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This paper introduces Weightless Fine-Tuning (WFT), a training-free decoding-time method that approximates supervised fine-tuning effects via logit-space transport, achieving competitive personalization performance with less than 7% of the computation.
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.