Syntactic Blind Spots: How Misalignment Leads to LLM s’ Mathematical Errors (2025)

Abstract: Large Language Models (LLMs) demonstrate strong mathematical problem-solving abilities but frequently fail on problems that deviate syntactically from their training distribution. We identify a systematic failure mode, syntactic blind spots, in which models misapply familiar reasoning strategies to problems that are semantically straightforward but phrased in unfamiliar ways. These errors are not due to gaps in mathematical competence, but rather reflect a brittle coupling between surface form and internal representation. To test this, we rephrase incorrectly answered questions using syntactic templates drawn from correct examples. These rephrasings, which preserve semantics while reducing structural complexity, often lead to correct answers. We quantify syntactic complexity using a metric based on Dependency Locality Theory (DLT), and show that higher DLT scores are associated with increased failure rates across multiple datasets. Our findings suggest that many reasoning errors stem from structural misalignment rather than conceptual difficulty, and that syntax-aware interventions can reveal and mitigate these inductive failures.

## introduction large language models (llms) show strong performance on mathematical benchmarks like gsm8k, svamp, multiarith, and asdiv @xcite @xcite ), yet they frequently make systematic errors, often reapplying familiar solution strategies even when the problem structure changes @xcite @xcite . these errors reflect an overreliance on surfacelevel pattern matching rather than adaptive reasoning. we focus on a specific class of such failures, which we term syntactic misalignment, cases where llms fail because a problem's phrasing deviates structurally from patterns they have learned to solve, even though the underlying logic remains "the great dragon, perg, sits atop mount farbo, breathing �re within a 1000-foot radius. polly can throw the gold javelin, the only known weapon to defeat the dragon, for a distance of 400 feet, within the dragon's �ames. however, when polly holds the sapphire gemstone, she can throw the javelin three times farther than usual. if holding the gemstone, how far outside the dragon's �ames can polly stand and still hit the dragon with the gold javelin?" "the great dragon, perg, sat high atop mount farbo, breathing �re upon anything within a distance of 1000 feet. polly could throw the gold javelin, the only known weapon that could slay the dragon, for a distance of 400 feet, well within the reach of the dragon's �ames. but when polly held the sapphire gemstone, she could throw the javelin three times farther than when not holding the gemstone. if holding the gemstone, how far outside of the reach of the dragon's �ames could polly stand and still hit the dragon with the gold javelin?" answer: 1200 feet answer: 200 feet unchanged. as shown in figure 1 , rephrasing a problem to reduce syntactic complexity can often reverse these failures. prior work has documented llm sensitivity to superficial variation: word order changes, paraphrasing, or structural perturbations can all induce performance drops @xcite @xcite . models sometimes ignore altered constraints and produce answers consistent with the original phrasing. we argue that this brittleness arises from a structural failure mode we call syntactic induction, the tendency to treat syntactic similarity as a proxy for problem similarity. this leads models to overapply familiar solution templates, even when the problem logic has changed. inspired by cognitive science, we draw an analogy to rule-based overgeneralizations in human learners @xcite , where errors arise not from lack of competence but from misapplied procedural regularities. to study this phenomenon, we develop a dependency-guided framework for identifying and mitigating syntactic induction failures. we use dependency locality theory (dlt) to quantify syntactic complexity and rephrase high-complexity questions using syntactic templates drawn from successful examples. this reveals that many reasoning errors stem not from mathematical difficulty, but from a mismatch between surface form and learned problem schemas. ## experiment setup to assess the impact of syntactic restructuring, we re-evaluate the llm on the rephrased variants ## experimental results we first test whether surface-level syntactic complexity predicts model failure. we then assess whether syntactic restructuring can recover accuracy on previously incorrect questions. ## further analysis the section provides further analysis regarding how syntactic structure influences llm reasoning behavior. it examines four key dimensions: first, we show that elevated syntactic complexity, measured using dlt, predicts failure on narrative math tasks. second, we demonstrate that rephrasing these complex questions into syntactically familiar forms improves model accuracy, supporting an interpretation of failure as schema misalignment. third, we analyze these errors in light of cognitive theory, arguing that llms overapply familiar strategies to structurally novel inputs, a form-function misalignment. finally, we outline implications for robustness and generalization, proposing syntax-aware interventions and cognitively grounded training approaches. ## conclusion this work investigated how syntactic structure influences the reasoning behavior of llms on mathematical problems. across four benchmarks: gsm8k, svamp, multiarith, and asdiv, we found that llms systematically fail on syntactically complex inputs, despite their semantic simplicity. these failures were reliably predicted by elevated dependency locality theory (dlt) scores and mitigated through targeted syntactic rephrasing. our findings demonstrate that many reasoning errors stem not from a lack of mathematical compe-tence, but from syntactic induction failures, a tendency to misapply known solution strategies when surface structure deviates from training priors. rephrasing misaligned questions into syntactically familiar forms improved accuracy across all models, with gains particularly notable in lower-performing systems like gemma and granite. this supports the view, rooted in cognitive science, that schema activation in both humans and llms is highly sensitive to surface cues. by framing these errors within a rule-based taxonomy and formalizing complexity through dlt, we offer a structured explanation for inductive failure in llms. rather than viewing mistakes as isolated or stochastic, we show they are predictable, syntaxsensitive, and recoverable through lightweight interventions. future directions this work opens several directions for enhancing llm robustness and interpretability. we highlight: while our experiments focus on mathematical benchmarks, the implications are broader. syntactic induction failures may underlie reasoning brittleness across domains. addressing these failures offers a path toward llms that reason more like human experts: flexibly, structurally, and with awareness of when form does, and does not, align with function.