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
- A more fluid way to model time-series dataResearchKim Martineau
- From simulated steps to real-world care: AI learns how we walk for neurologyResearchPeter Hess
- Revolutionizing industries with AI-powered digital twinsCase studyPeter Hess
- Simplifying geospatial AI with TerraTorch 1.0Technical noteRomeo Kienzler, Juan Bernabé-Moreno, Paolo Fraccaro, Bianca Zadrozny, Campbell Watson, Benedikt Blumenstiel, Joao Lucas de Sousa Almeida, Michael Johnston, Christian Pinto, and Michal Muszynski
- Meet IBM’s new family of AI models for materials discoveryNewsKim Martineau
- See more of our work on Foundation Models
Publications
- FlowState: Sampling-Rate Invariant Time Series Foundation Model with Dynamic Forecasting Horizons- Lars Graf
- Thomas Bohnstingl
- et al.
 
- 2025
- NeurIPS 2025
 
- MESS+: Dynamically Learned Inference-Time LLM Routing in Model Zoos with Service Level Guarantees- Herbert Woisetschläger
- Ryan Zhang
- et al.
 
- 2025
- NeurIPS 2025
 
- Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring- Shuxin Lin
- Dhaval Patel
- et al.
 
- 2025
- EMNLP 2025
 
- FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models- Radu Marinescu
- Debarun Bhattacharjya
- et al.
 
- 2025
- EMNLP 2025
 
- SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models- Debarun Bhattacharjya
- Balaji Ganesan
- et al.
 
- 2025
- EMNLP 2025
 
- Synthetic Data for Evaluation: Supporting LLM-as-a-Judge Workflows with EvalAssist- 2025
- EMNLP 2025
 
Tools + code
- Download Granite on Hugging Face- Explore our family of language, code, time series, and geospatial models. Download models
- Try Granite for Free- Chat with a Granite model and learn how it can be used across a variety of applications. View project
- Read Granite Documentation- Learn how to access, run, and start using the Granite family of AI models. View project