SyntaxBench: A Statistical Diagnostic Framework for Character-Level Reasoning in Large Language Models
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Computer Science > Computation and Language
arXiv:2610.03329 (cs)
[Submitted on 2 Oct 2026]
Title:SyntaxBench: A Statistical Diagnostic Framework for Character-Level Reasoning in Large Language Models
Authors:Mohsen Larni (1), Sobhan Ebrahimi Azar (1), Pouyan Nahed (1), Kazem Taghva (1) ((1) Department of Computer Science, University of Nevada, Las Vegas)
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Abstract:Large language models are increasingly used where small syntactic errors matter, yet character-level reasoning is still evaluated mostly through isolated probes and aggregate accuracy. We introduce SyntaxBench, a diagnostic benchmark and statistical evaluation framework for character-level reasoning. It contains five core tasks, character counting, letter containment, palindrome detection, edit distance, and longest-string selection, plus index_to_span, a harder substring-extraction stress test. The five core tasks use paired English and character-length-matched random-string inputs. index_to_span documents share a 200-500 word band and are not character-length matched. All six tasks use zero-, one-, and four-shot prompts.
We evaluate eight open-weight models from 2B to 32B parameters across 11 reasoning-mode configurations. The framework reports exact-match and relaxed accuracy, Cohen's kappa, paired McNemar tests with odds ratios, bootstrap confidence intervals, Kendall's tau, class-conditional metrics, tokenization analysis, and multiple-comparison-corrected tests. Three findings stand out. First, tokenization shapes accuracy: random strings are more character-visible than English strings (1.892 vs. 3.169 characters per token), and character-counting accuracy falls as English words occupy more tokens. Second, reasoning mode is not uniformly helpful: Gemma4-31B is nearly unchanged across modes on the near-saturated tasks, while Qwen3.6-27B is worse with thinking on palindrome detection (0.952 non-thinking vs. 0.886 thinking at four-shot). Third, index_to_span remains largely unsolved; the best four-shot exact-match accuracy is 6.75%.
Character-level evaluation needs controlled inputs, paired tests, and analyses of tokenization and reasoning mode rather than aggregate accuracy alone.
| Comments: | 32 pages, 17 figures. The first two authors contributed equally. The code will be released soon |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| ACM classes: | I.2.7; I.2.6 |
| Cite as: | arXiv:2610.03329 [cs.CL] |
| (or arXiv:2610.03329v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03329
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled SyntaxBench: A Statistical Diagnostic Framework for Character-Level Reasoning in Large Language Models, by Mohsen Larni (1) and 5 other authors
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