arXiv Machine Learning June 9, 2026 By Alonso Urbano, David W. Romero, Max Zimmer, Sebastian Pokutta

Neural Field Tokenizations with Hierarchy and Spatial Locality Priors

Read the original on arXiv Machine Learning →

arXiv:2606. 08204v1 Announce Type: new Abstract: Neural fields parameterize data as functions from coordinates to values, providing a unified framework for representation learning across modalities.

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arXiv Machine Learning

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Zero-shot generalization of transformer neural operators to larger domains

arXiv:2606. 14597v1 Announce Type: new Abstract: Transformer-based neural operators have shown remarkable performance for approximating solution operators of partial differential equations on complex geometries.

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llms rag benchmarks safety

arXiv AI

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JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures

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By Ariel Larey, Elay Dahan, Amit Bleiweiss, Raizy Kellerman, Guy Leib, Omri Nayshool, Dan Ofer, Tal Zinger, Dan Dominissini, Gideon Rechavi, Nicole Bussola, Simon Lee, Shane O'Connell, Dung Hoang, Marissa Wirth, Alexander W. Charney, Nati Daniel, Yoli Shavit

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TerraMind: Large-Scale Generative Multimodality for Earth Observation

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fine-tuning multimodal benchmarks

arXiv AI

Jun 10

Time Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models

arXiv:2606. 09861v1 Announce Type: cross Abstract: While Next-Token Prediction (NTP) has unified LLM pretraining, its adaptation to unbounded, continuous time series (TS) remains open.

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arXiv Machine Learning

Jun 19

Multi-Modal Contrastive Learning for Implicit Earth Embeddings via Location Tying

arXiv:2606. 20167v1 Announce Type: new Abstract: Spatial prediction tasks are often limited by a lack of high-quality labelled ground-truth observations.

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rag multimodal

arXiv AI

Jul 2

MEPA: Multi-Scale Representation Alignment for Visual Autoregressive Modeling with Mixture of Experts

arXiv:2607. 00371v1 Announce Type: cross Abstract: Visual AutoRegressive modeling (VAR) has pioneered a coarse-to-fine multi-scale autoregressive generative paradigm, demonstrating strong capabilities in image generation.

By Nuoyan Zhou, Zhijun Tu, Lei Yu, Kun Cheng, Jie Hu, Nannan Wang, Xinghao Chen

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