Security-Enhanced Seed-Based Weight Quantization for Large Language Models

Reported by arXiv cs.CR (Qiuyu Ren, Sudipta Paria, Aritra Dasgupta, Swarup Bhunia)

Summary by Cyber Security News1 Oct 2026, 06:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2609.38477

arXiv:2609.38477v1 Announce Type: new Abstract: Large language models (LLMs) incur substantial storage, memory-bandwidth and energy costs, motivating compact weight representations. Existing seed-based compression methods reconstruct weights from compact pseudo-random representations but do not explicitly account for the non-uniform sensitivity of model weights.

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