ARCHead: Activation-Metric Residual Correction for Large Language Model Output Heads
Weight-only quantization substantially reduces the storage of large language model (LLM) transformer blocks, but practical backends often retain the final language-modeling head (LM-head) in BF16 or FP16. Quantizing this projection naively can strongly perturb the vocabulary-logit distribution. We present ARCHead, a packed LM-head compressor that combines a quantized low-rank core, group-wise INT4 residuals, and a low-rank correction fitted in an activation-derived metric. ARCHead stores no dense BF16 head and reduces persistent LM-head storage by 3.7-3.9x. On Qwen3-8B-Base, it uses 25.6% of BF16 head storage while attaining 1.007 relative perplexity; storage-matched naive INT4 yields 1.1...
| Authors | Şuayp Talha Kocabay, Talha Rüzgar Akkuş, Kamer Ali Yuksel |
|---|---|
| Primary category | cs.CL |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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