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University of Michigan
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Temporal validity in patient memory design can significantly enhance clinical reasoning accuracy, challenging the notion that more historical data is always better.
A hybrid cloud-edge system achieves near-perfect diagnostic recall while slashing operational costs and latency in rural healthcare settings.
LLMVisor achieves up to 4.4x improvement in latency attribution accuracy for multi-tenant LLMs, revealing hidden inefficiencies in GPU resource usage.
DLA adapts memory management in linear attention, preserving crucial information while reducing error accumulation over long sequences.
Cut LLM cold starts from minutes to seconds by pre-materializing CUDA graph execution contexts, sidestepping brittle kernel patching and heavyweight checkpointing.
