Measure Twice, Deploy Once: The Hidden Geometry of Reliable AI
TL;DR for operators The practical problem is not that AI systems lack benchmarks. We are drowning in benchmarks. The problem is that many benchmarks, design scores, and demo metrics politely avoid the failure modes that will later become incident reports, refund requests, clinical risk reviews, or broken robots wedged under furniture. Two recent papers make the same point from very different directions. One studies Argus, a spherical, many-legged robot designed around dynamic isotropy: the uniformity of attainable center-of-mass acceleration across directions.1 The other reworks panoptic segmentation evaluation by replacing a fixed one-to-one segment matching rule with a configurable assignment framework that can handle fragmentation, merging, thresholds, Voronoi regions, and part-aware targets.2 ...