The Training Gap: When AI Capability Outpaces Workforce Readiness

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Gap Research
Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19420224  55stabilfr·wuedophcgmx
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The gap between what AI systems can do and what organizations can operationally deploy continues to widen — driven not only by technical integration challenges but increasingly by workforce unreadiness. This article examines the training gap as a structural component of the capability-adoption gap, analyzing why AI upskilling initiatives consistently fail to produce durable competency gains. Dr...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.19420224 55stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted47%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed21%○≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access79%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]2,092✓Minimum 1,000 words of article body. Current: 2,092
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19420224
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
[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 (4/4 × 30%) + Optional (5/7 × 10%)
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HPF-P in Practice: Deployment Lessons and Future Directions

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Framework Research
Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19417989  86stabilfr·wuedophcgmx
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[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef57%○≥80% indexed in CrossRef
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (81 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

The Heuristic Prediction Framework for Pharma (HPF-P) has been developed across fourteen articles in this series, from its theoretical foundations through DRI calibration, DRL operationalization, multi-scenario stress testing, and regulatory compliance integration. This final article synthesizes deployment experience from pharmaceutical portfolio contexts and identifies the principal lessons le...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19417989 86stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources57%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef57%○≥80% indexed in CrossRef
[i]Indexed81%✓≥80% have metadata indexed
[l]Academic71%○≥80% from journals/conferences/preprints
[f]Free Access76%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,112✓Minimum 1,000 words of article body. Current: 2,112
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19417989
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness72%✓≥60% of references from 2025–2026. Current: 72%
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (81 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)
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Regulatory Compliance Integration: Aligning DRL with Pharmaceutical Frameworks

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Framework Research
Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19414906  82stabilfr·wuedophcgmx
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[t]Trusted65%○≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef60%○≥80% indexed in CrossRef
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[f]Free Access100%✓≥80% are freely accessible
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (75 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Pharmaceutical organizations face increasing pressure to align their internal decision-making processes with externally imposed regulatory frameworks — ICH quality guidelines, FDA 21 CFR Part 11, EMA guidance on AI, and the revised ICH GCP E6(R3). The HPF-P Framework's Decision Readiness Level (DRL) provides a structured five-stage readiness ladder, yet its integration with formal compliance re...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19414906 82stabilfr·wuedophcgmx
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[s]Reviewed Sources35%○≥80% from editorially reviewed sources
[t]Trusted65%○≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
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[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,378✓Minimum 1,000 words of article body. Current: 2,378
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19414906
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness76%✓≥60% of references from 2025–2026. Current: 76%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (75 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)
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Real-Time DRI Monitoring: Continuous Decision Readiness Assessment

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Framework Research
Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19412430  83stabilfr·wuedophcgmx
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[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
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[l]Academic68%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]2,070✓Minimum 1,000 words of article body. Current: 2,070
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[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness75%✓≥60% of references from 2025–2026. Current: 75%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (76 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Decision Readiness Index (DRI) is the core metric of the HPF-P framework — a scalar signal summarising the information completeness required before a pharmaceutical portfolio decision can be trusted. Yet a single DRI snapshot provides only a point-in-time view. This article investigates how continuous, real-time monitoring of DRI signals transforms static readiness scores into dynamic control l...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.19412430 83stabilfr·wuedophcgmx
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[s]Reviewed Sources16%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
[b]CrossRef16%○≥80% indexed in CrossRef
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[l]Academic68%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]2,070✓Minimum 1,000 words of article body. Current: 2,070
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness75%✓≥60% of references from 2025–2026. Current: 75%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
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[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (76 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)
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Comparative Benchmarking: HPF-P vs Traditional Portfolio Methods

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Framework Research
Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19380196  81stabilfr·wuedophcgmx
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[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
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[l]Academic72%○≥80% from journals/conferences/preprints
[f]Free Access94%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]1,819✓Minimum 1,000 words of article body. Current: 1,819
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19380196
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness67%✓≥60% of references from 2025–2026. Current: 67%
[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 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 81stabilfr·wuedophcgmx
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[s]Reviewed Sources22%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef22%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic72%○≥80% from journals/conferences/preprints
[f]Free Access94%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]1,819✓Minimum 1,000 words of article body. Current: 1,819
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]9✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 9
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19380196
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness67%✓≥60% of references from 2025–2026. Current: 67%
[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
[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 (73 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)
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The Future of Intelligence Measurement: A 10-Year Projection

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Benchmark Research
Benchmark Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19375898  83stabilfr·wuedophcgmx
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[s]Reviewed Sources9%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI64%○≥80% have a Digital Object Identifier
[b]CrossRef14%○≥80% indexed in CrossRef
[i]Indexed77%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access95%✓≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]2,472✓Minimum 1,000 words of article body. Current: 2,472
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]16✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 16
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19375898
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness65%✓≥60% of references from 2025–2026. Current: 65%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[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 (76 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 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 83stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources9%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI64%○≥80% have a Digital Object Identifier
[b]CrossRef14%○≥80% indexed in CrossRef
[i]Indexed77%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access95%✓≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]2,472✓Minimum 1,000 words of article body. Current: 2,472
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]16✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 16
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19375898
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness65%✓≥60% of references from 2025–2026. Current: 65%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[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 (76 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)
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All-You-Can-Eat Agentic AI: The Economics of Unlimited Licensing in an Era of Non-Deterministic Costs

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Gap Research
Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19371258  65stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources10%○≥80% from editorially reviewed sources
[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
[b]CrossRef10%○≥80% indexed in CrossRef
[i]Indexed48%○≥80% have metadata indexed
[l]Academic38%○≥80% from journals/conferences/preprints
[f]Free Access81%✓≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,416✓Minimum 1,000 words of article body. Current: 2,416
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]15✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 15
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19371258
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness84%✓≥60% of references from 2025–2026. Current: 84%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (46 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 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 65stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources10%○≥80% from editorially reviewed sources
[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
[b]CrossRef10%○≥80% indexed in CrossRef
[i]Indexed48%○≥80% have metadata indexed
[l]Academic38%○≥80% from journals/conferences/preprints
[f]Free Access81%✓≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,416✓Minimum 1,000 words of article body. Current: 2,416
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]15✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 15
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19371258
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness84%✓≥60% of references from 2025–2026. Current: 84%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (46 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)
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The Future of AI Memory — From Fixed Windows to Persistent State

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19363248  70stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted91%✓≥80% from verified, high-quality sources
[a]DOI23%○≥80% have a Digital Object Identifier
[b]CrossRef5%○≥80% indexed in CrossRef
[i]Indexed23%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]2,062✓Minimum 1,000 words of article body. Current: 2,062
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19363248
[o]ORCID✓✓Author ORCID verified for academic identity
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[h]Freshness75%✓≥60% of references from 2025–2026. Current: 75%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (4/4 × 30%) + Optional (5/7 × 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 70stabilfr·wuedophcgmx
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[s]Reviewed Sources5%○≥80% from editorially reviewed sources
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[a]DOI23%○≥80% have a Digital Object Identifier
[b]CrossRef5%○≥80% indexed in CrossRef
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[f]Free Access100%✓≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]2,062✓Minimum 1,000 words of article body. Current: 2,062
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]16✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 16
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19363248
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness75%✓≥60% of references from 2025–2026. Current: 75%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (4/4 × 30%) + Optional (5/7 × 10%)
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FLAI & GROMUS Mathematical Glossary: Complete Variable Reference for Social Media Trend Prediction Models

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19361262  25stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed71%○≥80% have metadata indexed
[l]Academic71%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,631✓Minimum 1,000 words of article body. Current: 1,631
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19361262
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (3/4 × 30%) + Optional (2/7 × 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 25stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed71%○≥80% have metadata indexed
[l]Academic71%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,631✓Minimum 1,000 words of article body. Current: 1,631
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]0✗Cites at least 1 external source(s): links, DOIs or a reference list. Current: 0
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19361262
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness0%○≥60% of references from 2025–2026. Current: 0%
[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 (68 × 60%) + Required (3/4 × 30%) + Optional (2/7 × 10%)
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Biological Memory Models and Their AI Analogues

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19360007  68stabilfr·wuedophcgmx
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[s]Reviewed Sources5%○≥80% from editorially reviewed sources
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[a]DOI15%○≥80% have a Digital Object Identifier
[b]CrossRef5%○≥80% indexed in CrossRef
[i]Indexed30%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,867✓Minimum 1,000 words of article body. Current: 2,867
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]13✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 13
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19360007
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (52 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 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 68stabilfr·wuedophcgmx
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[s]Reviewed Sources5%○≥80% from editorially reviewed sources
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[a]DOI15%○≥80% have a Digital Object Identifier
[b]CrossRef5%○≥80% indexed in CrossRef
[i]Indexed30%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,867✓Minimum 1,000 words of article body. Current: 2,867
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]13✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 13
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19360007
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness76%✓≥60% of references from 2025–2026. Current: 76%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (52 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)
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