Lexical Perturbations Disrupt LLM Reasoning: An Empirical Study of Attention Diversion
Large Language Models (LLMs) achieve strong reasoning performance, but their robustness to realistic lexical corruption remains poorly understood. We evaluate four open-weight instruction-tuned models and frontier models across four reasoning benchmarks under keyboard noise, character swaps, and filler insertion. Character-level perturbations substantially degrade accuracy, especially on multi-step reasoning tasks, while filler insertion has little effect. We trace this asymmetry to Attention Diversion: lexical corruption fragments subword tokenization, and the resulting fragments attract disproportionate attention mass, concentrated in middle and final transformer layers. Length-matched ...
| Authors | Jiaqian Zhu, Yang Zhang, Junhua Ding et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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