@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T ReflectiveRAG: Rethinking Adaptivity in Retrieval-Augmented Generation
%A Verma, Akshay
%A Gupta, Swapnil
%A Pillai, Siddharth
%A Sircar, Prateek
%A Gupta, Deepak
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F verma-etal-2026-reflectiverag
%X 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.
%U https://aclanthology.org/2026.eacl-industry.27/
%P 377-384
Markdown (Informal)
[ReflectiveRAG: Rethinking Adaptivity in Retrieval-Augmented Generation](https://aclanthology.org/2026.eacl-industry.27/) (Verma et al., EACL 2026)
ACL
- Akshay Verma, Swapnil Gupta, Siddharth Pillai, Prateek Sircar, and Deepak Gupta. 2026. ReflectiveRAG: Rethinking Adaptivity in Retrieval-Augmented Generation. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track), pages 377–384, Rabat, Morocco. Association for Computational Linguistics.