How close are we to real AI reasoning?
Q & A
Peter Hess
Knowledge and Reasoning
For AI to be useful, it needs to understand what’s being asked of it, and to know how to respond. At IBM Research, we’re working on systems to help AI better reason with the tasks it’s presented, such as understanding context and analogies, comprehension, and planning through scenarios.
Our work
AI, you have a lot of explaining to do
ReleaseDinesh Garg, Parag Singla, Dinesh Khandelwal, Shourya Aggarwal, Divyanshu Mandowara, and Vishwajeet Agrawal5 minute readIBM, MIT and Harvard release “Common Sense AI” dataset at ICML 2021
ReleaseDan Gutfreund, Abhishek Bhandwaldar, and Chuang Gan6 minute readIBM’s new AI outperforms competition in table entry search with question-answering
ReleaseAlfio Gliozzo, Michael Glass, and Mustafa Canim7 minute readGetting AI to reason: using neuro-symbolic AI for knowledge-based question answering
ResearchSalim Roukos, Alexander Gray, and Pavan Kapanipathi7 minute readDualTKB: A Dual Learning Bridge between Text and Knowledge Base
ResearchPierre Dognin6 minute read
Publications
Learnable Channel Converter for Multi-Spectral Image to RGB Visualization using a Vision-Text Model
- Haoxiang Qiu
- Tomoya Sakai
- et al.
- 2025
- IGARSS 2025
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning
- Zheyuan Zhang
- Yiyang Li
- et al.
- 2025
- ACL 2025
Conceptual Diagnostics for Knowledge Graphs and Large Language Models
- Rosario Uceda-Sosa
- Maria Chang
- et al.
- 2025
- ACL 2025
Learning interpretable positional encodings in transformers depends on initialization
- Taku Ito
- Luca Cocchi
- et al.
- 2025
- ICML 2025
Rethinking Unlearning for Large Reasoning Models Download PDF
- Changsheng Wang
- Chongyu Fan
- et al.
- 2025
- ICML 2025
Wafer Defect Root Cause Analysis with Partial Trajectory Regression
- Kohei Miyaguchi
- Masao Joko
- et al.
- 2025
- ASMC 2025