Lakksh at SemEval-2026 Task 11(1 & 2): Neuro-Symbolic Decomposition to Mitigate Content Bias in Syllogistic Reasoning

AbstractSyllogistic reasoning is the ability to distinguish logical validity from semantic plausibility — a setting in which LLMs succumb to frequent content bias by conflating the two. The result is a characteristic failure to recognize logically valid arguments with highly implausible conclusions and logically invalid but semantically plausible arguments. This paper introduces a neuro-symbolic system that avoids this behavior by design: neural structure extraction is strictly separated from symbolic validity checking. A T5-Small parser is trained only on synthetic nonsense-symbol syllogisms, ensuring that the structural parse is learned in the absence of real-world semantics. Validity checking is performed by a deterministic symbolic kernel operating on extracted logical form alone, ensuring that semantic content cannot influence the final call. In binary validity classification, the system achieves 97.38% accuracy with a Total Content Effect of 3.10; in the retrieval setting, it achieves 82.11% accuracy with 99.47% F1 on premise identification. Ablation experiments show that formal theorem proving via NL-to-Z3 translation actually increases content bias due to leakage in intermediate representations. The results recommend architectural separation as a promising content-robustness strategy for syllogistic reasoning.