Structured Recurrent Mixers enable algebraic switching between parallel training and recurrent inference representations, delivering higher efficiency, information capacity, and throughput than other linear-complexity models.
Cox, Ruchir Puri, and Rameswar Panda
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SynConfRoute routes code completions using syntax validation and token confidence, improving pass@1 by up to 31% on hard tasks and reducing accelerator usage by 58% versus always using the largest model.
6G networks need LLM-based agents in a layered semantic control plane to achieve autonomous intelligence, with empirical results showing that heterogeneous deployment across device-edge-core is required due to inherent tradeoffs in reasoning, latency, and efficiency.
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Structured Recurrent Mixers for Massively Parallelized Sequence Generation
Structured Recurrent Mixers enable algebraic switching between parallel training and recurrent inference representations, delivering higher efficiency, information capacity, and throughput than other linear-complexity models.