Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference

• URL: http://arxiv.org/abs/2606.02430v1
• Date: Mon, 01 Jun 2026 16:04:51 GMT
• Title: Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference
• Abstract summary: We present a comprehensive study on error propagation in large language models (LLMs) inference, enabled by our proposed fault-injection framework.<n>We inject faults across three open-weighted LLMs and thirteen representative tasks, covering reasoning, multilingual, mathematical, and coding domains.<n>Overall, our study yields 17 takeaways that advance the understanding of error propagation in LLM inference.
• Score: 3.160702454918557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are increasingly integrated into high-performance computing (HPC) workflows, accelerating scientific discovery through diverse perspectives such as code generation and domain-specific decision-making. Yet, how soft errors propagate and affect LLM inference remains largely unexplored. To bridge this gap, we present a comprehensive study on error propagation in LLM inference, enabled by our proposed LLMFI, a configurable and deterministic fault-injection framework. Using LLMFI, we systematically inject faults across three open-weighted LLMs and thirteen representative tasks, covering reasoning, multilingual, mathematical, and coding domains. In addition, we conduct fine-grained case studies that reveal critical vulnerability patterns. Overall, our study yields 17 takeaways that advance the understanding of error propagation in LLM inference and introduces four low-overhead directions to improve reliability through software-only modification, offering practical guidance for future error detection and mitigation.

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