SNLP enables layer-parallel Transformer inference by replacing sequential layer execution with structured Newton corrections and SNLP-aware training regularization, yielding up to 2.3x wall-clock speedup on 0.5B models while improving perplexity.
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State-space models are vulnerable to three new attack types that corrupt state integrity, with experiments showing up to 156x output changes and 6x higher targeted corruption than random inputs.
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Safety, Security, and Cognitive Risks in State-Space Models: A Systematic Threat Analysis with Spectral, Stateful, and Capacity Attacks
State-space models are vulnerable to three new attack types that corrupt state integrity, with experiments showing up to 156x output changes and 6x higher targeted corruption than random inputs.