On-Device Intelligence: Foundation Models under Real-World Constraints

Intelligence should not rely solely on the cloud. As foundation models enter real-world interactive and embodied settings, their cloud-centric capabilities must be realized on devices under constraints on latency, memory, reliability and other factors. From LLMs to embodied foundation models, the key question is no longer only how to improve capability, but how to make it usable for efficient on-device inference and reasoning, stable interaction, continual adaptation, and safe execution. On-device intelligence is therefore not merely model compression or deployment, but a tightly coupled research problem spanning theory, algorithms, systems, and evaluation, where capability must be considered jointly with efficiency, adaptation, execution, and reliability under real-world constraints. This workshop will bring together researchers working on LLMs, embodied intelligence, and edge systems to clarify this shared problem space and identify key questions through invited talks, panels, contributed posters, and open discussions, helping connect separated communities and shape a shared research agenda for on-device intelligence.