{"type":"ScholarlyArticle","title":"ARMOR: Adaptive Curriculum Meta-Learning for Noise-Robust RAG Reasoning","authors":[{"name":"Yan Wang","slug":"yan-wang","given":"Yan","family":"Wang","affiliation":""},{"name":"Yuxin Zhang","slug":"yuxin-zhang","given":"Yuxin","family":"Zhang","affiliation":""},{"name":"Shenyu Zhang","slug":"shenyu-zhang","given":"Shenyu","family":"Zhang","affiliation":""},{"name":"Yongrui Chen","slug":"yongrui-chen","given":"Yongrui","family":"Chen","affiliation":""},{"name":"Sheng Bi","slug":"sheng-bi","given":"Sheng","family":"Bi","affiliation":""},{"name":"Guilin Qi","slug":"guilin-qi","given":"Guilin","family":"Qi","affiliation":""}],"abstract":"Retrieval-Augmented Generation (RAG) systems have demonstrated remarkable effectiveness in mitigating hallucinations by incorporating external knowledge. However, the retrieval process inevitably introduces noise, posing significant challenges to RAG robustness. Fundamentally, noise robustness is a composite competency encompassing noise identification, retrieval filtering, and robust reasoning. Unfortunately, existing methods often fail to capture this holistic nature, typically restricted to single-dimensional enhancements or hampered by noise-induced optimization interference. To address these limitations, we decompose RAG robustness into three sub-tasks: knowledge boundary perception, retrieval verification, and robust reasoning. Accordingly, we propose ARMOR, an Adaptive Curriculum Meta-Learning for Noise-Robust RAG Reasoning. Inspired by the teaching process, ARMOR introduces an adaptive task scheduler as a \"Teacher\" to curate training plans according to the absolute learning progress of the model (\"student\"). Distinct from GRPO, ARMOR utilizes inter-group advantage estimation during learning to effectively mitigate mode collapse caused by limited output spaces. Experiments on three noise-injected complex reasoning datasets (2WikiMultiHopQA, MusiQue, and HotPotQA) indicate that ARMOR achieves synergistic improvements in retrieval noise robustness, significantly outperforming baselines in output quality.","doi":"10.24963/ijcai.2026/663","url":"https://ijcai.org/proceedings/2026/663","pdf":"https://ijcai.org/proceedings/2026/0663.pdf","markdown":"https://ijcai.org/proceedings/2026/663.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/663","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Natural Language Processing","track":"Main Track","number":663,"pages":{"first":5955,"last":5963},"keywords":["Natural Language Processing: Applications","Natural Language Processing: Question answering","Natural Language Processing: Information retrieval and text mining"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}