3 papers
cs.LG2026
Augment the Cache, Replace the Activation: A 2x2 Study of Internal-State Sharing
Adam Deng, Shicheng Liu, Yada Zhu
Multi-agent systems built from frozen language models communicate through text, which discards the internal state the sender already computed and makes the receiver pay to rebuild…
cs.LG2026
Close Enough is Nowhere Near: Fidelity-Consumability Decoupling in KV Caches
Adam Deng, Shicheng Liu, Yada Zhu
Multi-agent LLM systems communicate in text, which is lossy and wasteful: the sender throws away the reading it already did. A growing literature instead transmits the KV cache, th…
cs.LG2026
ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models
Ryan Lucas, Mehdi Makni, Xiang Meng +2
Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (PTQ) is a leading approach for co…