Enhancing Retrieval Augmentation via Adversarial Collaboration

• URL: http://arxiv.org/abs/2509.14750v1
• Date: Thu, 18 Sep 2025 08:54:20 GMT
• Title: Enhancing Retrieval Augmentation via Adversarial Collaboration
• Authors: Letian Zhang, Guanghao Meng, Xudong Ren, Yiming Wang, Shu-Tao Xia,
• Abstract summary: We propose the Adrial Collaboration RAG (AC-RAG) framework to address "Retrieval Hallucinations"<n>AC-RAG employs two heterogeneous agents: a generalist Detector that identifies knowledge gaps, and a domain-specialized Resolver that provides precise solutions.<n>Experiments show that AC-RAG significantly improves retrieval accuracy and outperforms state-of-the-art RAG methods across various vertical domains.
• Score: 50.117273835877334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented Generation (RAG) is a prevalent approach for domain-specific LLMs, yet it is often plagued by "Retrieval Hallucinations"--a phenomenon where fine-tuned models fail to recognize and act upon poor-quality retrieved documents, thus undermining performance. To address this, we propose the Adversarial Collaboration RAG (AC-RAG) framework. AC-RAG employs two heterogeneous agents: a generalist Detector that identifies knowledge gaps, and a domain-specialized Resolver that provides precise solutions. Guided by a moderator, these agents engage in an adversarial collaboration, where the Detector's persistent questioning challenges the Resolver's expertise. This dynamic process allows for iterative problem dissection and refined knowledge retrieval. Extensive experiments show that AC-RAG significantly improves retrieval accuracy and outperforms state-of-the-art RAG methods across various vertical domains.

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