Silent Errors in AI: How a Single Bit Flip Can Compromise Large Language Models
![The analysis reveals that the mean transfer characteristic ratio [latex] TCR [/latex] fluctuates with bit position, demonstrating consistent variation across all five concurrency levels-a pattern underscored by the standard deviation observed across those levels.](https://arxiv.org/html/2604.17249v1/x1.png)
New research reveals a critical vulnerability in how large language models manage memory, potentially allowing subtle data corruption to alter outputs without detection.
![The analysis reveals that the mean transfer characteristic ratio [latex] TCR [/latex] fluctuates with bit position, demonstrating consistent variation across all five concurrency levels-a pattern underscored by the standard deviation observed across those levels.](https://arxiv.org/html/2604.17249v1/x1.png)
New research reveals a critical vulnerability in how large language models manage memory, potentially allowing subtle data corruption to alter outputs without detection.

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![The study demonstrates that employing a low-rank approximation-specifically utilizing [latex]MXFP8e4[/latex] quantization for the low-rank branch and activations alongside [latex]MXFP4e2[/latex] for the residual branch-within the LoRaQ framework on the PixArt-ΣΣ architecture demonstrably impacts generative quality, as corroborated by quantitative results presented elsewhere.](https://arxiv.org/html/2604.18117v1/sections/04_experiments/figures/table4_fp8/sample_7/r128.png)
A new method dramatically reduces the computational demands of large AI models without sacrificing performance by intelligently compressing their core components.

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