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In the authors' tests, changing opening tokens mainly helped models add or restyle information, not consistently leave it out.
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Random biases to output logits reportedly matched or beat full-weight perturbations on most reasoning tasks tested.
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A 109M JEPA-Text model is claimed to align with vision models better than 72B LLMs.
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A post links the 2026 paper, listing H Deng and colleagues as authors and Harbin Institute of Technology.
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A post links to an arXiv page for the 2026 computer vision paper, naming ENPC and École Polytechnique.
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A Stanford paper on how language models pick between context and memory is shared on X.