In-Place TTT adapts LLM MLP projection matrices at test time with a next-token-aligned objective and chunk-wise updates, enabling better long-context performance as a drop-in enhancement.
Tent: Fully test-time adaptation by entropy minimization
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ADAPT reframes test-time adaptation as probabilistic Gaussian inference with CLIP-guided regularization, delivering SOTA results without gradients, source data, or full target access in both online and transductive settings.
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In-Place Test-Time Training
In-Place TTT adapts LLM MLP projection matrices at test time with a next-token-aligned objective and chunk-wise updates, enabling better long-context performance as a drop-in enhancement.
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Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment
ADAPT reframes test-time adaptation as probabilistic Gaussian inference with CLIP-guided regularization, delivering SOTA results without gradients, source data, or full target access in both online and transductive settings.