SOURCE-LINKED INTELLIGENCE

SPeaR: Test-Time Adaptation with Steering Primitives for Realigning Representations

arXiv · AI, language, vision and robotics · article · Sep 21, 2026 · UTC

Test-time adaptation (TTA) addresses distribution shift using only unlabeled test data. Existing methods typically adapt pretrained models by updating their parameters, limiting both what is adapted and where adaptation can occur within the network. We instead keep the pretrained network frozen and steer its intermediate representations. We introduce SPeaR (Steering Primitive for Realigning Representations), which inserts lightweight learnable modules at stage boundaries and optimizes them directly from the test stream, requiring neither source data nor supervised warm-up. Each primitive is op

Read original source ↗ Open in workspace

recordType

paper

region

Global