We show that the pairwise angular structure of pre-trained weights serves as a domain-invariant semantic anchor for continual test-time adaptation.

We propose DISC, a dynamic decomposition method that adaptively adjusts step sizes during LLM inference to allocate compute more efficiently, significantly improving performance and sample efficiency across reasoning and code generation benchmarks.