AI Alignment Progress Report 2025: RLHF Successors and Constitutional AI Evaluation

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22149662  31stabilfr·wdophcgmx
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Academic Citation: Ivchenko, Oleh (2026). AI Alignment Progress Report 2025: RLHF Successors and Constitutional AI Evaluation. Research article: AI Alignment Progress Report 2025: RLHF Successors and Constitutional AI Evaluation. Odessa National Polytechnic University, Department of Economic Cybernetics. DOI: 10.5281/zenodo.22149662  ·  View on Zenodo (CERN)

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.22149662 31stabilfr·wdophcgmx
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Spec-Driven Development with AI Agents: A Brownfield Walkthrough

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Academic Research
Academic Research by Oleh Ivchenko  21stabilfr·wdophcgmx
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A full field guide from a live CoffeeJUG session: spec-driven development with OpenSpec on a brownfield codebase — history, method, tool internals, a full annotated walkthrough, and the practices that only show up after a few real changes.

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Academic Research by Oleh Ivchenko 21stabilfr·wdophcgmx
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Labor Market Informality Dynamics: AI Forecasting of Shadow Employment Under Economic Shocks

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.22135534  75stabilfr·wdophcgmx
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[w]Words [REQ]4,417✓Minimum 2,000 words for a full research article. Current: 4,417
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]73%✓≥60% of references from 2025–2026. Current: 73%
[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 (80 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

This article investigates the application of machine l[REDACTED]g (ML) forecasting models to estimate informal employment levels under economic stress scenarios. We calibrate our models using COVID-19 pandemic data and apply them to predict informality responses to AI-driven structural unemployment. Our research addresses three key questions: (RQ1) How accurately can ML models forecast informal...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.22135534 75stabilfr·wdophcgmx
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[a]DOI76%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]73%✓≥60% of references from 2025–2026. Current: 73%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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AI Regression Testing: Detecting Behavioral Drift Across Model Updates in Production

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22132007  33stabilfr·wdophcgmx
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[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 (31 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

System design for continuous behavioral regression testing of AI systems, covering snapshot testing, semantic similarity detection, and alerting for specification-violating behavioral changes. This article explores the design and implementation of a system for continuous behavioral regression testing of AI systems. We cover snapshot testing, semantic similarity detection, and alerting for speci...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.22132007 33stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Self-Healing Observation Orchestrators Using Reinforcement Learning for Resource Reallocation

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22123035  31stabilfr·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]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

Training reinforcement agents to dynamically reconfigure monitoring resources (e.g., sampling rates, logging granularity) in response to observed latency spikes and anomaly patterns.

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.22123035 31stabilfr·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]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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Open-Source AI in Government: Procurement Barriers and Adoption Patterns in Public Sector

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22118897  33stabilfr·wdophcgmx
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[l]Academic50%○≥80% from journals/conferences/preprints
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[r]References4 refs○Minimum 10 references required
[w]Words [REQ]696✗Minimum 2,000 words for a full research article. Current: 696
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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]33%✗≥60% of references from 2025–2026. Current: 33%
[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 (31 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Survey of open-source AI adoption in government agencies across EU and US, identifying procurement barriers, security review requirements, and emerging patterns for successful public-sector OSS AI adoption [1]. This article examines the current state of open-source AI adoption in government agencies, focusing on procurement barriers, security review requirements, and emerging patterns for succe...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.22118897 33stabilfr·wdophcgmx
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[w]Words [REQ]696✗Minimum 2,000 words for a full research article. Current: 696
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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]33%✗≥60% of references from 2025–2026. Current: 33%
[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 (31 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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AI Model Sharing Economy: Designing Royalty Structures for Distributed Model Usage

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22076941  69stabilfr·wdophcgmx
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[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
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[i]Indexed79%○≥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]964✗Minimum 2,000 words for a full research article. Current: 964
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22076941
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]72%✓≥60% of references from 2025–2026. Current: 72%
[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 (81 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The rapid proliferation of machine l[REDACTED]g models as services has created a nascent market for model sharing, yet sustainable royalty mechanisms remain under‑explored. This article investigates how distributed model usage can be quantified, attributed, and monetized through dynamic royalty structures. We pose three research questions: (RQ1) How can usage metrics be reliably captured across...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.22076941 69stabilfr·wdophcgmx
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[s]Reviewed Sources58%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed79%○≥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]964✗Minimum 2,000 words for a full research article. Current: 964
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22076941
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]72%✓≥60% of references from 2025–2026. Current: 72%
[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 (81 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Edge AI Cost-Benefit Tradeoff: Optimizing Deployment Locations for Energy-Constrained Services

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22071604  70stabilfr·wdophcgmx
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[s]Reviewed Sources64%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI79%○≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]798✗Minimum 2,000 words for a full research article. Current: 798
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22071604
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]77%✓≥60% of references from 2025–2026. Current: 77%
[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 (83 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Edge AI deployments face competing objectives of latency, energy consumption, and operational expense. This article investigates placement strategies for AI inference at the edge, balancing these dimensions through a multi-objective optimization framework. We evaluate a range of deployment options across heterogeneous edge environments and present empirical results that quantify tradeoffs betwe...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.22071604 70stabilfr·wdophcgmx
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[s]Reviewed Sources64%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI79%○≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]798✗Minimum 2,000 words for a full research article. Current: 798
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22071604
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]77%✓≥60% of references from 2025–2026. Current: 77%
[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 (83 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Concentration Index: Quantifying Market Power in Foundation Model Providers

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22065776  38stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
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Foundation model providers are rapidly consolidating control over the most valuable AI assets — massive parameter counts, proprietary training pipelines, and exclusive access to high‑quality multimodal datasets. This convergence raises critical antitrust and governance questions: how concentrated is market power, what metrics can reliably capture that concentration, and how do these metrics evo...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.22065776 38stabilfr·wdophcgmx
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Cross-Domain Capability Transfer: Measuring Latent Skill Portability Between AI Systems

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Gap Research
Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22057709  54stabilfr·wdophcgmx
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Measuring latent skill portability across AI domains remains a critical challenge for series research. This article quantifies how capabilities learned in one domain can be repurposed in unrelated sectors, identifying hidden adoption bottlenecks. We introduce a metric based on latent embedding similarity and evaluate it across five benchmark transfer tasks. Our analysis reveals that transferabi...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.22057709 54stabilfr·wdophcgmx
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