The work in the order it happened, one post per phase: contributions, experiments and negative results. Each number links back to the experiment log; nothing was rewritten after the fact.
ContactGuard (Zheng, Johnson-Roberson, Zhi; arXiv 2608.13438) is the closest paper to what I run: a LeWM-style world model used as a pre-contact failure monitor on a real arm. Their monitor works. Mine, built to the same protocol, does not, with the opposite sign.
D-JEPA (Liu, Wu, Wen, You, Zhang, Hao, Lin, Su; arXiv 2609.24749) names my strongest negative result — latent distance misorders the few candidates that compete for execution — and proposes a fix. I ran the fix with the wrong labels. This post says what the paper's test needs and why mine was not it.
DINO-WM (Zhou, Pan, LeCun, Pinto; arXiv 2411.04983) is the recipe behind W2: frozen DINOv2 patches plus a learned dynamics model. It had the better representation and lost the same planning gate as W1, more slowly.
Two papers, one experiment: both calibrate a time-varying conformal band on successful rollouts and flag a run that leaves it. FAIL-Detect (Xu et al., TRI; arXiv 2503.08558) does it on learned OOD scores with no failure data; SAFE (Gu et al., UofT/TRI; arXiv 2506.09937) on a probe of the VLA's own features trained with failures. I ran the band on W1's surprise (W1-C2). It failed, and the way it failed is the way FAIL-Detect's own hardware section describes.
H-JEPA (Zhang, Terver, Rabbat, LeCun, Balestriero; arXiv 2610.06805) stacks LeWorldModels across timescales. I tested its level-2 slow feature as a progress signal (G6b) and it failed. Reading the paper afterwards, the paper's own Fig. 5 predicts that failure.
One paper, one arm. This series takes each paper I actually tested on a SO-101 + Jetson Orin NX, states what the paper claims and how its authors measured it, puts my number next to theirs, and says what the gap means. Numbers are from journal/EXPERIMENTS.md; gate IDs are the register's.
LeWorldModel (Maes, Le Lidec, Scieur, LeCun, Balestriero; arXiv 2603.19312) is the recipe behind W1, the world model that runs on my arm. Four gates tested it: dynamics (G0), planning (G1b), progress (G6a), outcome readout (G10).
Real-Time Chunking (Black, Galliker, Levine, Physical Intelligence; arXiv 2506.07339) treats the next action chunk as an inpainting problem. My gradient-free prefix overwrite is the paper's "hard mask" line, and the paper says where it should lose.
ReAP (Liu, Qing, Wei, Hou; arXiv 2610.16309) trains a failure-supervised visual space and an action-conditioned risk predictor on top of it. My "ReAP-style risk head" is the closest any monitor got to my bar — and it is a stripped version with two confounds the paper's own design removes.
REKI (Marrie, Pacaud, Ramanana Rahary, Chen, Böhle; Kyutai / Inria; arXiv 2610.22178) gives a VLA a memory through register tokens and measures the oscillation it removes. I voided my own latency measurement of it and then found the same oscillation in my policies with their metric.
RoboJEPA (Zholus, Assran et al., Meta FAIR; arXiv 2610.10515) is a scaling law for latent world models trained on 15 000 hours of robot video, including 21.5k LeRobot community episodes from arms like mine. Two gates: can the encoder run on the Orin, and does the pretrained predictor use my actions.