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This paper explores the trade-off between memory and peer communication in bounded agents, proposing a 'remembering–signaling frontier' to optimize information allocation for task performance under resource limits.
This paper introduces RateQuant, a method for optimal mixed-precision KV cache quantization that uses rate-distortion theory to address distortion model mismatch. It significantly reduces perplexity compared to existing methods like KIVI and QuaRot with minimal calibration overhead.