RoboTTT scales robot policy context to 8K timesteps, three orders of magnitude past state of the art.

arXiv 2607.15275, submitted July 16 by a Stanford/NVIDIA team including Li Fei-Fei, Yuke Zhu, and Jim Fan. It uses fast weights updated by gradient descent at inference to compress visuomotor history, plus sequence action forcing and truncated backprop through time. 87% improvement over a single-step baseline on real-robot manipulation, and it completed a five-minute, ten-stage assembly task no baseline finished. 8K-context models beat 1K-context by 62%. Test-time training as a memory mechanism, applied to a domain where nobody had the context budget before.