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

Source: arXiv - 2606.02430v1

Overview

Large Language Models (LLMs) are now being woven into high‑performance computing pipelines for tasks ranging from code synthesis to scientific reasoning. However, when these models run on hardware that can suffer soft‑error faults (e.g., bit flips caused by radiation or voltage noise), we have little idea how those transient errors ripple through the model’s inference process. The paper “Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference” fills that gap by building a deterministic fault‑injection framework (LLMFI) and measuring how errors affect three open‑weight LLMs across 13 representative workloads.

Key Contributions

• LLMFI framework – a configurable, deterministic fault‑injection tool that can target any layer, tensor, or operation inside an LLM without modifying the original model code.
• Comprehensive empirical study – systematic injection of soft‑errors into three popular open‑weight LLMs (e.g., LLaMA‑2, Falcon, and Mistral) over 13 tasks covering reasoning, multilingual understanding, math, and code generation.
• 17 actionable takeaways – distilled insights about which model components, data types, and workload characteristics are most vulnerable to error propagation.
• Four low‑overhead mitigation strategies – software‑only techniques (e.g., selective redundancy, precision‑aware checkpointing, error‑aware token filtering, and adaptive decoding) that improve reliability with minimal performance penalty.
• Open‑source release – the authors publish LLMFI and all experimental scripts, enabling reproducibility and future research on fault‑tolerant LLM deployment.

Methodology

1. Fault‑injection design – LLMFI injects soft errors by flipping random bits in the floating‑point representation of tensors during forward passes. The framework lets researchers specify:

   • Target layer (embedding, attention, feed‑forward, etc.)
   • Tensor granularity (whole matrix, row, column, or individual element)
   • Error model (single‑bit flip, multi‑bit flip, or value‑clamp)

2. Deterministic replay – To guarantee reproducibility, LLMFI records the exact random seed and injection point, then re‑runs the same inference with the fault applied, ensuring that observed output differences are solely due to the injected error.

3. Benchmark suite – Thirteen tasks were chosen to span the typical LLM usage spectrum:

   • Reasoning (ARC‑Challenge, GSM‑8K)
   • Multilingual (XGLUE, MMLU‑Languages)
   • Mathematical (MATH, Symbolic Integration)
   • Coding (HumanEval, MBPP)

4. Metrics – For each run the authors measured:

   • Output correctness (exact match, BLEU, pass@k)
   • Confidence shift (log‑probability changes)
   • Latency overhead (to assess mitigation cost)

5. Case studies – Deep dives into selected failures highlighted patterns such as “attention‑head sensitivity” and “precision‑critical token embeddings”.

Results & Findings

Overall, the study shows that not all soft errors are equal—their impact hinges on where they strike, the precision mode, and the nature of the downstream task.

Practical Implications

• Reliability‑aware deployment – Cloud providers and HPC centers can use LLMFI to profile their specific LLM stacks and decide where to add redundancy (e.g., duplicate the first attention block) without over‑provisioning.
• Fault‑tolerant inference APIs – Service operators can expose a “robust mode” that activates selective checkpointing or token‑filtering for high‑stakes workloads (e.g., scientific code synthesis).
• Hardware‑software co‑design – Chip designers can prioritize error‑correction resources (ECC, parity) for memory regions that store early‑layer weights, yielding better ROI than blanket protection.
• Developer tooling – The open‑source LLMFI can be integrated into CI pipelines to automatically test new model releases against simulated soft‑errors, catching regressions before production.
• Cost‑effective safety – The low‑overhead mitigation strategies enable developers to meet reliability SLAs without resorting to expensive hardware redundancy, making fault‑tolerant LLM services more accessible to startups and research labs.

Limitations & Future Work

• Scope of models – The experiments focus on three open‑weight LLMs; proprietary or much larger models (e.g., GPT‑4) may exhibit different vulnerability patterns.
• Error model realism – Bit‑flip injection approximates radiation‑induced soft errors but does not capture timing‑related faults or permanent hardware defects.
• Task selection – While diverse, the benchmark suite omits real‑time streaming or reinforcement‑learning‑based applications where error propagation could behave differently.
• Mitigation evaluation – The proposed software tricks were tested under synthetic fault rates; real‑world fault distributions and interaction with existing hardware ECC need further study.

Future research directions include extending LLMFI to distributed inference pipelines, exploring adaptive runtime monitoring for on‑the‑fly error detection, and co‑optimizing hardware error‑correction schemes with LLM architecture design.

Authors

• Yafan Huang
• Sheng Di
• Guanpeng Li

Paper Information

• arXiv ID: 2606.02430v1
• Categories: cs.DC, cs.AI
• Published: June 1, 2026
• PDF: Download PDF