KL divergence correlates with benchmark scores over wide quantization ranges but loses all predictive power in the near-baseline silent zone because it tracks disagreement volume rather than direction.
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
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PipeSD is a cloud-edge collaborative inference framework that overlaps token generation and communication via dynamic programming pipeline scheduling and uses Bayesian-optimized dual-threshold NAV triggering, delivering 1.16x-2.16x speedup and 14.3%-25.3% energy reduction over baselines.
Empirical evaluation of quantization effects on eight LLMs across bit widths, showing performance generally declines at lower precision but with model-size-dependent resilience and acceptable accuracy at 2 bits for many cases.
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