Scaling world understanding for autonomous systems without equivalent cost scaling 2026-06-25 By: Jason Liu, Chao Zhang, Wolf Arnold, Niel Hu The transition from digital AI to physical AI demands a fundamental shift in how machines interact with their environments. While digital AI often operates within neatly structured data or constrained virtual spaces, physical AI must navigate a chaotic, unstructured, and endlessly dynamic real world where the most critical safety events are often rare, long-tail edge cases. For General Motors, the challenge goes beyond making the right decision in the moment. It also means building the data and validation engine needed to improve autonomous systems safely over time. Mining long-tail scenarios is essential for risk assessment, test creation, validation, and training, since the scenarios that matter most tend to surface only rarely across massive volumes of fleet data. For example, GM operates a fleet of undreds of test vehicles, which have generated millions of miles of high-quality data captured across multiple sensor and camera streams. On ingestion, this data flows through a pipeline that automatically mines for scenarios of interest, such as near-miss collisions, ambulances on the road, and hard braking events. As we expand our operations, we regularly identify new aspects to mine for, whether a specific form of dangerous debris or a previously unobserved type of road construction equipment. To improve safely, autonomous systems must convert continuous streams of raw fleet data into a structured, actionable understanding of the driving environment, a monumental perception and interpretation challenge that recent advances in MLLMs are just beginning to address. These models are expanding how machines can understand and reason about the physical world. While split-second decision-making happens onboard at the edge, the true bottleneck for continuous learning lies in offboard infrastructure that must process vast amounts of fleet data. In this