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MaRK: Markov-adapted Recurrent Kernels for Dynamic Operator Conditioning in State Space Models
State Space Models (SSMs) offer an efficient alternative to Transformers for sequence modeling, yet conditioning pre-trained SSMs for iterative generation typically operates outside the recurrent operator, through input injection or activation modulation. While such mechanisms expose the model to co
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