@inproceedings{jiang-etal-2026-immediate,
    title = "Immediate Inference: The Missing Foundation in Large Language Model Logical Reasoning",
    author = "Jiang, Sihang  and
      Lu, Zhiyu  and
      Wang, Keyi  and
      Liang, Jiaqing  and
      Xiao, Yanghua  and
      Meng, Xiaojun  and
      Wei, Jiansheng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.808/",
    pages = "17766--17799",
    ISBN = "979-8-89176-390-6",
    abstract = "While extensive research has evaluated LLMs on complex reasoning tasks, the foundational building blocks of logical reasoning remain underexplored. We introduce IIBench, a benchmark evaluating immediate inference (elementary operations over categorical propositions). Our evaluation reveals that even SoTA models exhibit systematic deficiencies in immediate inference, and establishes immediate inference as foundational: it mediates approximately 40{\%} of the effect on syllogistic reasoning, with near-perfect correlation ( = 0.98) across reasoning benchmarks. Our analysis reveals that models lack robust operator grounding, oscillating between structural reasoning and surface pattern matching with inconsistent handling of quantifiers and negation."
}