@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://preview.aclanthology.org/ingest-acl/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)."
}