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Category: Future of AI

Visionary research and essays on the trajectory of artificial intelligence, its cognitive implications, and the human-AI future

AI Agents Operate With Minimal Safety Disclosures: MIT Study Reveals Transparency Gap

Posted on February 23, 2026February 23, 2026 by
Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18741627  34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,130✓Minimum 2,000 words for a full research article. Current: 3,130
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741627
[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 (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

MIT CSAIL's 2025 AI Agent Index analyzed 30 prominent AI agents and found a striking transparency deficit: while 70% provide documentation and nearly half publish code, only 19% disclose formal safety policies and fewer than 10% report external safety evaluations. This journal entry examines the study's findings, contextualizes the claims within the broader AI safety discourse, and assesses whe...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18741627 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,130✓Minimum 2,000 words for a full research article. Current: 3,130
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741627
[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 (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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When AI Finally Beats the Experts: DeepRare and the End of the Diagnostic Odyssey

Posted on February 22, 2026February 22, 2026 by Admin
Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730582  49stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources36%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI36%○≥80% have a Digital Object Identifier
[b]CrossRef29%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic57%○≥80% from journals/conferences/preprints
[f]Free Access21%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,263✓Minimum 2,000 words for a full research article. Current: 2,263
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730582
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]27%✗≥60% of references from 2025–2026. Current: 27%
[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 (47 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

A new AI system published in Nature has achieved what many thought impossible: diagnosing rare diseases more accurately than experienced physicians. DeepRare, developed by researchers led by Zhao et al., demonstrates 64.4% top-1 diagnostic accuracy compared to 54.6% for human experts with over a decade of clinical experience. Tested across 6,401 cases spanning 2,919 diseases, the system provide...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18730582 49stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources36%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI36%○≥80% have a Digital Object Identifier
[b]CrossRef29%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic57%○≥80% from journals/conferences/preprints
[f]Free Access21%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,263✓Minimum 2,000 words for a full research article. Current: 2,263
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730582
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]27%✗≥60% of references from 2025–2026. Current: 27%
[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 (47 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Cognitive Shift: A Creative Vision of How AI Will Change the Way We Think and Perceive

Posted on February 18, 2026February 24, 2026 by Admin
Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18685239  57stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI43%○≥80% have a Digital Object Identifier
[b]CrossRef38%○≥80% indexed in CrossRef
[i]Indexed48%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access48%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]6,109✓Minimum 2,000 words for a full research article. Current: 6,109
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18685239
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (61 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Artificial intelligence is not primarily a threat to human labour — it is a repricing of human cognition. Drawing on Jürgen Schmidhuber's formal theory of intelligence as compression, Robert Sheckley's satirical science fiction, and Isaac Asimov's prescient design specifications for autonomous systems, this essay argues that AI is catalysing the most significant cognitive economy shift since th...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18685239 57stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI43%○≥80% have a Digital Object Identifier
[b]CrossRef38%○≥80% indexed in CrossRef
[i]Indexed48%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access48%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]6,109✓Minimum 2,000 words for a full research article. Current: 6,109
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18685239
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (61 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Transforming Science: Math, Biology, and Discovery 2025

Posted on February 2, 2026February 25, 2026 by Admin
Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18748877  35stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources13%○≥80% from editorially reviewed sources
[t]Trusted25%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access38%○≥80% are freely accessible
[r]References8 refs○Minimum 10 references required
[w]Words [REQ]2,997✓Minimum 2,000 words for a full research article. Current: 2,997
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18748877
[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 (24 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

2025 marked a watershed year for AI-driven scientific discovery, with systems transitioning from computational tools to active research partners. Google DeepMind's AlphaEvolve discovered novel algorithms for fundamental mathematical and computational problems, improving efficiency across Google's infrastructure by 0.7% globally and finding new solutions to open problems that have challenged mat...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18748877 35stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources13%○≥80% from editorially reviewed sources
[t]Trusted25%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access38%○≥80% are freely accessible
[r]References8 refs○Minimum 10 references required
[w]Words [REQ]2,997✓Minimum 2,000 words for a full research article. Current: 2,997
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18748877
[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 (24 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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