arXiv Machine Learning
Native Geometry vs. Linear Recoverability: How Alignment Changes Platonic Scaling
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Multi-Way Representation Alignment
arXiv:2602. 06205v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that independently trained neural networks converge to increasingly similar latent spaces.
arXiv Machine Learning
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What Converges in the Platonic Representation Hypothesis? Structure over Geometry
The paper investigates the Platonic Representation Hypothesis, which posits that more capable models converge toward shared representations. By distinguishing relational structure (which samples are related) from metric geometry (quantitative relations like distances), the authors develop a controlled $2 imes2$ framework to evaluate both aspects at local and global scales. Their findings show that relational structure consistently converges across vision‑language and video‑text models, while metric geometry converges much more weakly, a pattern that persists even when using a Riemannian metric approximation.
arXiv AI
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Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale
arXiv:2604. 18572v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that neural networks trained on different modalities (e.
arXiv Machine Learning
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Geometric Stability: The Missing Axis of Representations
arXiv:2601. 09173v5 Announce Type: replace Abstract: Representational similarity analysis and related methods compare the internal geometries of neural networks, but they measure only alignment between spaces, leaving a blind spot -- whether a representation's structure is reliably recoverable, not merely similar.
arXiv Computer Vision
Oct 1
Spherical Interpolation for Backward-Compatible Multimodal Representations
arXiv:2609.39836v1 Announce Type: new Abstract: Contrastive vision-language models map visual and textual representations into a shared normalized embedding space, making cosine similarity the natura...
arXiv Machine Learning
Jun 16
Size Doesn't Matter: Cosine-Scored Sparse Autoencoders
arXiv:2606. 15054v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) detect features via inner product, so a feature's activation scales with both its directional alignment and the input's norm.