mlsanityData to 14 Oct 2026

Enhancing Retrieval Augmentation via Adversarial Collaboration

arXiv 2509.14750 ↗First posted 18 Sep 2025cs.AIcited by 1

On the map: Retrieval-augmented generation → Privacy and security of RAG

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation. Builds on 23 of its 27 references that are in the map. First posted on arXiv 18 Sep 2025.

Builds on

23 of 27 references.

References · 23

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
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  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  5. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  6. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  7. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  8. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
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  11. 2022MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answeringcited by 543
  12. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
11 more
  1. 2024Benchmarking Retrieval-Augmented Generation for Medicinecited by 331
  2. 2023MEDITRON-70B: Scaling Medical Pretraining for Large Language Modelscited by 274
  3. 2024Corrective Retrieval Augmented Generationcited by 197
  4. 2023MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrievalcited by 185
  5. 2020Open Problems in Cooperative AIcited by 168
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  7. 2024SaulLM-7B: A pioneering Large Language Model for Lawcited by 142
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  10. 2023Thread of Thought Unraveling Chaotic Contextscited by 72
  11. 2023SAIL: Search-Augmented Instruction Learningcited by 33

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025FVA-RAG: Falsification-Verification Alignment for Mitigating Sycophantic Hallucinationscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  2. 2024The Power of Noise: Redefining Retrieval for RAG Systems
  3. 2024Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training
  4. 2024Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks
  5. 2024Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applications
  6. 2025Beyond Component Strength: Synergistic Integration and Adaptive Calibration in Multi-Agent RAG Systems
  7. 2024DuetRAG: Collaborative Retrieval-Augmented Generation
  8. 2025AIP: Subverting Retrieval-Augmented Generation via Adversarial Instructional Prompt