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.

Towards Consistent Geospatial Representations: Aligning One Encoder to Many Foundation Models

The paper introduces Pangaea-Latents, an extension of the Pangaea benchmark that provides frozen embeddings from five geospatial foundation models (GFMs) across eleven datasets, showing that these embeddings are neither redundant nor disjoint. It then proposes GeoMixture, a framework that aligns a Vision Transformer (ViT) to multiple GFMs per dataset using a multi-representation alignment (mREPA) objective, resulting in an encoder that achieves the best average rank across nine Pangaea datasets while maintaining cross-task consistency.

Dimensionality reduction for homological stability and global structure preservation

arXiv:2503. 03156v4 Announce Type: replace-cross Abstract: We propose DiRe, a force-directed dimensionality reduction framework designed to preserve global structure and homological features while remaining practical on modern hardware.

Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Exploration

arXiv:2605. 00972v2 Announce Type: replace-cross Abstract: Earth system science is producing increasingly large, high-dimensional datasets from both physics-based and AI-driven models.

New embedding models and API updates

Omni-Embed-Mini: Binding Modalities Without Forgetting via Dense Distillation

Omni-Embed-Mini is a 0.9B‑parameter model that embeds text, speech, audio, images, video, and visually‑rich documents into a single shared cosine space without updating any text‑side parameters. It uses a dense cascaded caption as a teacher signal, allowing the teacher and student to share identical backbone weights and requiring only lightweight projectors and phased LoRA adapters for alignment. The model achieves strong text retrieval performance (49.57 nDCG@10 on MTEB‑v2 BEIR‑8) while extending to five additional modalities and is significantly smaller than other open omni‑modal embedders.

Cluster and then Embed: A Modular Approach for Visualization

arXiv:2509. 03373v2 Announce Type: replace Abstract: Dimensionality reduction methods such as t-SNE and UMAP are popular methods for visualizing data with a potential (latent) clustered structure.