@inproceedings{wang-etal-2026-ynu,
    title = "{YNU}-{NLP} at {S}em{E}val-2026 Task 11: A Neuro-Symbolic Approach with Reflexion Mechanism Disentangling Content and Formal Reasoning in Language Models",
    author = "Wang, Yu  and
      Zhang, You  and
      Zhang, Hao  and
      Xu, Dan",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.126/",
    pages = "919--926",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our systems for SemEval-2026 Task 11, Disentangling Content and Formal Reasoning in Language Models. We participated in all four subtasks, addressing the Content Effect-a phenomenon where models rely on real-world plausibility rather than logical validity. Existing methods, such as standard Chain-of-Thought (CoT) prompting or single-task Supervised Fine-Tuning (SFT), often struggle to completely decouple content from reasoning due to the inherent probabilistic biases in pre-trained models. To address these limitations, a hybrid neuro-symbolic framework based on the Qwen2.5-14B architecture is proposed, integrating multi-task instruction tuning with a robust neuro-symbolic pipeline. The principal innovation lies in the deployment of a Reflexion mechanism coupled with formal verification: natural language arguments are parsed into First-Order Logic (FOL) and subsequently verified by the Z3 Theorem Prover. Parsing anomalies are automatically rectified through an iterative self-correction module. The proposed system ranked 1st in Subtasks 1 {\&} 2, 2nd in Subtask 4, and 9th in Subtask 3, validating its ability to decouple logical validity from content plausibility."
}