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Hard-KV introduces a Cascade Cache hierarchy and Logits Calibration mechanism to resolve the static-dynamic mismatch in head-adaptive KV cache compression, achieving up to 2x throughput improvement in long-context LLM inference.
This paper introduces AESOP, a framework for adversarial execution-path selection that significantly inflates FLOPs and latency in deep learning inference pipelines, revealing new efficiency-based vulnerabilities.