@inproceedings{chen-etal-2026-parasuite,
title = "{P}ara{S}uite: Boosting {LLM} Reasoning via Paradox Resolution",
author = "Chen, Bin and
Zhang, Yu and
Ye, Hongfei and
Wang, Huiyang and
Liu, Wenxi and
Chen, Hongyang",
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.2047/",
pages = "44246--44256",
ISBN = "979-8-89176-390-6",
abstract = "Logical reasoning is a key capability of large language models, yet current benchmarks focus almost entirely on tasks that just check basic logical consistency and overlook the reflective reasoning required for paradox detection and resolution. To fill the gap, we present ParaSuite, the first pipeline dedicated to paradox research that automates data synthesis, evaluation, and training. We introduce PARADOX, a synthetic, high-quality data spanning two difficulty tiers and three academic domains, accompanied by specialized evaluation metrics and solving algorithms. We propose ParadoxBreaker-7B, trained with Mutual-Information Guided Fine-Tuning and reinforcement learning step verify paradox reward(PAPO). Experiments demonstrate significant improvements in both paradoxical and general STEM reasoning."
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<abstract>Logical reasoning is a key capability of large language models, yet current benchmarks focus almost entirely on tasks that just check basic logical consistency and overlook the reflective reasoning required for paradox detection and resolution. To fill the gap, we present ParaSuite, the first pipeline dedicated to paradox research that automates data synthesis, evaluation, and training. We introduce PARADOX, a synthetic, high-quality data spanning two difficulty tiers and three academic domains, accompanied by specialized evaluation metrics and solving algorithms. We propose ParadoxBreaker-7B, trained with Mutual-Information Guided Fine-Tuning and reinforcement learning step verify paradox reward(PAPO). Experiments demonstrate significant improvements in both paradoxical and general STEM reasoning.</abstract>
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%0 Conference Proceedings
%T ParaSuite: Boosting LLM Reasoning via Paradox Resolution
%A Chen, Bin
%A Zhang, Yu
%A Ye, Hongfei
%A Wang, Huiyang
%A Liu, Wenxi
%A Chen, Hongyang
%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 chen-etal-2026-parasuite
%X Logical reasoning is a key capability of large language models, yet current benchmarks focus almost entirely on tasks that just check basic logical consistency and overlook the reflective reasoning required for paradox detection and resolution. To fill the gap, we present ParaSuite, the first pipeline dedicated to paradox research that automates data synthesis, evaluation, and training. We introduce PARADOX, a synthetic, high-quality data spanning two difficulty tiers and three academic domains, accompanied by specialized evaluation metrics and solving algorithms. We propose ParadoxBreaker-7B, trained with Mutual-Information Guided Fine-Tuning and reinforcement learning step verify paradox reward(PAPO). Experiments demonstrate significant improvements in both paradoxical and general STEM reasoning.
%U https://aclanthology.org/2026.acl-long.2047/
%P 44246-44256
Markdown (Informal)
[ParaSuite: Boosting LLM Reasoning via Paradox Resolution](https://aclanthology.org/2026.acl-long.2047/) (Chen et al., ACL 2026)
ACL
- Bin Chen, Yu Zhang, Hongfei Ye, Huiyang Wang, Wenxi Liu, and Hongyang Chen. 2026. ParaSuite: Boosting LLM Reasoning via Paradox Resolution. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 44246–44256, San Diego, California, United States. Association for Computational Linguistics.