Tag: agents

DAPLab affiliates have a strong presence at Forty-Third International Conference on Machine Learning (ICML 2026), in Seoul, South Korea. With papers across the main conference, posters, and workshops that spans agents, data systems, reliable AI, human-AI interaction, digital twins, model communication, and uncertainty.

The work reflects a core theme of the lab: building the data, systems, and interaction foundations needed for AI systems that can operate reliably in messy, real-world settings. This includes new benchmarks for exploratory question answering over million-scale data lakes, live kernel crash repair, adaptive querying, digital twin simulation, confidence calibration, and model-to-model communication without text.

Together, these papers show how DAPLab research connects machine learning advances to the infrastructure required to use AI safely and effectively: better environments for evaluating agents, better methods for eliciting and adapting to human or AI-generated information, better abstractions for cross-model communication, and better tools for understanding uncertainty.

Find the links to the papers here.