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GoldiMask is introduced, which selects tokens to reveal as context by approximately maximizing a submodular objective and weights the remaining targets according to how they benefit from the selected context and their remaining learning potential.
SymDrift is a framework that makes the drifting field itself symmetry-aware, and introduces two complementary strategies: a symmetrized drift in coordinate space based on optimal alignment, and a $G$-invariant embedding that removes symmetry ambiguity by construction.
SPECTRA is a framework built on a frozen encoder which adds three components, a lightweight trainable adapter that calibrates the generic embeddings to the task, an exemplar-free anti-forgetting scheme, and a transductive optimal-transport refinement of prototypes at test time.
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