arXiv: cs.AI preprintsInternational6 November 2026
Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation
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arXiv:2606.09864v3 Announce Type: replace-cross Abstract: Key-value (KV) cache quantization is widely used to reduce LLM inference memory, yet existing evaluations focus only on perplexity and accuracy without assessing the safety impact. In this study, we explore alignment preservation under KV cache quantization. Across eleven instruction-tuned models (3.8B-72B) and five benchmarks (1,894 prompts), we find that low-bit quantization can silently destroy safety alignment: Mistral-7B loses 15.2% of its refusals at only 1.03x perplexity, and no universal safe bit-width exists, with sharp model-s
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