Error Propagation in LLM Inference: Not All Errors Are Equal

Core Idea: This study systematically reveals the mechanism of soft error propagation in LLM inference, proposes the LLMFI fault injection framework, and summarizes 17 key conclusions and 4 low-cost reliability improvement directions. Original Author/Source: Paper author team (submitted to arXiv), source platform arXiv, original paper title: Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference, link: http://arxiv.org/abs/2606.02430v1, publication date: June 1, 2026.

Large Language Models (LLMs) are being integrated into High-Performance Computing (HPC) workflows, but the issue of soft error propagation during inference has been overlooked. Traditional error research focuses on hardware faults, while LLM inference involves massive parameters, complex attention mechanisms, and non-deterministic generation processes—where a single bit error can amplify into a completely incorrect output.

The research team developed the LLMFI (LLM Fault Injection) framework, which is configurable and deterministic. It allows precise control over the location, type, and timing of faults, simulates real hardware faults (such as memory bit flips, computing unit errors), and observes the model's performance under different fault scenarios to understand robustness boundaries.

The experiment selected 3 mainstream open-source LLMs (with different scales and architectures) and designed 13 representative tasks covering reasoning, multilingual, mathematics, and programming tasks to ensure the generality of the conclusions.

Key conclusions include: 1. The impact of errors depends on their location (errors in critical attention paths have more severe consequences); 2. Task type determines sensitivity (reasoning tasks are the most sensitive, while generation tasks have strong fault tolerance); 3. The relationship between model scale and robustness is non-linear (larger models are not always safer).

Case studies found: 1. Specific attention heads are particularly sensitive to errors—computational deviations lead to a sharp decline in output quality; 2. Errors in early layers of multi-layer Transformers are amplified by subsequent layers, forming a cascading effect, and early error detection yields significant benefits.

Four low-cost improvement directions: 1. Critical path redundant computation (majority voting to eliminate single-point faults); 2. Dynamic precision adjustment (balancing performance and reliability); 3. Error-aware scheduling (assigning high-risk tasks to reliable hardware); 4. Lightweight verification mechanism (inserting checkpoints for key intermediate results).

Research Significance: Provides tools and methodologies for the reliable deployment of LLMs in critical fields such as autonomous driving and medical diagnosis. Future Directions: Extend to multimodal models, distributed inference error propagation, and adaptive error recovery mechanisms. Conclusion: It is necessary to deeply understand LLM failure modes to build reliable intelligent systems.