80% have a Digital Object Identifier
[b]CrossRef16%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References25 refs✓Minimum 10 references required
[w]Words [REQ]3,102✓Minimum 2,000 words for a full research article. Current: 3,102
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928461
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]38%✗≥60% of references from 2025–2026. Current: 38%
[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 (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Read More
Read more

AI Architecture Comparison Observatory: AADA vs LLM-First Agents

Posted on March 9, 2026March 10, 2026 by
Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18928461  45stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted62%○≥80% from verified, high-quality sources
[a]DOI62%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic62%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]749✗Minimum 2,000 words for a full research article. Current: 749
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928461
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (50 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Interactive comparison of AI-Augmented Agentic Deterministic Architecture (AADA) vs LLM-First Agent paradigms — with real systems, real data, and real citations.

Show moreHide
Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18928461 45stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted62%○≥80% from verified, high-quality sources
[a]DOI62%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic62%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]749✗Minimum 2,000 words for a full research article. Current: 749
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928461
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (50 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
Future of AIRead More
Read more

Beyond the Benchmark: What AI Looks Like When It Actually Works

Posted on March 9, 2026March 9, 2026 by Admin
DOI: 10.5281/zenodo.18926904  61stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI61%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed61%○≥80% have metadata indexed
[l]Academic61%○≥80% from journals/conferences/preprints
[f]Free Access56%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,336✓Minimum 2,000 words for a full research article. Current: 2,336
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18926904
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
[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 (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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 ...

Show moreHide
DOI: 10.5281/zenodo.18926904 61stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI61%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed61%○≥80% have metadata indexed
[l]Academic61%○≥80% from journals/conferences/preprints
[f]Free Access56%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,336✓Minimum 2,000 words for a full research article. Current: 2,336
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18926904
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
[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 (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Read More
Read more

Posts pagination

  • Previous
  • 1
  • …
  • 25
  • 26
  • 27
  • 28
  • 29
  • 30
  • 31
  • …
  • 49
  • Next

Recent Posts

  • The Open Source AI Trust Gap: When Community Projects Do Not Meet Enterprise Standards
  • Запускаємо розділ кафедри ЕКІТ на hub.stabilarity.com
  • Cross-Industry AI Transparency Stacks: Open Source Reference Architectures for XAI
  • Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations
  • The Bus Factor of XAI: Community Risk in Critical Open Source Explainability Tools

Research Index

Browse all articles — filter by score, badges, views, series →

Categories

  • ai
  • AI Economics
  • AI Memory
  • AI Observability & Monitoring
  • AI Portfolio Optimisation
  • Ancient IT History
  • Anticipatory Intelligence
  • Article Quality Science
  • Capability-Adoption Gap
  • Cost-Effective Enterprise AI
  • Future of AI
  • Geopolitical Risk Intelligence
  • hackathon
  • healthcare
  • HPF-P Framework
  • innovation
  • Intellectual Data Analysis
  • medai
  • Medical ML Diagnosis
  • Open Humanoid
  • Research
  • ScanLab
  • Shadow Economy Dynamics
  • Spec-Driven AI Development
  • Technology
  • Trusted Open Source
  • Uncategorized
  • Universal Intelligence Benchmark
  • War Prediction
  • Кафедра ЕКІТ

About

Stabilarity Research Hub is dedicated to advancing the frontiers of AI, from Medical ML to Anticipatory Intelligence. Our mission is to build robust and efficient AI systems for a safer future.

Language

  • Medical ML Diagnosis
  • AI Economics
  • Cost-Effective AI
  • Anticipatory Intelligence
  • Data Mining
  • 🔑 API for Researchers

Connect

Facebook Group: Join

Telegram: @Y0man

Email: contact@stabilarity.com

© 2026 Stabilarity Research Hub

© 2026 Stabilarity Hub | Powered by Superbs Personal Blog theme
Stabilarity Research Hub

Open research platform for AI, machine learning, and enterprise technology. All articles are preprints with DOI registration via Zenodo.

480+
Articles
20+
Series
DOI
Archived

Research Series

  • Medical ML Diagnosis
  • Cost-Effective Enterprise AI
  • Future of AI
  • Trusted Open Source
  • Geopolitical Risk Intelligence
  • Capability–Adoption Gap
  • Spec-Driven AI
  • Shadow Economy Dynamics

Community

  • EKIT Department
  • Join Community
  • MedAI Hack
  • Zenodo Collection
  • GitHub
  • contact@stabilarity.com

Legal

  • Terms of Service
  • About Us
  • Contact
  • CC BY 4.0 License
Operated by
Stabilarity OÜ
Registry: 17150040
Estonian Business Register →
© 2026 Stabilarity OÜ. Content licensed under CC BY 4.0
Terms About Contact
Language: 🇬🇧 EN 🇺🇦 UK 🇩🇪 DE 🇵🇱 PL 🇫🇷 FR
Display Settings
Theme
Light
Dark
Auto
Width
Default
Column
Wide
Text 100%

We use cookies to enhance your experience and analyze site traffic. By clicking "Accept All", you consent to our use of cookies. Read our Terms of Service for more information.