Technical Report: Activation Residual Hessian Quantization (ARHQ) for Low-Bit LLM Quantization

• URL: http://arxiv.org/abs/2605.00140v1
• Date: Thu, 30 Apr 2026 18:55:22 GMT
• Title: Technical Report: Activation Residual Hessian Quantization (ARHQ) for Low-Bit LLM Quantization
• Abstract summary: Activation Residual Hessian Quantization (ARHQ) is a post-training weight splitting method designed to mitigate error propagation in low-bit activation-weight quantization.<n>ARHQ analytically identifies and isolates error-sensitive weight directions into a high-precision low-rank branch.
• Score: 21.244110575269723
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present Activation Residual Hessian Quantization (ARHQ), a post-training weight splitting method designed to mitigate error propagation in low-bit activation-weight quantization. By constructing an input-side residual Hessian from activation quantization residuals (G_x), ARHQ analytically identifies and isolates error-sensitive weight directions into a high-precision low-rank branch. This is achieved via a closed-form truncated SVD on the scaled weight matrix W G^{1/2}_x . Experimental results on Qwen3-4B-Thinking-2507 demonstrate that ARHQ significantly improves layer-wise SNR and preserves downstream reasoning performance on ZebraLogic even under aggressive quantization. The code is available at https://github.com/BeautMoonQ/ARHQ.

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