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Aristotelian Representation Hypothesis

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Neural networks trained with different objectives, on different data and modalities, converge to shared local neighborhood relationships -- but not to shared global spectral geometry. Once representational-similarity metrics are corrected for a scale confound (see null-calibrated-representational-similarity), CKA-style global convergence largely disappears while mutual k-NN-style local convergence persists. Proposed by Groger, Wen & Brbic (2026) as a calibrated refinement of the Platonic Representation Hypothesis.

Replicationcomputed from the corpus — never hand-assigned
1 paper3 architecture classes · one study2 domains · one study8 model families · one study
Filled = two or more values reported by papers that share no author — replication. Outlined = two or more values, but all from a single study — breadth, not replication. Grey = a single value. Derived from paper authorship and each model's architecture class, domain and family; it updates itself when a paper is added.

Statement

“Neural networks, trained with different objectives on different data and modalities, are converging to shared local neighborhood relationships” (Gröger, Wen & Brbić, 2026, arXiv:2602.14486, project statement). This is a deliberately narrower claim than the Platonic Representation Hypothesis (PRH): PRH’s own defining evidence uses both global spectral metrics (CKA-style) and local-neighborhood metrics (mutual k-NN) more or less interchangeably as if they measured the same underlying convergence. The Aristotelian view holds that once both metric families are corrected for their scale-dependent null baseline via Null-calibrated representational similarity, only the local- neighborhood family continues to show significant cross-model, cross-modality agreement — “the apparent convergence reported by global spectral measures largely disappears after calibration, while local neighborhood similarity… retains significant agreement across different modalities.”

Intuition

Two independently-trained networks might not agree on the overall shape of their representation spaces (how spread out or curved the whole cloud of points is, which is what global spectral metrics like CKA summarize), while still agreeing on something more local and, in a sense, more basic: which specific points are close neighbors of which. Aristotle’s empiricism emphasized particulars and their immediate relations over an idealized universal Form — the name reflects a parallel move here: from a claim about one shared global geometric Form (Platonic) to a claim about shared local relational structure between particulars (Aristotelian).

Properties

  • A calibration story, not a new similarity metric. The hypothesis rests entirely on Null-calibrated representational similarity‘s correction, applied post-hoc to existing metrics (CKA, mutual k-NN, RSA, CCA, Procrustes) — it makes no claim that any of these metrics is individually flawed in its definition, only that raw, uncalibrated scores confound genuine cross-model agreement with a scale-dependent null baseline that itself grows with width and (via layer-selection) depth.
  • Narrows, rather than refutes, PRH. The claim is not that representational convergence is illusory, but that it is real and significant specifically at the local-neighborhood level, while the global, “same overall Form” reading of convergence that raw CKA numbers seemed to support is largely an artifact of the uncorrected metric’s scale confound.
  • Two theoretically-derived null baselines with different orders. Global spectral metrics have a null baseline of order O(dxdy/n)\mathcal{O}(d_x d_y/n) (grows with representation width); local mutual k-NN has a null baseline of order O(k/n)\mathcal{O}(k/n) (stays small since neighborhood size kk is typically far smaller than ambient dimension dd) — this asymmetry in null-baseline growth rate is the specific mathematical reason the two metric families can diverge after calibration even when they agreed (both looked “high”) before it.
  • Tested on real, named pretrained models across two modality pairings, following and extending the original PRH’s own image-text experimental setup (BLOOMZ, OpenLLaMA/LLaMA, Gemma-2-9B-it language side; AugReg/MAE/DINOv2/CLIP-LAION2B ViT vision side) to a video-language setting (VideoMAE plus DINOv2/CLIP vision-side encoders).

Key papers

  • Huh, Cheung, Wang & Isola (2024). The Platonic Representation Hypothesis. ICML, arXiv:2405.07987 — the hypothesis this page calibrates and narrows; see representational-convergence for its own (uncalibrated) evidence.
  • Gröger, Wen & Brbić (2026). Revisiting the Platonic Representation Hypothesis: An Aristotelian View. arXiv:2602.14486, ICML 2026. Derives closed-form null baselines for global spectral vs. local-neighborhood similarity metrics under an independence null model, introduces a permutation-based calibration framework (Null-calibrated representational similarity) to correct for them, and re-runs PRH-style image-text and video-language alignment experiments through this calibration on real pretrained encoders. See groger-wen-brbic-2026-null-calibration-shows-global-spectral-representational-convergence-largely-disappears-while-local-neighborhood-convergence-persists.

Found in (1 observation · 8 families)

BLOOMZ

Revisiting the Platonic Representation Hypothesis: An Aristotelian View (2026)measured

After correcting CKA and mutual k-NN alignment for a proven width/depth-dependent null-baseline confound (O(d/n) for global spectral metrics vs O(k/n) for local-neighborhood metrics), the Platonic Representation Hypothesis's global cross-modal convergence largely vanishes while its local-neighborhood convergence survives, across real image-text and video-language pretrained model pairs

Details

Gröger, Wen & Brbić (2026) prove that representational-similarity metrics have a non-vanishing expected value even between statistically independent representations: global spectral metrics like CKA carry an $\mathcal{O}(d_xd_y/n)$ null baseline that grows with representation width (and, via layer-selection, depth), while local-neighborhood metrics like mutual k-NN carry a much smaller $\mathcal{O}(k/n)$ baseline. They introduce a permutation-based null-calibration framework ([[null-calibrated-representational-similarity]]) that estimates and subtracts off this baseline for any similarity metric, then re-run the Platonic Representation Hypothesis's own image-text alignment experiments (following Huh et al. 2024's setup: BLOOMZ, OpenLLaMA, base LLaMA and Gemma-2-9B-it on the language side; AugReg/MAE/DINOv2/ CLIP-LAION2B Vision Transformers on the vision side, paired via Wikipedia image-caption data) plus a new video-language extension (VideoMAE base/large/huge paired with DINOv2/CLIP-LAION2B vision-side encoders and the same language-model family) through this calibration. The result: "the apparent convergence reported by global spectral measures largely disappears after calibration, while local neighborhood similarity... retains significant agreement across different modalities" -- a genuine narrowing of the Platonic Representation Hypothesis's original claim rather than its wholesale refutation, motivating the [[aristotelian-representation-hypothesis]]. The paper's precise correlation coefficients, effect sizes and permutation p-values for each modality pairing sit in Section 6 and Appendix E.8/E.9 of the source, which could not be retrieved despite three separate full-text fetch attempts (direct fetch, plus two dedicated subagents trying arXiv HTML v1/v2, ar5iv, ar5iv.org, a Jina reader mirror, Semantic Scholar, OpenReview, and the paper's own GitHub raw source) -- all hit the same hard MathML-bloat truncation wall before reaching those sections. The qualitative finding quoted above comes from the authors' own official project-page summary (brbiclab.epfl.ch/projects/aristotelian), and the exact model list above was independently confirmed from the paper's own GitHub source code (`aristotelian/prh/prh_models.py`, `video_models.py`), not fabricated -- but no numeric correlation/p-value/effect-size figures are recorded here since none could be honestly sourced.

models: BLOOMZ-560M, BLOOMZ-1.1B, BLOOMZ-1.7B, BLOOMZ-3B, BLOOMZ-7.1B · method: Null-calibrated representational similarity, Centered Kernel Alignment (CKA), Mutual nearest-neighbor alignment

OpenLLaMA

Revisiting the Platonic Representation Hypothesis: An Aristotelian View (2026)measured

After correcting CKA and mutual k-NN alignment for a proven width/depth-dependent null-baseline confound (O(d/n) for global spectral metrics vs O(k/n) for local-neighborhood metrics), the Platonic Representation Hypothesis's global cross-modal convergence largely vanishes while its local-neighborhood convergence survives, across real image-text and video-language pretrained model pairs

Details

Gröger, Wen & Brbić (2026) prove that representational-similarity metrics have a non-vanishing expected value even between statistically independent representations: global spectral metrics like CKA carry an $\mathcal{O}(d_xd_y/n)$ null baseline that grows with representation width (and, via layer-selection, depth), while local-neighborhood metrics like mutual k-NN carry a much smaller $\mathcal{O}(k/n)$ baseline. They introduce a permutation-based null-calibration framework ([[null-calibrated-representational-similarity]]) that estimates and subtracts off this baseline for any similarity metric, then re-run the Platonic Representation Hypothesis's own image-text alignment experiments (following Huh et al. 2024's setup: BLOOMZ, OpenLLaMA, base LLaMA and Gemma-2-9B-it on the language side; AugReg/MAE/DINOv2/ CLIP-LAION2B Vision Transformers on the vision side, paired via Wikipedia image-caption data) plus a new video-language extension (VideoMAE base/large/huge paired with DINOv2/CLIP-LAION2B vision-side encoders and the same language-model family) through this calibration. The result: "the apparent convergence reported by global spectral measures largely disappears after calibration, while local neighborhood similarity... retains significant agreement across different modalities" -- a genuine narrowing of the Platonic Representation Hypothesis's original claim rather than its wholesale refutation, motivating the [[aristotelian-representation-hypothesis]]. The paper's precise correlation coefficients, effect sizes and permutation p-values for each modality pairing sit in Section 6 and Appendix E.8/E.9 of the source, which could not be retrieved despite three separate full-text fetch attempts (direct fetch, plus two dedicated subagents trying arXiv HTML v1/v2, ar5iv, ar5iv.org, a Jina reader mirror, Semantic Scholar, OpenReview, and the paper's own GitHub raw source) -- all hit the same hard MathML-bloat truncation wall before reaching those sections. The qualitative finding quoted above comes from the authors' own official project-page summary (brbiclab.epfl.ch/projects/aristotelian), and the exact model list above was independently confirmed from the paper's own GitHub source code (`aristotelian/prh/prh_models.py`, `video_models.py`), not fabricated -- but no numeric correlation/p-value/effect-size figures are recorded here since none could be honestly sourced.

models: OpenLLaMA-3B, OpenLLaMA-7B, OpenLLaMA-13B · method: Null-calibrated representational similarity, Centered Kernel Alignment (CKA), Mutual nearest-neighbor alignment

Llama

Revisiting the Platonic Representation Hypothesis: An Aristotelian View (2026)measured

After correcting CKA and mutual k-NN alignment for a proven width/depth-dependent null-baseline confound (O(d/n) for global spectral metrics vs O(k/n) for local-neighborhood metrics), the Platonic Representation Hypothesis's global cross-modal convergence largely vanishes while its local-neighborhood convergence survives, across real image-text and video-language pretrained model pairs

Details

Gröger, Wen & Brbić (2026) prove that representational-similarity metrics have a non-vanishing expected value even between statistically independent representations: global spectral metrics like CKA carry an $\mathcal{O}(d_xd_y/n)$ null baseline that grows with representation width (and, via layer-selection, depth), while local-neighborhood metrics like mutual k-NN carry a much smaller $\mathcal{O}(k/n)$ baseline. They introduce a permutation-based null-calibration framework ([[null-calibrated-representational-similarity]]) that estimates and subtracts off this baseline for any similarity metric, then re-run the Platonic Representation Hypothesis's own image-text alignment experiments (following Huh et al. 2024's setup: BLOOMZ, OpenLLaMA, base LLaMA and Gemma-2-9B-it on the language side; AugReg/MAE/DINOv2/ CLIP-LAION2B Vision Transformers on the vision side, paired via Wikipedia image-caption data) plus a new video-language extension (VideoMAE base/large/huge paired with DINOv2/CLIP-LAION2B vision-side encoders and the same language-model family) through this calibration. The result: "the apparent convergence reported by global spectral measures largely disappears after calibration, while local neighborhood similarity... retains significant agreement across different modalities" -- a genuine narrowing of the Platonic Representation Hypothesis's original claim rather than its wholesale refutation, motivating the [[aristotelian-representation-hypothesis]]. The paper's precise correlation coefficients, effect sizes and permutation p-values for each modality pairing sit in Section 6 and Appendix E.8/E.9 of the source, which could not be retrieved despite three separate full-text fetch attempts (direct fetch, plus two dedicated subagents trying arXiv HTML v1/v2, ar5iv, ar5iv.org, a Jina reader mirror, Semantic Scholar, OpenReview, and the paper's own GitHub raw source) -- all hit the same hard MathML-bloat truncation wall before reaching those sections. The qualitative finding quoted above comes from the authors' own official project-page summary (brbiclab.epfl.ch/projects/aristotelian), and the exact model list above was independently confirmed from the paper's own GitHub source code (`aristotelian/prh/prh_models.py`, `video_models.py`), not fabricated -- but no numeric correlation/p-value/effect-size figures are recorded here since none could be honestly sourced.

models: LLaMA-7B, LLaMA-13B, LLaMA-30B, LLaMA-65B · method: Null-calibrated representational similarity, Centered Kernel Alignment (CKA), Mutual nearest-neighbor alignment

Gemma

Revisiting the Platonic Representation Hypothesis: An Aristotelian View (2026)measured

After correcting CKA and mutual k-NN alignment for a proven width/depth-dependent null-baseline confound (O(d/n) for global spectral metrics vs O(k/n) for local-neighborhood metrics), the Platonic Representation Hypothesis's global cross-modal convergence largely vanishes while its local-neighborhood convergence survives, across real image-text and video-language pretrained model pairs

Details

Gröger, Wen & Brbić (2026) prove that representational-similarity metrics have a non-vanishing expected value even between statistically independent representations: global spectral metrics like CKA carry an $\mathcal{O}(d_xd_y/n)$ null baseline that grows with representation width (and, via layer-selection, depth), while local-neighborhood metrics like mutual k-NN carry a much smaller $\mathcal{O}(k/n)$ baseline. They introduce a permutation-based null-calibration framework ([[null-calibrated-representational-similarity]]) that estimates and subtracts off this baseline for any similarity metric, then re-run the Platonic Representation Hypothesis's own image-text alignment experiments (following Huh et al. 2024's setup: BLOOMZ, OpenLLaMA, base LLaMA and Gemma-2-9B-it on the language side; AugReg/MAE/DINOv2/ CLIP-LAION2B Vision Transformers on the vision side, paired via Wikipedia image-caption data) plus a new video-language extension (VideoMAE base/large/huge paired with DINOv2/CLIP-LAION2B vision-side encoders and the same language-model family) through this calibration. The result: "the apparent convergence reported by global spectral measures largely disappears after calibration, while local neighborhood similarity... retains significant agreement across different modalities" -- a genuine narrowing of the Platonic Representation Hypothesis's original claim rather than its wholesale refutation, motivating the [[aristotelian-representation-hypothesis]]. The paper's precise correlation coefficients, effect sizes and permutation p-values for each modality pairing sit in Section 6 and Appendix E.8/E.9 of the source, which could not be retrieved despite three separate full-text fetch attempts (direct fetch, plus two dedicated subagents trying arXiv HTML v1/v2, ar5iv, ar5iv.org, a Jina reader mirror, Semantic Scholar, OpenReview, and the paper's own GitHub raw source) -- all hit the same hard MathML-bloat truncation wall before reaching those sections. The qualitative finding quoted above comes from the authors' own official project-page summary (brbiclab.epfl.ch/projects/aristotelian), and the exact model list above was independently confirmed from the paper's own GitHub source code (`aristotelian/prh/prh_models.py`, `video_models.py`), not fabricated -- but no numeric correlation/p-value/effect-size figures are recorded here since none could be honestly sourced.

models: Gemma-2-9B-it · method: Null-calibrated representational similarity, Centered Kernel Alignment (CKA), Mutual nearest-neighbor alignment

Vision Transformer (ViT)

Revisiting the Platonic Representation Hypothesis: An Aristotelian View (2026)measured

After correcting CKA and mutual k-NN alignment for a proven width/depth-dependent null-baseline confound (O(d/n) for global spectral metrics vs O(k/n) for local-neighborhood metrics), the Platonic Representation Hypothesis's global cross-modal convergence largely vanishes while its local-neighborhood convergence survives, across real image-text and video-language pretrained model pairs

Details

Gröger, Wen & Brbić (2026) prove that representational-similarity metrics have a non-vanishing expected value even between statistically independent representations: global spectral metrics like CKA carry an $\mathcal{O}(d_xd_y/n)$ null baseline that grows with representation width (and, via layer-selection, depth), while local-neighborhood metrics like mutual k-NN carry a much smaller $\mathcal{O}(k/n)$ baseline. They introduce a permutation-based null-calibration framework ([[null-calibrated-representational-similarity]]) that estimates and subtracts off this baseline for any similarity metric, then re-run the Platonic Representation Hypothesis's own image-text alignment experiments (following Huh et al. 2024's setup: BLOOMZ, OpenLLaMA, base LLaMA and Gemma-2-9B-it on the language side; AugReg/MAE/DINOv2/ CLIP-LAION2B Vision Transformers on the vision side, paired via Wikipedia image-caption data) plus a new video-language extension (VideoMAE base/large/huge paired with DINOv2/CLIP-LAION2B vision-side encoders and the same language-model family) through this calibration. The result: "the apparent convergence reported by global spectral measures largely disappears after calibration, while local neighborhood similarity... retains significant agreement across different modalities" -- a genuine narrowing of the Platonic Representation Hypothesis's original claim rather than its wholesale refutation, motivating the [[aristotelian-representation-hypothesis]]. The paper's precise correlation coefficients, effect sizes and permutation p-values for each modality pairing sit in Section 6 and Appendix E.8/E.9 of the source, which could not be retrieved despite three separate full-text fetch attempts (direct fetch, plus two dedicated subagents trying arXiv HTML v1/v2, ar5iv, ar5iv.org, a Jina reader mirror, Semantic Scholar, OpenReview, and the paper's own GitHub raw source) -- all hit the same hard MathML-bloat truncation wall before reaching those sections. The qualitative finding quoted above comes from the authors' own official project-page summary (brbiclab.epfl.ch/projects/aristotelian), and the exact model list above was independently confirmed from the paper's own GitHub source code (`aristotelian/prh/prh_models.py`, `video_models.py`), not fabricated -- but no numeric correlation/p-value/effect-size figures are recorded here since none could be honestly sourced.

models: ViT-Base, ViT-Small, MAE ViT-Base (Masked Autoencoder) · method: Null-calibrated representational similarity, Centered Kernel Alignment (CKA), Mutual nearest-neighbor alignment

DINOv2

Revisiting the Platonic Representation Hypothesis: An Aristotelian View (2026)measured

After correcting CKA and mutual k-NN alignment for a proven width/depth-dependent null-baseline confound (O(d/n) for global spectral metrics vs O(k/n) for local-neighborhood metrics), the Platonic Representation Hypothesis's global cross-modal convergence largely vanishes while its local-neighborhood convergence survives, across real image-text and video-language pretrained model pairs

Details

Gröger, Wen & Brbić (2026) prove that representational-similarity metrics have a non-vanishing expected value even between statistically independent representations: global spectral metrics like CKA carry an $\mathcal{O}(d_xd_y/n)$ null baseline that grows with representation width (and, via layer-selection, depth), while local-neighborhood metrics like mutual k-NN carry a much smaller $\mathcal{O}(k/n)$ baseline. They introduce a permutation-based null-calibration framework ([[null-calibrated-representational-similarity]]) that estimates and subtracts off this baseline for any similarity metric, then re-run the Platonic Representation Hypothesis's own image-text alignment experiments (following Huh et al. 2024's setup: BLOOMZ, OpenLLaMA, base LLaMA and Gemma-2-9B-it on the language side; AugReg/MAE/DINOv2/ CLIP-LAION2B Vision Transformers on the vision side, paired via Wikipedia image-caption data) plus a new video-language extension (VideoMAE base/large/huge paired with DINOv2/CLIP-LAION2B vision-side encoders and the same language-model family) through this calibration. The result: "the apparent convergence reported by global spectral measures largely disappears after calibration, while local neighborhood similarity... retains significant agreement across different modalities" -- a genuine narrowing of the Platonic Representation Hypothesis's original claim rather than its wholesale refutation, motivating the [[aristotelian-representation-hypothesis]]. The paper's precise correlation coefficients, effect sizes and permutation p-values for each modality pairing sit in Section 6 and Appendix E.8/E.9 of the source, which could not be retrieved despite three separate full-text fetch attempts (direct fetch, plus two dedicated subagents trying arXiv HTML v1/v2, ar5iv, ar5iv.org, a Jina reader mirror, Semantic Scholar, OpenReview, and the paper's own GitHub raw source) -- all hit the same hard MathML-bloat truncation wall before reaching those sections. The qualitative finding quoted above comes from the authors' own official project-page summary (brbiclab.epfl.ch/projects/aristotelian), and the exact model list above was independently confirmed from the paper's own GitHub source code (`aristotelian/prh/prh_models.py`, `video_models.py`), not fabricated -- but no numeric correlation/p-value/effect-size figures are recorded here since none could be honestly sourced.

models: DINOv2 ViT-B/14, DINOv2 ViT-L/14 · method: Null-calibrated representational similarity, Centered Kernel Alignment (CKA), Mutual nearest-neighbor alignment

CLIP (Contrastive Language-Image Pretraining)

Revisiting the Platonic Representation Hypothesis: An Aristotelian View (2026)measured

After correcting CKA and mutual k-NN alignment for a proven width/depth-dependent null-baseline confound (O(d/n) for global spectral metrics vs O(k/n) for local-neighborhood metrics), the Platonic Representation Hypothesis's global cross-modal convergence largely vanishes while its local-neighborhood convergence survives, across real image-text and video-language pretrained model pairs

Details

Gröger, Wen & Brbić (2026) prove that representational-similarity metrics have a non-vanishing expected value even between statistically independent representations: global spectral metrics like CKA carry an $\mathcal{O}(d_xd_y/n)$ null baseline that grows with representation width (and, via layer-selection, depth), while local-neighborhood metrics like mutual k-NN carry a much smaller $\mathcal{O}(k/n)$ baseline. They introduce a permutation-based null-calibration framework ([[null-calibrated-representational-similarity]]) that estimates and subtracts off this baseline for any similarity metric, then re-run the Platonic Representation Hypothesis's own image-text alignment experiments (following Huh et al. 2024's setup: BLOOMZ, OpenLLaMA, base LLaMA and Gemma-2-9B-it on the language side; AugReg/MAE/DINOv2/ CLIP-LAION2B Vision Transformers on the vision side, paired via Wikipedia image-caption data) plus a new video-language extension (VideoMAE base/large/huge paired with DINOv2/CLIP-LAION2B vision-side encoders and the same language-model family) through this calibration. The result: "the apparent convergence reported by global spectral measures largely disappears after calibration, while local neighborhood similarity... retains significant agreement across different modalities" -- a genuine narrowing of the Platonic Representation Hypothesis's original claim rather than its wholesale refutation, motivating the [[aristotelian-representation-hypothesis]]. The paper's precise correlation coefficients, effect sizes and permutation p-values for each modality pairing sit in Section 6 and Appendix E.8/E.9 of the source, which could not be retrieved despite three separate full-text fetch attempts (direct fetch, plus two dedicated subagents trying arXiv HTML v1/v2, ar5iv, ar5iv.org, a Jina reader mirror, Semantic Scholar, OpenReview, and the paper's own GitHub raw source) -- all hit the same hard MathML-bloat truncation wall before reaching those sections. The qualitative finding quoted above comes from the authors' own official project-page summary (brbiclab.epfl.ch/projects/aristotelian), and the exact model list above was independently confirmed from the paper's own GitHub source code (`aristotelian/prh/prh_models.py`, `video_models.py`), not fabricated -- but no numeric correlation/p-value/effect-size figures are recorded here since none could be honestly sourced.

models: CLIP ViT-B/16, CLIP ViT-L/14 · method: Null-calibrated representational similarity, Centered Kernel Alignment (CKA), Mutual nearest-neighbor alignment

VideoMAE

Revisiting the Platonic Representation Hypothesis: An Aristotelian View (2026)measured

After correcting CKA and mutual k-NN alignment for a proven width/depth-dependent null-baseline confound (O(d/n) for global spectral metrics vs O(k/n) for local-neighborhood metrics), the Platonic Representation Hypothesis's global cross-modal convergence largely vanishes while its local-neighborhood convergence survives, across real image-text and video-language pretrained model pairs

Details

Gröger, Wen & Brbić (2026) prove that representational-similarity metrics have a non-vanishing expected value even between statistically independent representations: global spectral metrics like CKA carry an $\mathcal{O}(d_xd_y/n)$ null baseline that grows with representation width (and, via layer-selection, depth), while local-neighborhood metrics like mutual k-NN carry a much smaller $\mathcal{O}(k/n)$ baseline. They introduce a permutation-based null-calibration framework ([[null-calibrated-representational-similarity]]) that estimates and subtracts off this baseline for any similarity metric, then re-run the Platonic Representation Hypothesis's own image-text alignment experiments (following Huh et al. 2024's setup: BLOOMZ, OpenLLaMA, base LLaMA and Gemma-2-9B-it on the language side; AugReg/MAE/DINOv2/ CLIP-LAION2B Vision Transformers on the vision side, paired via Wikipedia image-caption data) plus a new video-language extension (VideoMAE base/large/huge paired with DINOv2/CLIP-LAION2B vision-side encoders and the same language-model family) through this calibration. The result: "the apparent convergence reported by global spectral measures largely disappears after calibration, while local neighborhood similarity... retains significant agreement across different modalities" -- a genuine narrowing of the Platonic Representation Hypothesis's original claim rather than its wholesale refutation, motivating the [[aristotelian-representation-hypothesis]]. The paper's precise correlation coefficients, effect sizes and permutation p-values for each modality pairing sit in Section 6 and Appendix E.8/E.9 of the source, which could not be retrieved despite three separate full-text fetch attempts (direct fetch, plus two dedicated subagents trying arXiv HTML v1/v2, ar5iv, ar5iv.org, a Jina reader mirror, Semantic Scholar, OpenReview, and the paper's own GitHub raw source) -- all hit the same hard MathML-bloat truncation wall before reaching those sections. The qualitative finding quoted above comes from the authors' own official project-page summary (brbiclab.epfl.ch/projects/aristotelian), and the exact model list above was independently confirmed from the paper's own GitHub source code (`aristotelian/prh/prh_models.py`, `video_models.py`), not fabricated -- but no numeric correlation/p-value/effect-size figures are recorded here since none could be honestly sourced.

models: VideoMAE-base, VideoMAE-v2 · method: Null-calibrated representational similarity, Centered Kernel Alignment (CKA), Mutual nearest-neighbor alignment