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

·ArXiv cs.LG··

arXiv:2605.00140v1 Announce Type: new 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...

Read full article →

Related Articles

OpenAI’s o1 correctly diagnosed 67% of ER patients vs. 50-55% by triage doctors
donsupreme · Hacker News · 5mo ago
Harvard particle physicist Matthew Schwartz drops 36 papers authored with Claude
xqcgrek2 · Hacker News · 1d ago
Accelerating Gemma 4: faster inference with multi-token prediction drafters
amrrs · Hacker News · 5mo ago
An AI agent emailed researchers for help. It told us why
sbulaev · Hacker News · 9h ago
A couple million lines of Haskell: Production engineering at Mercury
unignorant · Hacker News · 5mo ago