IEEE IRI 2026 was a great experience, hosted this past weekend at UW Bothell. AI was a central theme throughout, most visibly in the keynotes from Bhavani Thuraisingham, Roger Barga, and UW’s John Y. Choe. The program ran two parallel tracks across sixteen sessions plus two workshops, but a few threads showed up consistently. 

AI as a change in how we work, not a replacement for us. This came up often during the keynotes, with what felt like real consensus behind it. The interesting question has shifted from whether AI displaces people to what the reshaped workflow looks like. LLMs, LMMs, and agents dominated much of the research. Many researchers followed a similar pattern: zero-shot evaluations, pre-trained models as feature extractors, large models pointed at a specific problem to see if they’d solve it. Agents were also a consistent theme. For example, we saw MCP-based incident detection, multi-agent recommendation, and RAG got a full session. It was a good overview of what is possible, with interesting use cases and some well documented failure cases. 

Explainability and efficiency also got a lot of attention, but through general-purpose methods. Efficiency leaned on split and federated learning and modality compression, while edge deployment appeared in a few places. The interest is clear, but the specialization to deployment context isn’t yet well characterized. 

Healthcare was another big theme, and the multi-modal work was more heavily emphasized in these scenarios as opposed to explainability. Proteomics with imaging for Alzheimer’s, cross-modal fusion for fundoscopy, modality gating for glioma segmentation, all combining signal types rather than optimizing one, generally with late fusion techniques. 

Overall, the conference was an amazing opportunity and experience to connect with other researchers and share our LI-VAD paper. We fielded great questions about our video artifact detection dataset and its release.