Unleashing the Power of Foundation Models: From 3D Vision to Medical AI and Beyond
Latest 80 papers on foundation models: Feb. 7, 2026
Foundation Models (FMs) are rapidly transforming the AI landscape, demonstrating unprecedented capabilities across diverse domains. These large, pre-trained models are not just pushing performance benchmarks; they’re fundamentally reshaping how we approach complex problems in fields ranging from computer vision and robotics to healthcare and climate science. Recent research showcases remarkable breakthroughs, addressing challenges in efficiency, generalization, interpretability, and real-world applicability.
The Big Idea(s) & Core Innovations
At the heart of these advancements is a drive to imbue FMs with deeper contextual understanding, whether it’s geometric, semantic, or temporal. In computer vision, a key theme is enhancing 3D awareness and scene understanding. For instance, ShapeUP, a framework from researchers including Inbar Gat of Aigency.ai and Tel Aviv University, enables Scalable Image-Conditioned 3D Editing by leveraging native 3D representations and a synthetic dataset, DFM, to preserve identity during global edits. This is complemented by Splat and Distill from the Department of Computer Science, The Hebrew University of Jerusalem, which introduces Augmenting Teachers with Feed-Forward 3D Reconstruction For 3D-Aware Distillation to improve 2D Vision Foundation Models (VFMs) with fast 3D Gaussian representations, bypassing per-scene optimization. Further pushing 3D capabilities, SeeingThroughClutter by Rio Aguina-Kang and colleagues at the University of California, San Diego and Adobe Research, proposes Structured 3D Scene Reconstruction via Iterative Object Removal using VLMs to reconstruct complex scenes from single images by iteratively segmenting and removing objects.
Another significant trend is improving model efficiency and robustness. In natural language processing, CSRv2 by Lixuan Guo et al. from Stony Brook University and MIT CSAIL, tackles Unlocking Ultra-Sparse Embeddings, achieving comparable performance to dense models with significantly fewer active features. For audio, Bagpiper by Jinchuan Tian from Carnegie Mellon University and LY Corporation, is Solving Open-Ended Audio Tasks via Rich Captions by reformulating audio tasks as text-reasoning problems, enabling flexible and general-purpose audio intelligence. Meanwhile, in reinforcement learning, Constrained Group Relative Policy Optimization by Roger Girgis et al. from Mila and École Polytechnique de Montréal, introduces Constrained Group Relative Policy Optimization, a Lagrangian-based extension of GRPO, for stable constraint satisfaction in embodied AI like autonomous driving.
Across medical AI, there’s a strong focus on self-supervised learning and multimodal integration. OmniRad, from the University of Cagliari, Italy, proposes A Radiological Foundation Model for Multi-Task Medical Image Analysis, leveraging 1.2 million medical images for task-agnostic representation. Similarly, EchoJEPA from University Health Network and Vector Institute, presents A Latent Predictive Foundation Model for Echocardiography, trained on 18 million videos to improve diagnostic consistency and reduce annotation burden. In pathology, iSight by Jacob S. Leiby et al. from the University of Pennsylvania, introduces expert-AI co-assessment for improved immunohistochemistry staining interpretation using the massive HPA10M dataset. Furthermore, Cell-JEPA from Carnegie Mellon University, pioneers Latent Representation Learning for Single-Cell Transcriptomics to learn robust representations from sparse gene expression data.
Under the Hood: Models, Datasets, & Benchmarks
These innovations are powered by novel architectures, vast datasets, and rigorous benchmarking:
- Models:
- ERNIE 5.0 (ERNIE 5.0 Technical Report) from Baidu: A trillion-parameter unified autoregressive model with an ultra-sparse Mixture-of-Experts (MoE) architecture and elastic training for multimodal understanding and generation.
- GraphBFF (Billion-Scale Graph Foundation Models) by Bronstein et al. (University of Edinburgh, Google Research, DeepMind): The first end-to-end framework for billion-scale Graph Foundation Models (GFMs) on heterogeneous graphs, introducing the GraphBFF Transformer with two attention components and sparse softmax.
- HORAI (Empowering Time Series Analysis with Large-Scale Multimodal Pretraining) from East China Normal University and HuaWei: A frequency-enhanced multimodal foundation model for time series analysis, integrating text, image, and news.
- UniSurg (UniSurg: A Video-Native Foundation Model for Universal Understanding of Surgical Videos) by Jinlin Wu et al. (Chinese Academy of Sciences, Technical University of Munich): A video-native foundation model prioritizing motion prediction for surgical video understanding.
- GAMformer (GAMformer: Bridging Tabular Foundation Models and Interpretable Machine Learning) from Microsoft Research and University of Freiburg: The first tabular foundation model for Generalized Additive Models (GAMs), enabling interpretable shape functions.
- OUTFORMER (From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection) from Carnegie Mellon University: A zero-shot tabular outlier detection model using synthetic priors and self-evolving curriculum training.
- OpticalDNA (Rethinking Genomic Modeling Through Optical Character Recognition) from Hunan University: A vision-based DNA foundation model that reframes genomic modeling as an OCR-style document understanding problem.
- WIND (WIND: Weather Inverse Diffusion for Zero-Shot Atmospheric Modeling) from Technical University of Munich and JKU Linz: A pre-trained diffusion-based model for zero-shot atmospheric modeling, capable of generating physically consistent counterfactuals.
- Datasets:
- UniSurg-15M: Largest surgical video dataset (3,658 hours) for self-supervised pretraining of surgical understanding models.
- MM-TS: The first large-scale multimodal time series dataset (up to one billion points) spanning six domains for multimodal time series analysis.
- SOMA-1M (SOMA-1M: A Large-Scale SAR-Optical Multi-resolution Alignment Dataset for Multi-Task Remote Sensing) from Wuhan University: A million-scale SAR-optical multi-resolution dataset for multi-task remote sensing.
- HPA10M (iSight: Towards expert-AI co-assessment for improved immunohistochemistry staining interpretation) from University of Pennsylvania: Over 10 million IHC images with comprehensive annotations for AI-assisted pathology.
- GIQ (GIQ: Benchmarking 3D Geometric Reasoning of Vision Foundation Models with Simulated and Real Polyhedra) by Mateusz Michalkiewicz et al. (Rice University): A benchmark for 3D geometric reasoning, revealing shortcomings in current vision-language models.
- SynthVerse (SynthVerse: A Large-Scale Diverse Synthetic Dataset for Point Tracking) by Weiguang Zhao et al. (University of Liverpool, Zhejiang University): A large-scale synthetic dataset for general point tracking, emphasizing diversity and robustness.
- SELVAMASK (SelvaMask: Segmenting Trees in Tropical Forests and Beyond) from Université de Montréal and Mila: The largest open tropical crown delineation dataset with high-resolution imagery and dense annotations.
- OmniCellTOSG (OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Graph Language Foundation Modeling) from Washington University in St. Louis: Integrates biomedical textual knowledge with omic data and signaling networks for multimodal graph language modeling.
Impact & The Road Ahead
The impact of these advancements is profound. From enhancing the safety and generalizability of embodied AI agents with GeneralVLA (GeneralVLA: Generalizable Vision-Language-Action Models with Knowledge-Guided Trajectory Planning) and LIEREx (LIEREx: Language-Image Embeddings for Robotic Exploration), to making complex scientific computing more accessible with OpInf-LLM (OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference), foundation models are expanding their reach and capabilities. The push for interpretable models like GAMformer and KernelICL (Interpretable Tabular Foundation Models via In-Context Kernel Regression) is crucial for adoption in high-stakes domains like healthcare, where LegalOne (LegalOne: A Family of Foundation Models for Reliable Legal Reasoning) is demonstrating reliable legal reasoning.
However, challenges remain. As highlighted by **
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