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Large language models impose substantial memory demands and arithmetic costs during inference. Dense-to-mixture-of-experts (MoE) conversion is a model optimization technique that aims to reduce these arithmetic costs. Dense-to-MoE conversion partitions pretrained feed-forward network (FFN) neurons into experts and sele...
BEHAVE, an agentic framework for multi-turn joint hardware design and verification through functional behavior modeling, defines Behavior IR to express task functionality as executable behavior models without prescribing implementation timing beyond the specification.
This work presents the first systems characterization of agent memory, introducing a system-oriented taxonomy classifying agent memory systems along four axes, and derives 10 system recommendations covering construction scheduling, capability floors, amortization via query volume, freshness-latency tradeoffs, and fleet...
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