arXiv:2609.07901v1 Announce Type: new
Abstract: Weight quantization largely determines the economics of serving open-weight LLMs. Its costs are usually assessed with capability benchmarks, on which 4...
By Dachi Kurtskhalia
The paper investigates how reducing numerical precision through model quantization impacts the vulnerability of neural networks to model inversion attacks. It provides theoretical bounds on mutual information changes and identifies data-dependent effects, especially at 4‑bit precision. Based on these findings, the authors propose a privacy‑aware post‑training quantization strategy that allocates bits adaptively, calibrates activation ranges, and jointly optimizes weight and activation scaling to improve inversion resistance while preserving model utility.
By Rongke Liu, Youwen Zhu
The study investigates how post‑training quantization (PTQ) affects proactive interference (PI) in large language models. Using bitsandbytes, the authors compare FP16, INT8, and INT4/NF4 precision across three instruction‑tuned models and find that INT4 quantization markedly degrades accuracy under high interference, with INT8 also incurring a smaller penalty in two of the three models. The degradation is linked to increased same‑key intrusion errors and originates in the quantized transformer backbone rather than the output layer.
By Shayan Shahrabi-Farahani (Shahid Beheshti University, Tehran, Iran), Dara Rahmati (Shahid Beheshti University, Tehran, Iran)
arXiv:2609.06473v1 Announce Type: new
Abstract: Inference-time activation steering enables behavioral control of large language models without parameter modification, while post-training quantization...
By Saurav Bhandari, Benjamin Wade
The paper investigates where post‑training quantization (PTQ) harms large language models (LLMs) and how to best allocate a limited precision budget. By causally raising each layer to 8‑bit precision across nine open‑weight models, the authors find that quantization damage is diffuse rather than concentrated in specific task circuits or weight statistics, and that globally refining quantization granularity outperforms selectively protecting the most recoverable layers. They also observe that the residual accuracy loss is budget‑limited and that peak recovery locations correlate with architecture within families but not across families.
By Jundong Hu, Shekar Ramachandran
arXiv:2608. 06564v1 Announce Type: new Abstract: Quantization is how large language models are actually deployed, and below four bits it is known to hurt.
By Zekun Wu, Swati Dhiman, Adriano Koshiyama