TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
Achieving fairness generalizability for learning-based congestion control with jury,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Learning-based congestion control acquires available bandwidth with low latency in fixed conditions and resists non-congestive loss, yet fairness and adaptation fail to generalize when bandwidth or latency vary over time.
ShadowNPU presents shadowAttn, a co-designed sparse attention system that uses NPU pilot compute and techniques like graph bucketing and per-head sparsity to minimize CPU/GPU fallback during on-device LLM inference while maintaining accuracy.
citing papers explorer
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TIDAL: Recovering Temporal Phase for Cloud Block Storage Placement from LLM-Derived Semantics
TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
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Learning-Based vs Human-Derived Congestion Control: An In-Depth Experimental Study
Learning-based congestion control acquires available bandwidth with low latency in fixed conditions and resists non-congestive loss, yet fairness and adaptation fail to generalize when bandwidth or latency vary over time.
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ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference
ShadowNPU presents shadowAttn, a co-designed sparse attention system that uses NPU pilot compute and techniques like graph bucketing and per-head sparsity to minimize CPU/GPU fallback during on-device LLM inference while maintaining accuracy.