20 Articles · Ongoing Daily Journal · 2025–present · ONGOING
Abstract
The rapid advancement of AI capabilities outpaces the institutional capacity to understand their implications — for cognitive science, economics, and society. This series offers academically rigorous but accessible commentary on the evolving AI landscape: critical reviews of influential papers and preprints with explicit verdicts; daily journal entries analyzing significant AI news; and longer-form visionary essays exploring AI’s trajectory through the lens of decision-making, human cognition, and economic impact. Every claim is sourced and every review verdict is justified. The goal is not prediction, but clarity.
Idea and Motivation
AI capabilities are advancing faster than scientific consensus can form around their implications. News cycles conflate speculation with fact. Academic silos keep insights in economics, cognitive science, and ML architecture isolated from each other. Researchers working at the intersection of these domains see patterns that specialists in any single field miss — but those insights rarely surface in any systematic form.
This series began with a commitment to change that. The premise is simple: if you work in AI research, economics, or cognitive science, you deserve access to unfiltered analysis of what the latest papers actually claim, what they actually show, and what they mean for your field. Not hype. Not pessimism. Clarity.
Goal
Provide a structured, ongoing commentary on AI research and development — one that bridges disciplinary silos and maintains rigorous standards for evidence even when addressing speculative questions. The series aims to be a resource for researchers, practitioners, and informed observers who want to understand the AI landscape without the noise.
Scope
The series covers three types of content: Paper Reviews — critical assessments of significant arXiv preprints and published papers from cognitive science, ML, and economic perspectives; Daily Journal Entries — rapid commentary on important AI news with explicit verdict assessment; and Visionary Essays — longer-form analysis of AI’s trajectory, particularly on decision-making, reasoning, economic consequences, and cognitive implications.
Geographic scope is global. Institutional affiliation is ONPU. Content is discipline-agnostic but technically rigorous.
Focus
Technical focus spans ML architecture (large language models, reasoning systems, multimodal AI, agentic systems), AI economics (capability-to-deployment gaps, scaling laws, cost efficiency), and cognitive/societal implications (decision-making under uncertainty, human-AI collaboration, institutional readiness).
Every published item carries one of three explicit verdicts. This verdict scale ensures clarity about evidence quality and claim justification:
Table 1. Verdict scale for paper reviews and news analysis
Verdict Definition Example
Solid Well-supported claims backed by sufficient evidence; clear methodology; appropriate scope limitations stated Paper demonstrates CNN outperforms ViT on specific dataset with ablation studies and confidence intervals
Overstated Kernel of truth, but presentation inflates significance; selective evidence; scope limitations understated; implications overreached Paper shows improvement on benchmark, headlines claim “AI achieves human-level reasoning”
Misleading Cherry-picked evidence, out-of-context claims, factual errors, or conclusions unsupported by data Study on narrow task interpreted as proving AGI is imminent; methodology not disclosed
Verdict assignment is transparent: every review explains the reasoning explicitly so readers can disagree and form their own assessment.
Limitations
Scope of review Focus on published/visible research. Private company systems and proprietary work outside public journals are not assessed directly.
Author perspective Verdicts reflect Oleh’s disciplinary training (ML, economics, cognitive science). Other experts may assign different verdicts to the same work.
Lag time Daily entries published with author’s schedule. Real-time feeds are not maintained; coverage is opportunistic not exhaustive.
No empirical lab work This is pure analysis. No experiments conducted; no new datasets produced. Commentary only.
Scientific Value
The series contributes structured critical discourse on a rapidly moving research frontier. Value comes from three sources: first, explicit verdict scales applied consistently create a reference standard for claim assessment; second, cross-disciplinary framing (ML + economics + cognitive science) reveals patterns that single-field analysis misses; third, sustained commentary on the same research landscape over time creates a documented record of how consensus forms (or fails to) as evidence accumulates.
Secondary value is pedagogical: researchers early in their careers can study the reasoning behind verdicts to develop their own critical reading practices.
Resources
Status
Ongoing. New articles published regularly — typically 2–3 times per week. No fixed endpoint. This is a living commentary series that will continue as long as new AI research warrants assessment.
Contribution Opportunities
Researchers interested in contributing to or extending this work are encouraged to explore these directions:
Disciplinary collaboration: If you work in cognitive science, economics, philosophy of mind, or policy studies and see implications of AI research relevant to your field, contact the author to discuss cross-disciplinary analysis.
Verdict challenge: If you disagree with an assigned verdict, detailed counterargument submissions are welcomed and may be published as addenda.
Paper nomination: Recommend papers you believe deserve review in this series. Send title, arXiv/DOI link, and brief justification.
Localization: Translate series content to other languages for distribution in non-English-speaking research communities.
Institutional adoption: Use the verdict framework in your own research group or course as a framework for critical reading practices.
All Articles
1 AI Transforming Science: Math, Biology, and Discovery 2025 DOI 3/10 47 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 13% ○ ≥80% from editorially reviewed sources [t] Trusted 25% ○ ≥80% from verified, high-quality sources [a] DOI 13% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 25% ○ ≥80% have metadata indexed [l] Academic 25% ○ ≥80% from journals/conferences/preprints [f] Free Access 38% ○ ≥80% are freely accessible [r] References 8 refs ○ Minimum 10 references required [w] Words [REQ] 3,015 ✓ Minimum 1,000 words of article body. Current: 3,015 [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.18748877 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 25% ○ ≥60% of references from 2025–2026. Current: 25% [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 (24 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Feb 2, 2026 · 15 min read
2 The Cognitive Shift: A Creative Vision of How AI Will Change the Way We Think and Perceive DOI 2/10 69 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 33% ○ ≥80% from editorially reviewed sources [t] Trusted 86% ✓ ≥80% from verified, high-quality sources [a] DOI 43% ○ ≥80% have a Digital Object Identifier [b] CrossRef 38% ○ ≥80% indexed in CrossRef [i] Indexed 48% ○ ≥80% have metadata indexed [l] Academic 43% ○ ≥80% from journals/conferences/preprints [f] Free Access 48% ○ ≥80% are freely accessible [r] References 21 refs ✓ Minimum 10 references required [w] Words [REQ] 5,959 ✓ Minimum 1,000 words of article body. Current: 5,959 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 24 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 24 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18685239 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 0% ○ ≥60% 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 3 ✓ Mermaid architecture/flow diagrams. Current: 3 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (61 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Feb 18, 2026 · 30 min read
3 When AI Finally Beats the Experts: DeepRare and the End of the Diagnostic Odyssey DOI 3/10 61 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 36% ○ ≥80% from editorially reviewed sources [t] Trusted 50% ○ ≥80% from verified, high-quality sources [a] DOI 36% ○ ≥80% have a Digital Object Identifier [b] CrossRef 29% ○ ≥80% indexed in CrossRef [i] Indexed 29% ○ ≥80% have metadata indexed [l] Academic 57% ○ ≥80% from journals/conferences/preprints [f] Free Access 21% ○ ≥80% are freely accessible [r] References 14 refs ✓ Minimum 10 references required [w] Words [REQ] 2,222 ✓ Minimum 1,000 words of article body. Current: 2,222 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 11 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 11 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18730582 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 27% ○ ≥60% of references from 2025–2026. Current: 27% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 5 ✓ Mermaid architecture/flow diagrams. Current: 5 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Feb 22, 2026 · 11 min read
4 AI Agents Operate With Minimal Safety Disclosures: MIT Study Reveals Transparency Gap DOI 4/10 47 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 0% ○ ≥80% from editorially reviewed sources [t] Trusted 33% ○ ≥80% from verified, high-quality sources [a] DOI 8% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 17% ○ ≥80% have metadata indexed [l] Academic 17% ○ ≥80% from journals/conferences/preprints [f] Free Access 42% ○ ≥80% are freely accessible [r] References 12 refs ✓ Minimum 10 references required [w] Words [REQ] 3,097 ✓ Minimum 1,000 words of article body. Current: 3,097 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 10 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741627 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 31% ○ ≥60% of references from 2025–2026. Current: 31% [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 (23 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Feb 23, 2026 · 16 min read
— Daily Journal: The 95% Crisis — When AI Pilots Can't Cross the Production Chasm (Draft — in preparation)
5 AI is not like us? DOI 3/10 60 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 12% ○ ≥80% from editorially reviewed sources [t] Trusted 65% ○ ≥80% from verified, high-quality sources [a] DOI 19% ○ ≥80% have a Digital Object Identifier [b] CrossRef 15% ○ ≥80% indexed in CrossRef [i] Indexed 42% ○ ≥80% have metadata indexed [l] Academic 46% ○ ≥80% from journals/conferences/preprints [f] Free Access 54% ○ ≥80% are freely accessible [r] References 26 refs ✓ Minimum 10 references required [w] Words [REQ] 2,897 ✓ Minimum 1,000 words of article body. Current: 2,897 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 20 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 20 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18824472 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 4% ○ ≥60% of references from 2025–2026. Current: 4% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 4 ✓ Mermaid architecture/flow diagrams. Current: 4 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (46 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Mar 1, 2026 · 15 min read
6 The Planning Illusion DOI 3/10 53 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 0% ○ ≥80% from editorially reviewed sources [t] Trusted 50% ○ ≥80% from verified, high-quality sources [a] DOI 13% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 50% ○ ≥80% have metadata indexed [l] Academic 13% ○ ≥80% from journals/conferences/preprints [f] Free Access 100% ✓ ≥80% are freely accessible [r] References 8 refs ○ Minimum 10 references required [w] Words [REQ] 3,096 ✓ Minimum 1,000 words of article body. Current: 3,096 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 23 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 23 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18824558 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 20% ○ ≥60% of references from 2025–2026. Current: 20% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 5 ✓ Mermaid architecture/flow diagrams. Current: 5 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (34 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Mar 1, 2026 · 16 min read
7 World Models: The Next AI Paradigm — Morning Review 2026-03-02 DOI 10/10 52 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 7% ○ ≥80% from editorially reviewed sources [t] Trusted 43% ○ ≥80% from verified, high-quality sources [a] DOI 14% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 29% ○ ≥80% have metadata indexed [l] Academic 29% ○ ≥80% from journals/conferences/preprints [f] Free Access 36% ○ ≥80% are freely accessible [r] References 14 refs ✓ Minimum 10 references required [w] Words [REQ] 2,219 ✓ Minimum 1,000 words of article body. Current: 2,219 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 11 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 11 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18829069 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 40% ○ ≥60% of references from 2025–2026. Current: 40% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 4 ✓ Mermaid architecture/flow diagrams. Current: 4 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Mar 2, 2026 · 11 min read
8 AI Pragmatism — The Morning After the Hype Party DOI 3/10 47 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 0% ○ ≥80% from editorially reviewed sources [t] Trusted 30% ○ ≥80% from verified, high-quality sources [a] DOI 15% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 15% ○ ≥80% have metadata indexed [l] Academic 15% ○ ≥80% from journals/conferences/preprints [f] Free Access 35% ○ ≥80% are freely accessible [r] References 20 refs ✓ Minimum 10 references required [w] Words [REQ] 2,008 ✓ Minimum 1,000 words of article body. Current: 2,008 [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.18838622 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 47% ○ ≥60% of references from 2025–2026. Current: 47% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 4 ✓ Mermaid architecture/flow diagrams. Current: 4 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (23 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Mar 2, 2026 · 10 min read
9 Super-Agent Front Door: Who Controls the Interface Controls the Market DOI 4/10 40 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 0% ○ ≥80% from editorially reviewed sources [t] Trusted 5% ○ ≥80% from verified, high-quality sources [a] DOI 5% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 5% ○ ≥80% have metadata indexed [l] Academic 5% ○ ≥80% from journals/conferences/preprints [f] Free Access 16% ○ ≥80% are freely accessible [r] References 19 refs ✓ Minimum 10 references required [w] Words [REQ] 2,323 ✓ Minimum 1,000 words of article body. Current: 2,323 [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.18844227 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 60% ✓ ≥60% of references from 2025–2026. Current: 60% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 4 ✓ Mermaid architecture/flow diagrams. Current: 4 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (10 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
Journal Commentary · Mar 3, 2026 · 12 min read
10 AI Agents in the Trough: The Reality Check on Agentic AI DOI 4/10 46 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 0% ○ ≥80% from editorially reviewed sources [t] Trusted 33% ○ ≥80% from verified, high-quality sources [a] DOI 8% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 17% ○ ≥80% have metadata indexed [l] Academic 8% ○ ≥80% from journals/conferences/preprints [f] Free Access 50% ○ ≥80% are freely accessible [r] References 12 refs ✓ Minimum 10 references required [w] Words [REQ] 2,185 ✓ Minimum 1,000 words of article body. Current: 2,185 [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.18865601 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 38% ○ ≥60% of references from 2025–2026. Current: 38% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 4 ✓ Mermaid architecture/flow diagrams. Current: 4 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Mar 4, 2026 · 11 min read
11 Daily Review: AI Hallucinations in Wartime — When Chatbots Get Geopolitics Wrong DOI 4/10 51 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 17% ○ ≥80% from editorially reviewed sources [t] Trusted 35% ○ ≥80% from verified, high-quality sources [a] DOI 22% ○ ≥80% have a Digital Object Identifier [b] CrossRef 17% ○ ≥80% indexed in CrossRef [i] Indexed 17% ○ ≥80% have metadata indexed [l] Academic 26% ○ ≥80% from journals/conferences/preprints [f] Free Access 17% ○ ≥80% are freely accessible [r] References 23 refs ✓ Minimum 10 references required [w] Words [REQ] 3,140 ✓ Minimum 1,000 words of article body. Current: 3,140 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 20 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 20 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18884216 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 29% ○ ≥60% of references from 2025–2026. Current: 29% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 5 ✓ Mermaid architecture/flow diagrams. Current: 5 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (30 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Mar 6, 2026 · 16 min read
12 Daily Review: MIT Sloan Pulls Back Agentic AI Expectations — March 2026 Recalibration DOI 7/10 45 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 0% ○ ≥80% from editorially reviewed sources [t] Trusted 31% ○ ≥80% from verified, high-quality sources [a] DOI 8% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 15% ○ ≥80% have metadata indexed [l] Academic 8% ○ ≥80% from journals/conferences/preprints [f] Free Access 46% ○ ≥80% are freely accessible [r] References 13 refs ✓ Minimum 10 references required [w] Words [REQ] 2,135 ✓ Minimum 1,000 words of article body. Current: 2,135 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 10 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18930643 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 43% ○ ≥60% of references from 2025–2026. Current: 43% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 4 ✓ Mermaid architecture/flow diagrams. Current: 4 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (21 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Mar 7, 2026 · 11 min read
13 Agent Auditor — The Rise of a New Profession DOI 3/10 58 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 7% ○ ≥80% from editorially reviewed sources [t] Trusted 36% ○ ≥80% from verified, high-quality sources [a] DOI 21% ○ ≥80% have a Digital Object Identifier [b] CrossRef 7% ○ ≥80% indexed in CrossRef [i] Indexed 86% ✓ ≥80% have metadata indexed [l] Academic 21% ○ ≥80% from journals/conferences/preprints [f] Free Access 36% ○ ≥80% are freely accessible [r] References 14 refs ✓ Minimum 10 references required [w] Words [REQ] 2,439 ✓ Minimum 1,000 words of article body. Current: 2,439 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 10 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18902439 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 29% ○ ≥60% of references from 2025–2026. Current: 29% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 5 ✓ Mermaid architecture/flow diagrams. Current: 5 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (42 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Mar 7, 2026 · 12 min read
14 Agent Auditor — Part 2: Skills, Tools & Frameworks DOI 4/10 47 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 3% ○ ≥80% from editorially reviewed sources [t] Trusted 30% ○ ≥80% from verified, high-quality sources [a] DOI 17% ○ ≥80% have a Digital Object Identifier [b] CrossRef 3% ○ ≥80% indexed in CrossRef [i] Indexed 13% ○ ≥80% have metadata indexed [l] Academic 17% ○ ≥80% from journals/conferences/preprints [f] Free Access 43% ○ ≥80% are freely accessible [r] References 30 refs ✓ Minimum 10 references required [w] Words [REQ] 2,532 ✓ Minimum 1,000 words of article body. Current: 2,532 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 24 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 24 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18923680 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 7% ○ ≥60% of references from 2025–2026. Current: 7% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 5 ✓ Mermaid architecture/flow diagrams. Current: 5 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (24 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Mar 9, 2026 · 12 min read
— AI Architecture Comparison Observatory: AADA vs LLM-First Agents (Draft — in preparation)
15 Agent Auditor — Part 3: Career Landscape & Market Forecast DOI 5/10 50 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 0% ○ ≥80% from editorially reviewed sources [t] Trusted 36% ○ ≥80% from verified, high-quality sources [a] DOI 14% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 29% ○ ≥80% have metadata indexed [l] Academic 21% ○ ≥80% from journals/conferences/preprints [f] Free Access 50% ○ ≥80% are freely accessible [r] References 14 refs ✓ Minimum 10 references required [w] Words [REQ] 2,412 ✓ Minimum 1,000 words of article body. Current: 2,412 [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.18930666 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 31% ○ ≥60% of references from 2025–2026. Current: 31% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 5 ✓ Mermaid architecture/flow diagrams. Current: 5 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (28 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Mar 10, 2026 · 12 min read
16 When Your Research Gets Cited on Medium: A Clarification, a Thank You, and Why AGI Is Closer Than the Pessimists Think DOI 2/10 60 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 10% ○ ≥80% from editorially reviewed sources [t] Trusted 60% ○ ≥80% from verified, high-quality sources [a] DOI 20% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 60% ○ ≥80% have metadata indexed [l] Academic 50% ○ ≥80% from journals/conferences/preprints [f] Free Access 70% ○ ≥80% are freely accessible [r] References 10 refs ✓ Minimum 10 references required [w] Words [REQ] 1,510 ✓ Minimum 1,000 words of article body. Current: 1,510 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 7 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 7 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18968176 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 42% ○ ≥60% of references from 2025–2026. Current: 42% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 0 ○ Mermaid architecture/flow diagrams. Current: 0 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (48 × 60%) + Required (4/4 × 30%) + Optional (1/7 × 10%)
Journal Commentary · Mar 11, 2026 · 7 min read
17 The Confidence Gate Theorem: A Framework That Promises More Than It Proves DOI 3/10 63 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 33% ○ ≥80% from editorially reviewed sources [t] Trusted 67% ○ ≥80% from verified, high-quality sources [a] DOI 67% ○ ≥80% have a Digital Object Identifier [b] CrossRef 33% ○ ≥80% indexed in CrossRef [i] Indexed 0% ○ ≥80% have metadata indexed [l] Academic 67% ○ ≥80% from journals/conferences/preprints [f] Free Access 67% ○ ≥80% are freely accessible [r] References 6 refs ○ Minimum 10 references required [w] Words [REQ] 1,508 ✓ Minimum 1,000 words of article body. Current: 1,508 [u] Original [REQ] 99% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 99% unique [e] Evidence [REQ] 6 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 6 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18971752 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 29% ○ ≥60% of references from 2025–2026. Current: 29% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 0 ○ Mermaid architecture/flow diagrams. Current: 0 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (53 × 60%) + Required (4/4 × 30%) + Optional (1/7 × 10%)
Journal Commentary · Mar 12, 2026 · 7 min read
18 Review: Beyond the Illusion of Consensus — What the LLM-as-a-Judge Paradigm Gets Dangerously Wrong DOI 5/10 70 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 0% ○ ≥80% from editorially reviewed sources [t] Trusted 78% ○ ≥80% from verified, high-quality sources [a] DOI 22% ○ ≥80% have a Digital Object Identifier [b] CrossRef 11% ○ ≥80% indexed in CrossRef [i] Indexed 78% ○ ≥80% have metadata indexed [l] Academic 78% ○ ≥80% from journals/conferences/preprints [f] Free Access 100% ✓ ≥80% are freely accessible [r] References 9 refs ○ Minimum 10 references required [w] Words [REQ] 1,739 ✓ Minimum 1,000 words of article body. Current: 1,739 [u] Original [REQ] 99% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 99% unique [e] Evidence [REQ] 6 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 6 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18973500 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 10% ○ ≥60% of references from 2025–2026. Current: 10% [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 (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Mar 12, 2026 · 9 min read
19 Can You Slap an LLM? Pain Simulation as a Path to Responsible AI Behavior DOI 7/10 63 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 5% ○ ≥80% from editorially reviewed sources [t] Trusted 71% ○ ≥80% from verified, high-quality sources [a] DOI 19% ○ ≥80% have a Digital Object Identifier [b] CrossRef 5% ○ ≥80% indexed in CrossRef [i] Indexed 14% ○ ≥80% have metadata indexed [l] Academic 71% ○ ≥80% from journals/conferences/preprints [f] Free Access 95% ✓ ≥80% are freely accessible [r] References 21 refs ✓ Minimum 10 references required [w] Words [REQ] 4,537 ✓ Minimum 1,000 words of article body. Current: 4,537 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 14 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 14 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19347956 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 67% ✓ ≥60% of references from 2025–2026. Current: 67% [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 (45 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)
Journal Commentary · Mar 31, 2026 · 22 min read
20 FLAI & GROMUS Mathematical Glossary: Complete Variable Reference for Social Media Trend Prediction Models DOI 2/10 25 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 0% ○ ≥80% from editorially reviewed sources [t] Trusted 71% ○ ≥80% from verified, high-quality sources [a] DOI 71% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 71% ○ ≥80% have metadata indexed [l] Academic 71% ○ ≥80% from journals/conferences/preprints [f] Free Access 100% ✓ ≥80% are freely accessible [r] References 7 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] Freshness 0% ○ ≥60% 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 (68 × 60%) + Required (3/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Mar 31, 2026 · 8 min read
21 The Mirror and the Self: What AI Reveals About Being Human DOI 3/10 64 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 6% ○ ≥80% from editorially reviewed sources [t] Trusted 82% ✓ ≥80% from verified, high-quality sources [a] DOI 18% ○ ≥80% have a Digital Object Identifier [b] CrossRef 12% ○ ≥80% indexed in CrossRef [i] Indexed 6% ○ ≥80% have metadata indexed [l] Academic 53% ○ ≥80% from journals/conferences/preprints [f] Free Access 88% ✓ ≥80% are freely accessible [r] References 17 refs ✓ Minimum 10 references required [w] Words [REQ] 1,120 ✓ Minimum 1,000 words of article body. Current: 1,120 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 8 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19497196 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 82% ✓ ≥60% of references from 2025–2026. Current: 82% [c] Data Charts 2 ✓ Original data charts from reproducible analysis (min 2). Current: 2 [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 (44 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)
Journal Commentary · Apr 10, 2026 · 6 min read
22 The Human Needs Its AI Copy - Memory Synchronization and Personal Agents DOI 2/10 59 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 14% ○ ≥80% from editorially reviewed sources [t] Trusted 71% ○ ≥80% from verified, high-quality sources [a] DOI 29% ○ ≥80% have a Digital Object Identifier [b] CrossRef 14% ○ ≥80% indexed in CrossRef [i] Indexed 14% ○ ≥80% have metadata indexed [l] Academic 43% ○ ≥80% from journals/conferences/preprints [f] Free Access 100% ✓ ≥80% are freely accessible [r] References 7 refs ○ Minimum 10 references required [w] Words [REQ] 1,489 ✓ Minimum 1,000 words of article body. Current: 1,489 [u] Original [REQ] 97% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 97% unique [e] Evidence [REQ] 1 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 1 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486190 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 25% ○ ≥60% of references from 2025–2026. Current: 25% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code ✓ ✓ Source code available on GitHub [m] Diagrams 0 ○ Mermaid architecture/flow diagrams. Current: 0 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (43 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Apr 10, 2026 · 7 min read
23 Self-Interpretable AI: Knowledge Distillation and Bias as Human-Level Error DOI 2/10 61 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 13% ○ ≥80% from editorially reviewed sources [t] Trusted 75% ○ ≥80% from verified, high-quality sources [a] DOI 38% ○ ≥80% have a Digital Object Identifier [b] CrossRef 13% ○ ≥80% indexed in CrossRef [i] Indexed 13% ○ ≥80% have metadata indexed [l] Academic 50% ○ ≥80% from journals/conferences/preprints [f] Free Access 100% ✓ ≥80% are freely accessible [r] References 8 refs ○ Minimum 10 references required [w] Words [REQ] 1,405 ✓ Minimum 1,000 words of article body. Current: 1,405 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 2 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 2 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486203 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 20% ○ ≥60% of references from 2025–2026. Current: 20% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code ✓ ✓ Source code available on GitHub [m] Diagrams 0 ○ Mermaid architecture/flow diagrams. Current: 0 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Apr 10, 2026 · 7 min read
— Conscious Products: When AI Is the Product Personality Itself (Draft — in preparation)
24 Ubiquitous AI Integration: When Every Human Action Has an AI Partner DOI 2/10 48 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 0% ○ ≥80% from editorially reviewed sources [t] Trusted 50% ○ ≥80% from verified, high-quality sources [a] DOI 17% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 0% ○ ≥80% have metadata indexed [l] Academic 33% ○ ≥80% from journals/conferences/preprints [f] Free Access 100% ✓ ≥80% are freely accessible [r] References 6 refs ○ Minimum 10 references required [w] Words [REQ] 4,445 ✓ Minimum 1,000 words of article body. Current: 4,445 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 8 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19503250 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 33% ○ ≥60% of references from 2025–2026. Current: 33% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 0 ○ Mermaid architecture/flow diagrams. Current: 0 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (27 × 60%) + Required (4/4 × 30%) + Optional (1/7 × 10%)
Journal Commentary · Apr 10, 2026 · 22 min read
— AI Memory Architecture: From Fixed Windows to Persistent State (Draft — in preparation)
25 The AI Mirror: What AI Reveals About Being Human DOI 2/10 56 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 11% ○ ≥80% from editorially reviewed sources [t] Trusted 67% ○ ≥80% from verified, high-quality sources [a] DOI 22% ○ ≥80% have a Digital Object Identifier [b] CrossRef 11% ○ ≥80% indexed in CrossRef [i] Indexed 11% ○ ≥80% have metadata indexed [l] Academic 33% ○ ≥80% from journals/conferences/preprints [f] Free Access 100% ✓ ≥80% are freely accessible [r] References 9 refs ○ Minimum 10 references required [w] Words [REQ] 1,351 ✓ Minimum 1,000 words of article body. Current: 1,351 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 1 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 1 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486197 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 25% ○ ≥60% of references from 2025–2026. Current: 25% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code ✓ ✓ Source code available on GitHub [m] Diagrams 0 ○ Mermaid architecture/flow diagrams. Current: 0 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Apr 11, 2026 · 7 min read
— The XAI Frontier: What Comes After SHAP and LIME (Draft — in preparation)
— Causal Explanation AI: The Next Breakthrough in Explainable Artificial Intelligence (Draft — in preparation)
— Industry Transformation 2026-2030: Which Sectors Will Be Most Transformed by AI (Draft — in preparation)
— The Healthcare AI Transformation Map: From Diagnosis to Treatment Planning (Draft — in preparation)
— The Financial Industry AI Transformation: From Trading to Compliance (Draft — in preparation)
— The Legal Industry AI Transformation: From Research to Courtroom (Draft — in preparation)
— The Manufacturing AI Transformation: From Reactive to Predictive to Prescriptive (Draft — in preparation)
— The Transportation AI Transformation: From Vehicles to Logistics Networks (Draft — in preparation)
— The Education AI Transformation: From Classrooms to Personalized Learning Pathways (Draft — in preparation)
— The Trust Architecture: Designing AI Systems That Earn Explainability-Based Trust (Draft — in preparation)
— Human-AI Collaboration Futures: When Explanations Enable Better Human-AI Teams (Draft — in preparation)
— AI Transparency as Competitive Moat: Why Explainability Creates Sustainable Advantage (Draft — in preparation)
26 Citation Hallucination Rates in LLM-Generated Research: A 2025 Benchmark Across 10 Models DOI 9/10 57 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 0% ○ ≥80% from editorially reviewed sources [t] Trusted 67% ○ ≥80% from verified, high-quality sources [a] DOI 33% ○ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 0% ○ ≥80% have metadata indexed [l] Academic 67% ○ ≥80% from journals/conferences/preprints [f] Free Access 67% ○ ≥80% are freely accessible [r] References 3 refs ○ Minimum 10 references required [w] Words [REQ] 1,632 ✓ Minimum 1,000 words of article body. Current: 1,632 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 2 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 2 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21131031 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 25% ○ ≥60% of references from 2025–2026. Current: 25% [c] Data Charts 0 ○ Original data charts from reproducible analysis (min 2). Current: 0 [g] Code — ○ Source code available on GitHub [m] Diagrams 4 ✓ Mermaid architecture/flow diagrams. Current: 4 [x] Cited by 0 ○ Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
Journal Commentary · Jul 2, 2026 · 8 min read
— Post-Transformer Architectures in 2025: Mamba, RWKV, and Hybrid Models in Production (Draft — in preparation)
— AI Agent Reliability in 2025: Failure Modes and Success Rates of Long-Horizon Tasks (Draft — in preparation)
— Multimodal AI Reasoning: Benchmarking Vision-Language Models on Scientific and Engineering Tasks (Draft — in preparation)
— The 2025 AI Safety Landscape: Mechanistic Interpretability Results and Their Practical Implications (Draft — in preparation)
— Foundation Model Commoditization: How API Parity Is Reshaping the AI Stack in 2025 (Draft — in preparation)
— Claim Density and Evidence Ratio: Automated Quality Signals for AI-Generated Technical Content (Draft — in preparation)
— Mixture of Experts Scaling Laws: What MoE Architectures Mean for 2025-2026 Model Development (Draft — in preparation)
27 Iterative Quality Improvement in AI Writing Pipelines: Measuring Redactor Cycle Effectiveness DOI 2/10 79 s t a b i l f r · w u e d o p h c g m x Badge Metric Value Status Description [s] Reviewed Sources 0% ○ ≥80% from editorially reviewed sources [t] Trusted 97% ✓ ≥80% from verified, high-quality sources [a] DOI 93% ✓ ≥80% have a Digital Object Identifier [b] CrossRef 0% ○ ≥80% indexed in CrossRef [i] Indexed 17% ○ ≥80% have metadata indexed [l] Academic 97% ✓ ≥80% from journals/conferences/preprints [f] Free Access 100% ✓ ≥80% are freely accessible [r] References 29 refs ✓ Minimum 10 references required [w] Words [REQ] 1,874 ✓ Minimum 1,000 words of article body. Current: 1,874 [u] Original [REQ] 100% ✓ ≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique [e] Evidence [REQ] 26 ✓ Cites at least 1 external source(s): links, DOIs or a reference list. Current: 26 [d] DOI [REQ] ✓ ✓ Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21884458 [o] ORCID ✓ ✓ Author ORCID verified for academic identity [p] Peer Reviewed — ○ Peer reviewed by an assigned reviewer [h] Freshness 61% ✓ ≥60% of references from 2025–2026. Current: 61% [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 (75 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
Journal Commentary · Aug 11, 2026 · 9 min read
— AI Agent Memory Architectures: Episodic, Semantic, and Working Memory in Long-Horizon Tasks (Draft — in preparation)
— World Models for AI Planning: Current State and Gaps Between Research and Deployment (Draft — in preparation)
— Multimodal AI in Scientific Discovery: 2025 Benchmarks in Drug Discovery and Materials Science (Draft — in preparation)
— Multimodal AI in Scientific Discovery: 2025 Benchmarks in Drug Discovery and Materials Science (Draft — in preparation)
— AI Alignment Progress Report 2025: RLHF Successors and Constitutional AI Evaluation (Draft — in preparation)
— Test-Time Compute Scaling: When Letting a Model \'Think Longer\' Beats a Bigger Model (Draft — in preparation)
— Interoperable Agent Protocols: Evaluating MCP and Successor Standards for Cross-Vendor Tool Use (Draft — in preparation)
— Agentic Benchmarks 2026: Measuring Long-Horizon Task Completion Beyond Single-Turn Evals (Draft — in preparation)
— The Compute-Efficient Frontier: Small Models Matching Large-Model Performance via Better Post-Training (Draft — in preparation)
— Continual Learning Without Catastrophic Forgetting: Production-Ready Approaches in 2026 (Draft — in preparation)