IBM’s newest time-series models cover a full range of enterprise prediction tasks
Technical note
Pankaj Dayama, Vijay Ekambaram, Wesley Gifford, Lars Graf, Thomas Ortner, Angeliki Pantazi, Chandra Reddy, and Roman Vaculin
Granite
The future of AI is open source. At IBM, we are building state-of-the-art generative models for language, code, time-series, materials discovery, and more and making them available to the community.
Our work
Turning turbulence into transcripts
NewsMike MurphyIBM Granite tops Stanford’s list as the world’s most transparent model
NewsPeter HessAll decisions have trade-offs. IBM’s Wei Sun is an expert at weighing them
Q & AKim MartineauWhat’s next in computing is generative and quantum
NewsPeter HessIBM Granite now has eyes
ResearchKim Martineau- See more of our work on Granite
Publications
SatWellMCQ: A Vision–Language Satellite Dataset for MCQ‑Based Image Grounding of Oil Wells
- Ahmed Emam
- Sultan Alrowili
- et al.
- 2026
- IGARSS 2026
Conference paperContext-Aware Visual Multi-Turn Conversation Generation from Wikipedia and Wikidata
- Basel Shbita
- Pengyuan Li
- et al.
- 2026
- ESWC 2026
Conference paperAdaptive Decoding via Test-Time Policy Learning for Self-Improving Generation
- Asmita Bhardwaj
- Yuya Ong
- et al.
- 2026
- ICLR 2026
Workshop paperEvaluating Ill-Defined Tasks in Large Language Models
- 2026
- ICLR 2026
Workshop paperCross-modal fusion of protein language models, large language models, and 3D structural models for unified protein representations
- Ben Shapira
- Michal Ozery-Flato
- et al.
- 2026
- ACS Spring 2026
PosterReducing the Scope of Language Models
- David Yunis
- siyu huo
- et al.
- 2026
- AAAI 2026
Conference paper
Tools + code
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