轻量级故障检测与快速修复框架，提升大模型可靠性
LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Models
- 通过短测试向量+哈希校验快速定位位翻转错误
- 单比特翻转检测率超94%，多比特接近100%，开销1%-7.7%
- 修复速度比重载快100倍以上，适合生产环境部署
本文提出LM-Fix，一种针对大语言模型（LLMs）故障的轻量级检测与快速恢复框架。现有完整性方法对现代大模型而言通常开销过大或响应缓慢。LM-Fix通过一次短测试向量遍历，结合哈希引导的校验机制检测位翻转故障，并在本地完成修复，无需全量重载。在多个模型上，其在测试向量长度（TVL）为200时可检测超过94%的单比特翻转，近似100%的多比特翻转，运行开销仅为1%至7.7%；恢复速度比重新加载快逾100倍。结果表明，该方案是保障大模型在生产环境中可靠运行的实用低开销方案。
原文摘要 · Abstract (English)
This paper presents LM-Fix, a lightweight detection and rapid recovery framework for faults in large language models (LLMs). Existing integrity approaches are often heavy or slow for modern LLMs. LM-Fix runs a short test-vector pass and uses hash-guided checks to detect bit-flip faults, then repairs them locally without a full reload. Across multiple models, it detects over 94% of single-bit flips at TVL=200 and nearly 100% of multi-bit flips with approximately 1% to 7.7% runtime overhead; recovery is more than 100x faster than reloading. These results show a practical, low-overhead solution to keep LLMs reliable in production
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