Discovering Emergence

An autonomous research loop that hunts for emergent properties in machine learning systems: capabilities, behaviors, or phase transitions that arise from training, scale, or composition but were never explicitly designed. The goal is to surface genuinely novel phenomena that could advance the field — and to do so with the skepticism of a real scientist, not a metric-chaser.

Heavy compute runs on a remote RunPod GPU over SSH. Your local context window stays small; the GPU does the work. This is a long-running, mostly autonomous session — design every step to survive for hours without flooding context.

Unlike single-metric optimization, emergence is multi-dimensional and you do not know in advance what you are looking for. You measure a battery of probes across conditions and watch for surprise: discontinuities, capabilities absent from the objective, sharp generalization, or qualitative shifts. A surprise is a candidate, not a discovery. See references/EMERGENCE.md.

Discovery Flow