arXiv:2606. 30625v1 Announce Type: cross Abstract: Contrastive embedding models trained with scale-invariant losses are typically paired with distance metrics like cosine similarity, effectively ignoring embedding magnitudes.
By Ziwei Su, Junyu Ren, Victor Veitch
arXiv:2609.05721v1 Announce Type: new
Abstract: Understanding whether language-model embeddings encode structured real-world information is important for both representation analysis and information...
By Esteban Feuerstein, Victoria Klimkowski, Juan Manuel Ortiz de Zarate, Federico Hern\'an Suaiter
arXiv:2606. 28330v1 Announce Type: cross Abstract: Embedding-based retrieval systems rely on the assumption that geometric proximity in highdimensional representation spaces reflects semantic relevance.
By Ernesto Lopez Fune (DE)
arXiv:2602. 15029v3 Announce Type: replace Abstract: The internal representations learned by language models consistently exhibit striking geometric structure: calendar months organize into a circle, historical years form a smooth one-dimensional manifold, and cities' latitudes and longitudes can be decoded using a linear probe.
By Dhruva Karkada, Daniel J. Korchinski, Andres Nava, Matthieu Wyart, Yasaman Bahri
arXiv:2608.28840v1 Announce Type: new
Abstract: Independently trained neural networks tend to encode the same data with similar latent geometries. These latent geometries are not directly compatible,...
By Cameron Ryan, Vivek Sivaraman Narayanaswamy, Kowshik Thopalli, Shusen Liu
We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use.