AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Large language model (LLM) routing distributes inference work across different models, advancing the cost-quality Pareto frontier of LLM serving. While coarse-grained routing at the session or query level has been widely adopted in production systems, recent algorithmic work shows that fine-grained token-level routing...
Looped transformers have demonstrated promising parameter efficiency by reusing layers for latent computation. Prior studies compare looped and non-looped models at matched parameters or per-token FLOPs. However, to the best of our knowledge, whether looping improves test-time scaling as outputs grow longer remains und...
This work proposes Think-at-Hard (TaH), a looped transformer optimized for selective iteration that employs a lightweight neural decider to trigger latent iteration, only at tokens likely to be incorrect after the standard forward pass.
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