Unverified50% confidenceOpinionExact time
The student model requires no access to MoE internal routing at inference time, making deployment more flexible, which explains why it can transfer across architectures.
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Long-term
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10/22/2026
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UnverifiedModel Routing and Hybrid Inference have already been explored in academia and were not pioneered by Cognition.68% similarUnverifiedThe unique advantage of knowledge distillation is that it does not alter the student model's architecture and can be orthogonally combined with other compression techniques.68% similarUnverifiedOrchestration frameworks extend the practical boundaries of models by building a perception-memory-action enhancement layer outside the model, without retraining the model.67% similarUnverifiedDense models have simpler inference logic and better hardware compatibility in local deployment scenarios, whereas MoE models, despite activating fewer parameters during inference, still require all weights to be loaded into memory, placing even higher demands on hardware.66% similarUnverifiedRecurrent architectures internalize additional computation inside the model's forward pass, whereas chain-of-thought or reasoning models generate more tokens externally.66% similar
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