TokDrift: When LLM Speaks in Subwords but Code Speaks in Grammar
Authors: name: Yinxi Li, name: Yuntian Deng, name: Pengyu Nie
Organization: University of Waterloo

Large language models (LLMs) for code rely on subword tokenizers, such as
byte-pair encoding (BPE), learned from mixed natural language text and
programming language code but driven by statistics rather than grammar. As a
result, semantically identical code snippets can be tokenized differently
depending on superficial factors such as whitespace or identifier naming. To
measure the impact of this misalignment, we introduce TokDrift, a framework
that applies semantic-preserving rewrite rules to create code variants
differing only in tokenization. Across nine code LLMs, including large ones
with over 30B parameters, even minor formatting changes can cause substantial
shifts in model behavior. Layer-wise analysis shows that the issue originates
in early embeddings, where subword segmentation fails to capture grammar token
boundaries. Our findings identify misaligned tokenization as a hidden obstacle
to reliable code understanding and generation, highlighting the need for
grammar-aware tokenization for future code LLMs.