CALM: A CKA-Guided Adaptive Layer-Wise Modularization Framework for LLM Quantization

Jinhao Zhang; Yunquan Zhang; Daning Chen; JunSun; Zicheng Yan

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arXiv · 2512.16282

CALM: A CKA-Guided Adaptive Layer-Wise Modularization Framework for LLM Quantization

Jinhao Zhang·Yunquan Zhang·Daning Chen·JunSun·Zicheng Yan

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Abstract

Current mainstream post-training quantization methods for large language models typically apply a uniform quantization strategy across all network layers, overlooking the substantial differences in algorithmic suitability among layers. To address this limitation, we propose CALM (A CKA-guided Adaptive Layer-wise Modularization)a fine-tuning-free, plug-and-play framework for algorithmic heterogeneous quantization. CALM independently evaluates multiple PTQ algorithms on each layer and employs Linear Centered Kernel Alignment (CKA) as a metric to automatically select the optimal quantization strategy per layer. The individually optimized strategies are then integrated to construct a hybrid quantized model. Experiments demonstrate that our approach consistently outperforms both uniform quantization baselines and state-of-the-art mixed-precision methods across mainstream LLMsincluding LLaMA and Qwenin terms of perplexity (PPL) and downstream task performance.

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Jinhao Zhang, Yunquan Zhang, Daning Chen, JunSun, Zicheng Yan. 2026-01-08. CALM: A CKA-Guided Adaptive Layer-Wise Modularization Framework for LLM Quantization. https://arxiv.org/abs/2512.16282

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