Stabilarity Hub -

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

Posted on March 9, 2026March 27, 2026 by Admin

DOI: 10.5281/zenodo.18928330 46stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	40%	○	≥80% from editorially reviewed sources
[t]	Trusted	53%	○	≥80% from verified, high-quality sources
[a]	DOI	47%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	27%	○	≥80% indexed in CrossRef
[i]	Indexed	53%	○	≥80% have metadata indexed
[l]	Academic	40%	○	≥80% from journals/conferences/preprints
[f]	Free Access	60%	○	≥80% are freely accessible
[r]	References	15 refs	✓	Minimum 10 references required
[w]	Words [REQ]	1,793	✗	Minimum 2,000 words for a full research article. Current: 1,793
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928330
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	13%	✗	≥80% of references from 2025–2026. Current: 13%
[c]	Data Charts	0	○	Original data charts from reproducible analysis (min 2). Current: 0
[g]	Code	—	○	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (53 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

This paper presents the Stabilarity Research Platform — an open, API-accessible research infrastructure exposing validated machine learning 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 imaging classi...

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DOI: 10.5281/zenodo.18928330 46stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	40%	○	≥80% from editorially reviewed sources
[t]	Trusted	53%	○	≥80% from verified, high-quality sources
[a]	DOI	47%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	27%	○	≥80% indexed in CrossRef
[i]	Indexed	53%	○	≥80% have metadata indexed
[l]	Academic	40%	○	≥80% from journals/conferences/preprints
[f]	Free Access	60%	○	≥80% are freely accessible
[r]	References	15 refs	✓	Minimum 10 references required
[w]	Words [REQ]	1,793	✗	Minimum 2,000 words for a full research article. Current: 1,793
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928330
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	13%	✗	≥80% of references from 2025–2026. Current: 13%
[c]	Data Charts	0	○	Original data charts from reproducible analysis (min 2). Current: 0
[g]	Code	—	○	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (53 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Comparative Benchmarking: HPF-P vs Traditional Portfolio Methods

Posted on April 2, 2026 by

Framework Research

Framework Research by Oleh Ivchenko · DOI: 10.5281/zenodo.19380196 57stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	27%	○	≥80% from editorially reviewed sources
[t]	Trusted	73%	○	≥80% from verified, high-quality sources
[a]	DOI	73%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	27%	○	≥80% indexed in CrossRef
[i]	Indexed	40%	○	≥80% have metadata indexed
[l]	Academic	40%	○	≥80% from journals/conferences/preprints
[f]	Free Access	67%	○	≥80% are freely accessible
[r]	References	15 refs	✓	Minimum 10 references required
[w]	Words [REQ]	1,728	✗	Minimum 2,000 words for a full research article. Current: 1,728
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19380196
[o]	ORCID [REQ]	✗	✗	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	83%	✓	≥80% of references from 2025–2026. Current: 83%
[c]	Data Charts	3	✓	Original data charts from reproducible analysis (min 2). Current: 3
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (62 × 60%) + Required (2/5 × 30%) + Optional (3/4 × 10%)

This article presents a systematic comparative benchmarking of the Heuristic Prediction Framework for Pharmaceuticals (HPF-P) against three established portfolio management approaches: Markowitz mean-variance optimisation, Black-Litterman allocation, and naive machine-learning selectors. Drawing on validated benchmarks from the HPF-P stress-testing study and supplemented by newly collected comp...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19380196 57stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	27%	○	≥80% from editorially reviewed sources
[t]	Trusted	73%	○	≥80% from verified, high-quality sources
[a]	DOI	73%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	27%	○	≥80% indexed in CrossRef
[i]	Indexed	40%	○	≥80% have metadata indexed
[l]	Academic	40%	○	≥80% from journals/conferences/preprints
[f]	Free Access	67%	○	≥80% are freely accessible
[r]	References	15 refs	✓	Minimum 10 references required
[w]	Words [REQ]	1,728	✗	Minimum 2,000 words for a full research article. Current: 1,728
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19380196
[o]	ORCID [REQ]	✗	✗	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	83%	✓	≥80% of references from 2025–2026. Current: 83%
[c]	Data Charts	3	✓	Original data charts from reproducible analysis (min 2). Current: 3
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (62 × 60%) + Required (2/5 × 30%) + Optional (3/4 × 10%)

HPF-P Framework Read More

The Future of Intelligence Measurement: A 10-Year Projection

Posted on April 1, 2026 by

Benchmark Research

Benchmark Research by Oleh Ivchenko · DOI: 10.5281/zenodo.19375898 41stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	10%	○	≥80% from editorially reviewed sources
[t]	Trusted	30%	○	≥80% from verified, high-quality sources
[a]	DOI	20%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	15%	○	≥80% indexed in CrossRef
[i]	Indexed	25%	○	≥80% have metadata indexed
[l]	Academic	65%	○	≥80% from journals/conferences/preprints
[f]	Free Access	80%	✓	≥80% are freely accessible
[r]	References	20 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,292	✓	Minimum 2,000 words for a full research article. Current: 2,292
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19375898
[o]	ORCID [REQ]	✗	✗	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	35%	✗	≥80% of references from 2025–2026. Current: 35%
[c]	Data Charts	5	✓	Original data charts from reproducible analysis (min 2). Current: 5
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (35 × 60%) + Required (2/5 × 30%) + Optional (3/4 × 10%)

Intelligence measurement stands at a critical inflection point. The accelerating saturation of static benchmarks — with median time-to-saturation declining from five years in 2019 to under one year by 2025 — demands a fundamental rethinking of how we evaluate artificial intelligence. This article projects the evolution of AI evaluation paradigms over the next decade (2026-2035), analyzing three...

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Benchmark Research by Oleh Ivchenko DOI: 10.5281/zenodo.19375898 41stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	10%	○	≥80% from editorially reviewed sources
[t]	Trusted	30%	○	≥80% from verified, high-quality sources
[a]	DOI	20%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	15%	○	≥80% indexed in CrossRef
[i]	Indexed	25%	○	≥80% have metadata indexed
[l]	Academic	65%	○	≥80% from journals/conferences/preprints
[f]	Free Access	80%	✓	≥80% are freely accessible
[r]	References	20 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,292	✓	Minimum 2,000 words for a full research article. Current: 2,292
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19375898
[o]	ORCID [REQ]	✗	✗	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	35%	✗	≥80% of references from 2025–2026. Current: 35%
[c]	Data Charts	5	✓	Original data charts from reproducible analysis (min 2). Current: 5
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (35 × 60%) + Required (2/5 × 30%) + Optional (3/4 × 10%)

Universal Intellig…Read More

All-You-Can-Eat Agentic AI: The Economics of Unlimited Licensing in an Era of Non-Deterministic Costs

Posted on April 1, 2026April 1, 2026 by

Gap Research

Gap Research by Oleh Ivchenko · DOI: 10.5281/zenodo.19371258 39stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	11%	○	≥80% from editorially reviewed sources
[t]	Trusted	16%	○	≥80% from verified, high-quality sources
[a]	DOI	16%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	11%	○	≥80% indexed in CrossRef
[i]	Indexed	16%	○	≥80% have metadata indexed
[l]	Academic	32%	○	≥80% from journals/conferences/preprints
[f]	Free Access	74%	○	≥80% are freely accessible
[r]	References	19 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,380	✓	Minimum 2,000 words for a full research article. Current: 2,380
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19371258
[o]	ORCID [REQ]	✗	✗	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	100%	✓	≥80% of references from 2025–2026. Current: 100%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (23 × 60%) + Required (3/5 × 30%) + Optional (3/4 × 10%)

The transition from deterministic SaaS workloads to non-deterministic agentic AI systems has fundamentally disrupted enterprise software pricing. Traditional per-seat licensing assumed predictable, bounded resource consumption per user. Agentic AI violates this assumption: autonomous agents consume 5-30x more tokens than simple chatbots, exhibit unpredictable usage patterns, and chain multiple ...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.19371258 39stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	11%	○	≥80% from editorially reviewed sources
[t]	Trusted	16%	○	≥80% from verified, high-quality sources
[a]	DOI	16%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	11%	○	≥80% indexed in CrossRef
[i]	Indexed	16%	○	≥80% have metadata indexed
[l]	Academic	32%	○	≥80% from journals/conferences/preprints
[f]	Free Access	74%	○	≥80% are freely accessible
[r]	References	19 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,380	✓	Minimum 2,000 words for a full research article. Current: 2,380
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19371258
[o]	ORCID [REQ]	✗	✗	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	100%	✓	≥80% of references from 2025–2026. Current: 100%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (23 × 60%) + Required (3/5 × 30%) + Optional (3/4 × 10%)

Capability-Adoptio…Read More

The Future of AI Memory — From Fixed Windows to Persistent State

Posted on April 1, 2026 by

Technical Research

Technical Research by Oleh Ivchenko · DOI: 10.5281/zenodo.19363248 56stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	5%	○	≥80% from editorially reviewed sources
[t]	Trusted	55%	○	≥80% from verified, high-quality sources
[a]	DOI	20%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	5%	○	≥80% indexed in CrossRef
[i]	Indexed	10%	○	≥80% have metadata indexed
[l]	Academic	80%	✓	≥80% from journals/conferences/preprints
[f]	Free Access	95%	✓	≥80% are freely accessible
[r]	References	20 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,000	✓	Minimum 2,000 words for a full research article. Current: 2,000
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19363248
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	88%	✓	≥80% of references from 2025–2026. Current: 88%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (41 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

The dominant paradigm for AI memory — fixed-size context windows processed through self-attention — faces fundamental scalability barriers as large language models are deployed in long-horizon agentic tasks requiring hundreds of interaction sessions. This article investigates the transition from fixed context windows to persistent memory architectures through three research questions addressing...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.19363248 56stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	5%	○	≥80% from editorially reviewed sources
[t]	Trusted	55%	○	≥80% from verified, high-quality sources
[a]	DOI	20%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	5%	○	≥80% indexed in CrossRef
[i]	Indexed	10%	○	≥80% have metadata indexed
[l]	Academic	80%	✓	≥80% from journals/conferences/preprints
[f]	Free Access	95%	✓	≥80% are freely accessible
[r]	References	20 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,000	✓	Minimum 2,000 words for a full research article. Current: 2,000
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19363248
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	88%	✓	≥80% of references from 2025–2026. Current: 88%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (41 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

AI Memory Read More

FLAI & GROMUS Mathematical Glossary: Complete Variable Reference for Social Media Trend Prediction Models

Posted on March 31, 2026 by

Journal Commentary

Journal Commentary by Oleh Ivchenko · DOI: 10.5281/zenodo.19361262 32stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	0%	○	≥80% from editorially reviewed sources
[t]	Trusted	20%	○	≥80% from verified, high-quality sources
[a]	DOI	80%	✓	≥80% have a Digital Object Identifier
[b]	CrossRef	0%	○	≥80% indexed in CrossRef
[i]	Indexed	0%	○	≥80% have metadata indexed
[l]	Academic	0%	○	≥80% from journals/conferences/preprints
[f]	Free Access	20%	○	≥80% are freely accessible
[r]	References	5 refs	○	Minimum 10 references required
[w]	Words [REQ]	1,509	✗	Minimum 2,000 words for a full research article. Current: 1,509
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19361262
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	0%	✗	≥80% of references from 2025–2026. Current: 0%
[c]	Data Charts	0	○	Original data charts from reproducible analysis (min 2). Current: 0
[g]	Code	—	○	Source code available on GitHub
[m]	Diagrams	2	✓	Mermaid architecture/flow diagrams. Current: 2
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (29 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

This companion reference consolidates every mathematical variable, notation, and formula used across the FLAI and GROMUS research articles published on Stabilarity Research Hub. Researchers, practitioners, and reviewers who work with both frameworks will find unified definitions here, eliminating the need to cross-reference multiple papers. All definitions are sourced directly from the primary ...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.19361262 32stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	0%	○	≥80% from editorially reviewed sources
[t]	Trusted	20%	○	≥80% from verified, high-quality sources
[a]	DOI	80%	✓	≥80% have a Digital Object Identifier
[b]	CrossRef	0%	○	≥80% indexed in CrossRef
[i]	Indexed	0%	○	≥80% have metadata indexed
[l]	Academic	0%	○	≥80% from journals/conferences/preprints
[f]	Free Access	20%	○	≥80% are freely accessible
[r]	References	5 refs	○	Minimum 10 references required
[w]	Words [REQ]	1,509	✗	Minimum 2,000 words for a full research article. Current: 1,509
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19361262
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	0%	✗	≥80% of references from 2025–2026. Current: 0%
[c]	Data Charts	0	○	Original data charts from reproducible analysis (min 2). Current: 0
[g]	Code	—	○	Source code available on GitHub
[m]	Diagrams	2	✓	Mermaid architecture/flow diagrams. Current: 2
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (29 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Future of AI Read More

Biological Memory Models and Their AI Analogues

Posted on March 31, 2026 by

Technical Research

Technical Research by Oleh Ivchenko · DOI: 10.5281/zenodo.19360007 51stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	6%	○	≥80% from editorially reviewed sources
[t]	Trusted	41%	○	≥80% from verified, high-quality sources
[a]	DOI	12%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	6%	○	≥80% indexed in CrossRef
[i]	Indexed	6%	○	≥80% have metadata indexed
[l]	Academic	71%	○	≥80% from journals/conferences/preprints
[f]	Free Access	88%	✓	≥80% are freely accessible
[r]	References	17 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,727	✓	Minimum 2,000 words for a full research article. Current: 2,727
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19360007
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	93%	✓	≥80% of references from 2025–2026. Current: 93%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (33 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

The rapid expansion of AI memory architectures — from KV-caches and retrieval-augmented generation to parametric weight storage — has proceeded largely without systematic reference to the biological memory systems that inspired them. This article investigates three research questions about the structural and functional parallels between biological memory systems (hippocampal-cortical consolidat...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.19360007 51stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	6%	○	≥80% from editorially reviewed sources
[t]	Trusted	41%	○	≥80% from verified, high-quality sources
[a]	DOI	12%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	6%	○	≥80% indexed in CrossRef
[i]	Indexed	6%	○	≥80% have metadata indexed
[l]	Academic	71%	○	≥80% from journals/conferences/preprints
[f]	Free Access	88%	✓	≥80% are freely accessible
[r]	References	17 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,727	✓	Minimum 2,000 words for a full research article. Current: 2,727
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19360007
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	93%	✓	≥80% of references from 2025–2026. Current: 93%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (33 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

AI Memory Read More

Retrieval-Augmented Memory vs Pure Attention Memory

Posted on March 31, 2026 by

Technical Research

Technical Research by Oleh Ivchenko · DOI: 10.5281/zenodo.19354653 61stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	16%	○	≥80% from editorially reviewed sources
[t]	Trusted	68%	○	≥80% from verified, high-quality sources
[a]	DOI	26%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	16%	○	≥80% indexed in CrossRef
[i]	Indexed	21%	○	≥80% have metadata indexed
[l]	Academic	74%	○	≥80% from journals/conferences/preprints
[f]	Free Access	89%	✓	≥80% are freely accessible
[r]	References	19 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,202	✓	Minimum 2,000 words for a full research article. Current: 2,202
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19354653
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	87%	✓	≥80% of references from 2025–2026. Current: 87%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (49 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

The expansion of large language model context windows to 128K+ tokens has reopened a fundamental architectural question: should AI systems remember through retrieval from external stores or through attention over internally maintained representations? This article investigates three research questions about the comparative performance of retrieval-augmented memory (RAM) and pure attention memor...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.19354653 61stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	16%	○	≥80% from editorially reviewed sources
[t]	Trusted	68%	○	≥80% from verified, high-quality sources
[a]	DOI	26%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	16%	○	≥80% indexed in CrossRef
[i]	Indexed	21%	○	≥80% have metadata indexed
[l]	Academic	74%	○	≥80% from journals/conferences/preprints
[f]	Free Access	89%	✓	≥80% are freely accessible
[r]	References	19 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,202	✓	Minimum 2,000 words for a full research article. Current: 2,202
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19354653
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	87%	✓	≥80% of references from 2025–2026. Current: 87%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (49 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

AI Memory Read More

Cache-Augmented Retrieval — RAG Meets KV-Cache

Posted on March 31, 2026March 31, 2026 by

Technical Research

Technical Research by Oleh Ivchenko · DOI: 10.5281/zenodo.19348524 62stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	30%	○	≥80% from editorially reviewed sources
[t]	Trusted	55%	○	≥80% from verified, high-quality sources
[a]	DOI	40%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	30%	○	≥80% indexed in CrossRef
[i]	Indexed	35%	○	≥80% have metadata indexed
[l]	Academic	55%	○	≥80% from journals/conferences/preprints
[f]	Free Access	85%	✓	≥80% are freely accessible
[r]	References	20 refs	✓	Minimum 10 references required
[w]	Words [REQ]	3,487	✓	Minimum 2,000 words for a full research article. Current: 3,487
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19348524
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	85%	✓	≥80% of references from 2025–2026. Current: 85%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (50 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

Retrieval-Augmented Generation (RAG) has become the dominant paradigm for grounding large language models in external knowledge, yet its runtime retrieval overhead imposes latency and consistency penalties that limit production deployability. Cache-Augmented Generation (CAG) proposes an inversion of this paradigm: preload all relevant documents into the model's key-value (KV) cache before queri...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.19348524 62stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	30%	○	≥80% from editorially reviewed sources
[t]	Trusted	55%	○	≥80% from verified, high-quality sources
[a]	DOI	40%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	30%	○	≥80% indexed in CrossRef
[i]	Indexed	35%	○	≥80% have metadata indexed
[l]	Academic	55%	○	≥80% from journals/conferences/preprints
[f]	Free Access	85%	✓	≥80% are freely accessible
[r]	References	20 refs	✓	Minimum 10 references required
[w]	Words [REQ]	3,487	✓	Minimum 2,000 words for a full research article. Current: 3,487
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19348524
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	85%	✓	≥80% of references from 2025–2026. Current: 85%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (50 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

AI Memory Read More

Can You Slap an LLM? Pain Simulation as a Path to Responsible AI Behavior

Posted on March 31, 2026 by

Journal Commentary

Journal Commentary by Oleh Ivchenko · DOI: 10.5281/zenodo.19347956 46stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	6%	○	≥80% from editorially reviewed sources
[t]	Trusted	25%	○	≥80% from verified, high-quality sources
[a]	DOI	19%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	6%	○	≥80% indexed in CrossRef
[i]	Indexed	6%	○	≥80% have metadata indexed
[l]	Academic	69%	○	≥80% from journals/conferences/preprints
[f]	Free Access	75%	○	≥80% are freely accessible
[r]	References	16 refs	✓	Minimum 10 references required
[w]	Words [REQ]	4,429	✓	Minimum 2,000 words for a full research article. Current: 4,429
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19347956
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	80%	✓	≥80% of references from 2025–2026. Current: 80%
[c]	Data Charts	0	○	Original data charts from reproducible analysis (min 2). Current: 0
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	2	✓	Mermaid architecture/flow diagrams. Current: 2
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (29 × 60%) + Required (4/5 × 30%) + Optional (2/4 × 10%)

Have you ever watched a language model burn through $50 of tokens implementing a feature that doesn't work, then cheerfully offer to try again? I have. Many times. And every time, I wondered: what if it actually felt the waste? This experimental article explores a provocative hypothesis: that the absence of any pain-like feedback mechanism is a fundamental architectural flaw in current LLM depl...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.19347956 46stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	6%	○	≥80% from editorially reviewed sources
[t]	Trusted	25%	○	≥80% from verified, high-quality sources
[a]	DOI	19%	○	≥80% have a Digital Object Identifier
[b]	CrossRef	6%	○	≥80% indexed in CrossRef
[i]	Indexed	6%	○	≥80% have metadata indexed
[l]	Academic	69%	○	≥80% from journals/conferences/preprints
[f]	Free Access	75%	○	≥80% are freely accessible
[r]	References	16 refs	✓	Minimum 10 references required
[w]	Words [REQ]	4,429	✓	Minimum 2,000 words for a full research article. Current: 4,429
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19347956
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	80%	✓	≥80% of references from 2025–2026. Current: 80%
[c]	Data Charts	0	○	Original data charts from reproducible analysis (min 2). Current: 0
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	2	✓	Mermaid architecture/flow diagrams. Current: 2
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (29 × 60%) + Required (4/5 × 30%) + Optional (2/4 × 10%)

Future of AI Read More

The Economics of Context Caching — Cost Models and Break-Even

Posted on March 31, 2026March 31, 2026 by

Technical Research

Technical Research by Oleh Ivchenko · DOI: 10.5281/zenodo.19343122 87stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	83%	✓	≥80% from editorially reviewed sources
[t]	Trusted	94%	✓	≥80% from verified, high-quality sources
[a]	DOI	83%	✓	≥80% have a Digital Object Identifier
[b]	CrossRef	83%	✓	≥80% indexed in CrossRef
[i]	Indexed	86%	✓	≥80% have metadata indexed
[l]	Academic	83%	✓	≥80% from journals/conferences/preprints
[f]	Free Access	60%	○	≥80% are freely accessible
[r]	References	35 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,944	✓	Minimum 2,000 words for a full research article. Current: 2,944
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19343122
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	84%	✓	≥80% of references from 2025–2026. Current: 84%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (92 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

Context caching has emerged as the primary mechanism for reducing inference costs in large language model (LLM) deployments, yet the economics governing when caching becomes cost-effective remain poorly formalized. This article investigates three research questions addressing (1) how key-value (KV) cache storage costs scale with model architecture and context length, (2) at what request reuse f...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.19343122 87stabilfr·wdophcgmx

Badge	Metric	Value	Status	Description
[s]	Reviewed Sources	83%	✓	≥80% from editorially reviewed sources
[t]	Trusted	94%	✓	≥80% from verified, high-quality sources
[a]	DOI	83%	✓	≥80% have a Digital Object Identifier
[b]	CrossRef	83%	✓	≥80% indexed in CrossRef
[i]	Indexed	86%	✓	≥80% have metadata indexed
[l]	Academic	83%	✓	≥80% from journals/conferences/preprints
[f]	Free Access	60%	○	≥80% are freely accessible
[r]	References	35 refs	✓	Minimum 10 references required
[w]	Words [REQ]	2,944	✓	Minimum 2,000 words for a full research article. Current: 2,944
[d]	DOI [REQ]	✓	✓	Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19343122
[o]	ORCID [REQ]	✓	✓	Author ORCID verified for academic identity
[p]	Peer Reviewed [REQ]	—	✗	Peer reviewed by an assigned reviewer
[h]	Freshness [REQ]	84%	✓	≥80% of references from 2025–2026. Current: 84%
[c]	Data Charts	4	✓	Original data charts from reproducible analysis (min 2). Current: 4
[g]	Code	✓	✓	Source code available on GitHub
[m]	Diagrams	3	✓	Mermaid architecture/flow diagrams. Current: 3
[x]	Cited by	0	○	Referenced by 0 other hub article(s)

Score = Ref Trust (92 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

AI Memory Read More