Foundation Models
Foundation models can be applied across domains and tasks. But there are challenges to scalability, and how AI is applied in specific use cases. At IBM Research, we create new foundation models for business, integrating deep domain expertise, a focus on responsible AI, and a commitment to open-source innovation.
Overview
Modern AI models can learn from millions of examples to help find new solutions to difficult problems. But building new systems tends to take time — and lots of data. The next wave in AI will replace task-specific models with ones that are trained on a broad set of unlabeled data that can be used for different tasks — with minimal fine-tuning. These are called foundation models. They can be the foundation for many applications of the AI model. Using self-supervised learning and fine-tuning, the model can apply information it has learned in general to a specific task.
We believe that foundation models will dramatically accelerate AI adoption in business. Reducing time spent labeling data and programming models will make it much easier for businesses to dive in, allowing more companies to deploy AI in a wider range of mission-critical situations. Our goal is to bring the power of foundation models to every enterprise in a frictionless hybrid-cloud environment.
Our work
- ReleaseMike Murphy
Next-generation algorithms could move fusion from the lab to the grid
ReleaseKim MartineauIBM’s newest time-series models cover a full range of enterprise prediction tasks
Technical notePankaj Dayama, Vijay Ekambaram, Wesley Gifford, Lars Graf, Thomas Ortner, Angeliki Pantazi, Chandra Reddy, and Roman VaculinAI-powered satellites will upend how we observe our changing planet
ReleaseMike MurphyA more fluid way to model time-series data
ResearchKim MartineauFrom simulated steps to real-world care: AI learns how we walk for neurology
ResearchPeter Hess- See more of our work on Foundation Models
Publications
How Can Mamba Learn In Context with Outliers and Generalize Provably?
- Hongkang Li
- Songtao Lu
- et al.
- 2026
- ICML 2026
Conference paperPosition: Agentic Systems Should be General
- Elron Bandel
- Asaf Yehudai
- et al.
- 2026
- ICML 2026
Conference paperHow to Embed Matters: Evaluation of EO Embedding Design Choices
- Luis Gilch
- Isabelle Wittmann
- et al.
- 2026
- CVPR 2026
Workshop paperMarkdown Mayhem : Taming the Agentic Documentation Explosion
- 2026
- ACM CAIS 2026
Workshop paperDialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models
- Malik Altakrori
- Nizar Habash
- et al.
- 2026
- LREC 2026
Conference paperSparse Gradient Compression for Fine-Tuning Large Language Models
- David H. Yang
- Mohammad Mohammadi Amiri
- et al.
- 2026
- ICASSP 2026
Conference paper
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