@inproceedings{verma-etal-2026-reflectiverag,
    title = "{R}eflective{RAG}: Rethinking Adaptivity in Retrieval-Augmented Generation",
    author = "Verma, Akshay  and
      Gupta, Swapnil  and
      Pillai, Siddharth  and
      Sircar, Prateek  and
      Gupta, Deepak",
    editor = {Matusevych, Yevgen  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en  and
      Aletras, Nikolaos},
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-industry.27/",
    pages = "377--384",
    ISBN = "979-8-89176-384-5",
    abstract = "Retrieval-Augmented Generation (RAG) systems degrade sharply under extreme noise,where irrelevant or redundant passages dominate. Current methods-fixed top-k retrieval, cross-encoder reranking, or policy based iteration-depend on static heuristics orcostly reinforcement learning, failing to assess evidence sufficiency, detect subtle mismatches, or reduce redundancy, leading to hallucinations and poor grounding. We introduce ReflectiveRAG, a lightweight yet reasoning-driven architecture that enhances factual grounding through two complementary mechanisms: Self-Reflective Retrieval (SRR) and Contrastive Noise Removal (NR). SRR employs small language model as a decision controller that iteratively evaluates evidence sufficiency, enabling adaptive query reformulation withoutfixed schedules or policy training. NR further refines retrieved content via embedding-based contrastive filtering, enforcing semanticsparsity and removing redundant or tangential passages. Evaluated on WebQuestions, HotpotQA (distractor setting) and InternalQAwith 50M Common Crawl distractors, ReflectiveRAG achieves substantial gains over strong baselines-including DeepRAG-improving EMby +2.7 pp and F1 by +2.5 pp, while reducing evidence redundancy by 30.88{\%} with only 18 ms additional latency. Ablation studies con-firm that SRR and NR jointly drive both factual accuracy and efficiency, validating our central claim that retrieval reasoning and contrastivefiltering can outperform large-scale policy optimization in RAG."
}