T3R applies multiple Rotograd matrices and a rotation technique to create surrogate gradients, enabling deeper test-time adaptation in GNNs and yielding 0.172 MAE reduction plus 9.37% relative gains on OGB benchmarks.
A short survey on almost orthogonal vectors in a few specific large dimensions.arXiv preprint arXiv:2510.23609,
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T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation
T3R applies multiple Rotograd matrices and a rotation technique to create surrogate gradients, enabling deeper test-time adaptation in GNNs and yielding 0.172 MAE reduction plus 9.37% relative gains on OGB benchmarks.