OlmoEarth v1.3: State-of-the-art embeddings for the whole planet

New and improved embedding model

We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use.

Getting Started With Embeddings

Gestalt: a meta-foundation model for astronomy

arXiv:2609.38312v1 Announce Type: cross Abstract: The Platonic Representation Hypothesis predicts that sufficiently scaled foundation models converge on a shared representation of the world. As each...

OrBIT: Structure-Guided Embedding Compression

OrBIT is a structure‑guided embedding compression framework that learns reusable local geometry from orbit dynamics to constrain a small set of shared codewords. It allocates a fixed coding budget based on a global reconstruction residual, allowing redundant overlapping charts to compensate local errors after gluing. Across four large language model embedding tables, OrBIT achieves 37.9× compression on GPT‑2 and over 23× on each 7B table compared to 16‑bit storage, while maintaining competitive rate‑distortion performance against existing quantization and low‑rank methods.

Welcome EmbeddingGemma, Google's new efficient embedding model

COMPLEX: A Closed-Form Certified Embedding of Multiparameter Persistence Modules

COMPLEX is a closed‑form, training‑free embedding for multiparameter persistence modules that provides both an upper and a lower Lipschitz bound, enabling faithful feature representations. By slicing modules along a near‑diagonal net and embedding each slice with the certified PLACE/PALACE landmark map, the method guarantees that separated modules remain separated in the embedding. On Orbit benchmarks and molecular graph tasks, COMPLEX achieves state‑of‑the‑art accuracy, outperforming existing landmark, transformer, and graph‑based approaches.