@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Immediate Inference: The Missing Foundation in Large Language Model Logical Reasoning
%A Jiang, Sihang
%A Lu, Zhiyu
%A Wang, Keyi
%A Liang, Jiaqing
%A Xiao, Yanghua
%A Meng, Xiaojun
%A Wei, Jiansheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F jiang-etal-2026-immediate
%X 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.
%U https://aclanthology.org/2026.acl-long.808/
%P 17766-17799
Markdown (Informal)
[Immediate Inference: The Missing Foundation in Large Language Model Logical Reasoning](https://aclanthology.org/2026.acl-long.808/) (Jiang et al., ACL 2026)
ACL
- Sihang Jiang, Zhiyu Lu, Keyi Wang, Jiaqing Liang, Yanghua Xiao, Xiaojun Meng, and Jiansheng Wei. 2026. Immediate Inference: The Missing Foundation in Large Language Model Logical Reasoning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 17766–17799, San Diego, California, United States. Association for Computational Linguistics.