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arXiv · 2601.09089

SubTokenTest: A Practical Benchmark for Real-World Sub-token Understanding

Shuyang Hou·Yi Hu·Muhan Zhang
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Abstract

Recent advancements in large language models (LLMs) have significantly enhanced their reasoning capabilities. However, they continue to struggle with basic character-level tasks, such as counting letters in words, a problem rooted in their tokenization process. While existing benchmarks have highlighted this weakness through basic character operations, such failures are often dismissed due to lacking practical relevance. Yet, many real-world applications, such as navigating text-based maps or interpreting structured tables, rely heavily on precise sub-token understanding. In this regard, we introduce SubTokenTest, a comprehensive benchmark that assesses sub-token understanding through practical, utility-driven tasks. Our benchmark includes ten tasks across four domains and isolates tokenization-related failures by decoupling performance from complex reasoning. We provide a comprehensive evaluation of nine advanced LLMs. Additionally, we investigate the impact of test-time scaling on sub-token reasoning and explore how character-level information is encoded within the hidden states.

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Shuyang Hou, Yi Hu, Muhan Zhang. 2026-01-14. SubTokenTest: A Practical Benchmark for Real-World Sub-token Understanding. https://arxiv.org/abs/2601.09089

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