@inproceedings{xu-etal-2026-adversarial,
title = "Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models",
author = "Xu, Can and
Yan, Lingyong and
Wu, Jiayi and
Wang, Haosen and
Wang, Shuaiqiang and
Li, Yuchen and
Huang, Jizhou and
Yin, Dawei and
Li, Xiang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1868/",
pages = "37469--37480",
ISBN = "979-8-89176-395-1",
abstract = "Recent advances in synergizing large reasoning models (LRMs) with retrieval-augmented generation (RAG) have shown promising results, yet two critical challenges remain: (1) reasoning models typically operate from a single, unchallenged perspective, limiting their ability to conduct deep, self-correcting reasoning over external documents, and (2) existing training paradigms rely excessively on outcome-oriented rewards, which provide insufficient signal for shaping the complex, multi-step reasoning process. To address these issues, we propose an Reasoner-Verifier framework named Adversarial Reasoning RAG (ARR). The Reasoner and Verifier engage in reasoning on retrieved evidence and critiquing each other{'}s logic while being guided by process-aware advantage that requires no external scoring model. This reward combines explicit observational signals with internal model uncertainty to jointly optimize reasoning fidelity and verification rigor. Experiments on multiple benchmarks demonstrate the effectiveness of our method. Our code is available at [link](https://github.com/lakhfskn/anonymous-code-of-arr)."
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<abstract>Recent advances in synergizing large reasoning models (LRMs) with retrieval-augmented generation (RAG) have shown promising results, yet two critical challenges remain: (1) reasoning models typically operate from a single, unchallenged perspective, limiting their ability to conduct deep, self-correcting reasoning over external documents, and (2) existing training paradigms rely excessively on outcome-oriented rewards, which provide insufficient signal for shaping the complex, multi-step reasoning process. To address these issues, we propose an Reasoner-Verifier framework named Adversarial Reasoning RAG (ARR). The Reasoner and Verifier engage in reasoning on retrieved evidence and critiquing each other’s logic while being guided by process-aware advantage that requires no external scoring model. This reward combines explicit observational signals with internal model uncertainty to jointly optimize reasoning fidelity and verification rigor. Experiments on multiple benchmarks demonstrate the effectiveness of our method. Our code is available at [link](https://github.com/lakhfskn/anonymous-code-of-arr).</abstract>
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%0 Conference Proceedings
%T Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models
%A Xu, Can
%A Yan, Lingyong
%A Wu, Jiayi
%A Wang, Haosen
%A Wang, Shuaiqiang
%A Li, Yuchen
%A Huang, Jizhou
%A Yin, Dawei
%A Li, Xiang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F xu-etal-2026-adversarial
%X Recent advances in synergizing large reasoning models (LRMs) with retrieval-augmented generation (RAG) have shown promising results, yet two critical challenges remain: (1) reasoning models typically operate from a single, unchallenged perspective, limiting their ability to conduct deep, self-correcting reasoning over external documents, and (2) existing training paradigms rely excessively on outcome-oriented rewards, which provide insufficient signal for shaping the complex, multi-step reasoning process. To address these issues, we propose an Reasoner-Verifier framework named Adversarial Reasoning RAG (ARR). The Reasoner and Verifier engage in reasoning on retrieved evidence and critiquing each other’s logic while being guided by process-aware advantage that requires no external scoring model. This reward combines explicit observational signals with internal model uncertainty to jointly optimize reasoning fidelity and verification rigor. Experiments on multiple benchmarks demonstrate the effectiveness of our method. Our code is available at [link](https://github.com/lakhfskn/anonymous-code-of-arr).
%U https://aclanthology.org/2026.findings-acl.1868/
%P 37469-37480
Markdown (Informal)
[Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models](https://aclanthology.org/2026.findings-acl.1868/) (Xu et al., Findings 2026)
ACL
- Can Xu, Lingyong Yan, Jiayi Wu, Haosen Wang, Shuaiqiang Wang, Yuchen Li, Jizhou Huang, Dawei Yin, and Xiang Li. 2026. Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 37469–37480, San Diego, California, United States. Association for Computational Linguistics.