Author: Admin

2025 AI Research Impact: A Year of Transformation

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DOI: 10.5281/zenodo.18752916  32stabilfr·wdophcgmx
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2025 marked a fundamental shift in artificial intelligence research—transitioning from "powerful tool" to "fundamental infrastructure." This comprehensive review examines the year's transformative achievements across model efficiency, reasoning capabilities, multimodal intelligence, and real-world deployment. We analyze key breakthroughs including the evolution of the Gemini model series, the e...

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DOI: 10.5281/zenodo.18752916 32stabilfr·wdophcgmx
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Google Antigravity: Redefining AI-Assisted Software Development

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DOI: 10.5281/zenodo.18752918  13stabilfr·wdophcgmx
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Google Antigravity, launched November 2025, represents the next generation of AI-assisted coding. It's not a tool that suggests code—it's a developer that works alongside you.

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DOI: 10.5281/zenodo.18752918 13stabilfr·wdophcgmx
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Pixel 10 & AI Integration: The Agentic Phone Era Begins

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DOI: 10.5281/zenodo.18752920  31stabilfr·wdophcgmx
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Pixel 10 represents a watershed moment: the first phone designed from the ground up to be an agentic device. It's not just running AI—it's an AI that runs a phone.

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DOI: 10.5281/zenodo.18752920 31stabilfr·wdophcgmx
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AI Transforming Science: Math, Biology, and Discovery 2025

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18748877  35stabilfr·wdophcgmx
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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
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Gemini 3: Google’s Leap in Reasoning and Multimodal AI

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DOI: 10.5281/zenodo.18752922  13stabilfr·wdophcgmx
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In November 2025, Google unveiled Gemini 3 Pro, marking a watershed moment in AI development. This model represents a quantum leap in reasoning capabilities, multimodal understanding, and raw intelligence.

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DOI: 10.5281/zenodo.18752922 13stabilfr·wdophcgmx
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Hyperscale AI Data Centers: The Infrastructure Revolution

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DOI: 10.5281/zenodo.18752932  13stabilfr·wdophcgmx
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Old strategy: build bigger data centers. New strategy: smarter, distributed, efficient systems that do more with less.

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DOI: 10.5281/zenodo.18752932 13stabilfr·wdophcgmx
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Agent Interoperability: Building the AI Economy of Tomorrow

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DOI: 10.5281/zenodo.18752926  13stabilfr·wdophcgmx
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Score = Ref Trust (1 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

Today: AI agents are isolated. Cannot collaborate. Cannot discover each other. Tomorrow: an "agent economy" where systems work together seamlessly.

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DOI: 10.5281/zenodo.18752926 13stabilfr·wdophcgmx
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Score = Ref Trust (1 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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English as the New Programming Language: AI Coding Revolution

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DOI: 10.5281/zenodo.18752934  13stabilfr·wdophcgmx
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Score = Ref Trust (1 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

For decades: l[REDACTED]g to code was hard. Now: describing what you want is the bottleneck. AI handles syntax, patterns, implementation. Humans handle design and creativity.

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DOI: 10.5281/zenodo.18752934 13stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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Score = Ref Trust (1 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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AI in Healthcare 2026: From Research Settings to Real-World Impact

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DOI: 10.5281/zenodo.18752936  21stabilfr·wdophcgmx
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[a]DOI0%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
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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 (1 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Artificial intelligence has transitioned from experimental research to operational deployment across healthcare systems globally. This comprehensive analysis examines the 2026 landscape of medical AI adoption, documenting the gap between regulatory approval—1,200+ FDA-cleared devices—and clinical implementation, where 81% of U.S. hospitals maintain zero AI adoption. We analyze deployment patter...

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DOI: 10.5281/zenodo.18752936 21stabilfr·wdophcgmx
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Open-Source Models Breaking the AI Monopoly

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DOI: 10.5281/zenodo.18752938  51stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI22%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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The artificial intelligence landscape is undergoing a fundamental transformation as open-source models challenge the dominance of proprietary systems. This analysis examines the economic, technical, and strategic implications of open-source AI adoption for enterprise organizations. We demonstrate that the most significant advances now occur in post-training rather than pre-training, making fron...

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DOI: 10.5281/zenodo.18752938 51stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI22%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed78%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,949✗Minimum 2,000 words for a full research article. Current: 1,949
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752938
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (60 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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