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  1. MIMIC: A Generative Multimodal Foundation Model for Biomolecules

    Publication
    We introduce MIMIC a generative multimodal foundation model trained across biomolecular sequences 3D structures and functional assays. MIMIC performs unified representation learning and conditional generation across nucleic acids …

  2. Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme

    Publication
    Conditional diffusion models provide a natural framework for probabilistic prediction of dynamical systems and have been successfully applied to fluid dynamics and weather prediction. However, in many settings …

  3. Walrus: A Cross-Domain Foundation Model for Continuum Dynamics

    Publication
    Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unstable long-term dynamics inhibit …

  4. Walrus: A Cross-Domain Foundation Model for Continuum Dynamics

    Publication
    Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unstable long-term dynamics inhibit …

  5. Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning

    Publication
    Sequential scientific data span many resolutions and domains, and unifying them into a common representation is a key step toward developing foundation models for the sciences. Astronomical spectra …

  6. AION-1: Omnimodal Foundation Model for Astronomical Sciences

    Publication
    While foundation models have shown promise across a variety of fields, astronomy still lacks a unified framework for joint modeling across its highly diverse data modalities. In this …

  7. Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme

    Publication
    Conditional diffusion models provide a natural framework for probabilistic prediction of dynamical systems and have been successfully applied to fluid dynamics and weather prediction. However, in many settings …

  8. AstroCLIP: a cross-modal foundation model for galaxies

    Publication
    We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used …

  9. Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task

    Publication
    Transformers have revolutionized machine learning across diverse domains, yet understanding their behavior remains crucial, particularly in high-stakes applications. This paper introduces the contextual counting task, a novel …

  10. Multiple Physics Pretraining for Spatiotemporal Surrogate Models

    Publication
    We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling of spatiotemporal systems with transformers. In MPP, rather than training one model …