DLMs encode a decodable latent timestep signal in residual activations that can be steered to predictably change model confidence and entropy.
arXiv preprint arXiv:2603.06123 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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VoidPadding decouples padding from termination in MDLMs via a new [VOID] token, delivering +17.84 average benchmark points and 55.7% fewer decoding steps on Dream-7B-Instruct.
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Subliminal Clocks: Latent Time Modelling in Diffusion Language Models
DLMs encode a decodable latent timestep signal in residual activations that can be steered to predictably change model confidence and entropy.
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VoidPadding: Let [VOID] Handle Padding in Masked Diffusion Language Models so that [EOS] Can Focus on Semantic Termination
VoidPadding decouples padding from termination in MDLMs via a new [VOID] token, delivering +17.84 average benchmark points and 55.7% fewer decoding steps on Dream-7B-Instruct.