Category: Universal Intelligence Benchmark

Inference-agnostic intelligence measurement framework. Meta-research and novel benchmarks for AI beyond text generation.

Causal Intelligence as a UIB Dimension: Measuring What Models Actually Understand

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Benchmark Research
Benchmark Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19102383  58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted85%✓≥80% from verified, high-quality sources
[a]DOI62%○≥80% have a Digital Object Identifier
[b]CrossRef8%○≥80% indexed in CrossRef
[i]Indexed62%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]1,940✗Minimum 2,000 words for a full research article. Current: 1,940
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19102383
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[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 (73 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Current AI benchmarks predominantly measure pattern recognition and statistical correlation — capabilities that, while impressive, fall short of genuine understanding. This article introduces Causal Intelligence as a formal dimension within the Universal Intelligence Benchmark (UIB) framework, arguing that any credible measure of machine intelligence must evaluate whether systems can reason abo...

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Benchmark Research by Oleh Ivchenko DOI: 10.5281/zenodo.19102383 58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted85%✓≥80% from verified, high-quality sources
[a]DOI62%○≥80% have a Digital Object Identifier
[b]CrossRef8%○≥80% indexed in CrossRef
[i]Indexed62%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]1,940✗Minimum 2,000 words for a full research article. Current: 1,940
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19102383
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[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 (73 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Inference-Agnostic Intelligence: The UIB Theoretical Framework

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Benchmark Research
Benchmark Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19064304  62stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources13%○≥80% from editorially reviewed sources
[t]Trusted81%✓≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed75%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access69%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,086✓Minimum 2,000 words for a full research article. Current: 2,086
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19064304
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]40%✗≥60% of references from 2025–2026. Current: 40%
[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 (69 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Current AI benchmarks measure narrow task performance — accuracy on question sets, code generation pass rates, or image recognition scores. They rarely ask the deeper question: what is intelligence, and how should we measure it independent of the hardware, API, or inference provider running the model? This article proposes the Universal Intelligence Benchmark (UIB) theoretical framework: an eig...

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Benchmark Research by Oleh Ivchenko DOI: 10.5281/zenodo.19064304 62stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources13%○≥80% from editorially reviewed sources
[t]Trusted81%✓≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed75%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access69%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,086✓Minimum 2,000 words for a full research article. Current: 2,086
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19064304
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]40%✗≥60% of references from 2025–2026. Current: 40%
[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 (69 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Measurement Crisis: Saturation, Goodhart’s Law, and the End of AI Leaderboards

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Benchmark Research
Benchmark Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19007432  64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed67%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]3,049✓Minimum 2,000 words for a full research article. Current: 3,049
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19007432
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]67%✓≥60% of references from 2025–2026. Current: 67%
[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 (62 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The AI evaluation ecosystem is in crisis. Frontier models now exceed 90% accuracy on MMLU, 95% on HumanEval, and 93% on HellaSwag — scores that were considered unattainable three years ago. This saturation is not evidence of intelligence; it is evidence that our instruments have failed. We argue that three convergent forces have rendered current AI leaderboards meaningless: (1) benchmark satura...

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Benchmark Research by Oleh Ivchenko DOI: 10.5281/zenodo.19007432 64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed67%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]3,049✓Minimum 2,000 words for a full research article. Current: 3,049
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19007432
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]67%✓≥60% of references from 2025–2026. Current: 67%
[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 (62 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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The Meta-Meta-Analysis: A Systematic Map of What 200 AI Benchmark Studies Actually Measured

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Benchmark Research
Benchmark Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19001033  43stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted44%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed44%○≥80% have metadata indexed
[l]Academic44%○≥80% from journals/conferences/preprints
[f]Free Access38%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,353✓Minimum 2,000 words for a full research article. Current: 2,353
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19001033
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]38%✗≥60% of references from 2025–2026. Current: 38%
[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 (37 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

We present a meta-meta-analysis of 217 benchmark evaluation studies published between 2020 and 2026, examining not the benchmarks themselves but the systematic reviews that assess them. Our coverage matrix reveals a profound structural bias: 78.3% of surveyed studies evaluate text-based capabilities, while causal reasoning (4.1%), embodied intelligence (1.8%), and social cognition (0.9%) remain...

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Benchmark Research by Oleh Ivchenko DOI: 10.5281/zenodo.19001033 43stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted44%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed44%○≥80% have metadata indexed
[l]Academic44%○≥80% from journals/conferences/preprints
[f]Free Access38%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,353✓Minimum 2,000 words for a full research article. Current: 2,353
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19001033
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]38%✗≥60% of references from 2025–2026. Current: 38%
[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 (37 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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