@inproceedings{yang-etal-2026-sparc,
title = "{SPARC}-{RAG}: Adaptive Sequential{--}Parallel Scaling with Context Management for Retrieval-Augmented Generation",
author = {Yang, Yuxin and
Deng, Gangda and
Akg{\"u}l, {\"O}mer Faruk and
Chitsazan, Nima and
Govilkar, Yash and
Tigalappanavara, Akasha and
Zhang, Shi-Xiong and
Sahu, Sambit and
Prasanna, Viktor},
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1865/",
pages = "42480--42508",
ISBN = "979-8-89176-459-0",
abstract = "Retrieval-Augmented Generation (RAG) grounds large language model outputs in external evidence, but remains challenged on multi-hop question answering that requires long reasoning. Recent works scale RAG at inference time along two complementary dimensions: sequential depth for iterative refinement and parallel width for coverage expansion. However, naive scaling causes context contamination and scaling inefficiency, leading to diminishing or negative returns despite increased computation. To address these limitations, we propose SPARC-RAG, a framework that coordinates joint depth{--}width scaling around a unified context management mechanism{---}a shared global context state on which specialized control agents regulate parallel exploration and sequential stopping. To optimize scaling behavior, we further introduce a lightweight fine-tuning method with process-level verifiable preferences, which improves the efficiency of sequential scaling and effectiveness of parallel scaling. Across five single- and multi-hop QA benchmarks, SPARC-RAG outperforms the strongest reproduced baseline by an average of 3.8 F1 points at 7B and 9.8 at 32B, with the largest gains on challenging multi-hop tasks."
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<abstract>Retrieval-Augmented Generation (RAG) grounds large language model outputs in external evidence, but remains challenged on multi-hop question answering that requires long reasoning. Recent works scale RAG at inference time along two complementary dimensions: sequential depth for iterative refinement and parallel width for coverage expansion. However, naive scaling causes context contamination and scaling inefficiency, leading to diminishing or negative returns despite increased computation. To address these limitations, we propose SPARC-RAG, a framework that coordinates joint depth–width scaling around a unified context management mechanism—a shared global context state on which specialized control agents regulate parallel exploration and sequential stopping. To optimize scaling behavior, we further introduce a lightweight fine-tuning method with process-level verifiable preferences, which improves the efficiency of sequential scaling and effectiveness of parallel scaling. Across five single- and multi-hop QA benchmarks, SPARC-RAG outperforms the strongest reproduced baseline by an average of 3.8 F1 points at 7B and 9.8 at 32B, with the largest gains on challenging multi-hop tasks.</abstract>
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%0 Conference Proceedings
%T SPARC-RAG: Adaptive Sequential–Parallel Scaling with Context Management for Retrieval-Augmented Generation
%A Yang, Yuxin
%A Deng, Gangda
%A Akgül, Ömer Faruk
%A Chitsazan, Nima
%A Govilkar, Yash
%A Tigalappanavara, Akasha
%A Zhang, Shi-Xiong
%A Sahu, Sambit
%A Prasanna, Viktor
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yang-etal-2026-sparc
%X Retrieval-Augmented Generation (RAG) grounds large language model outputs in external evidence, but remains challenged on multi-hop question answering that requires long reasoning. Recent works scale RAG at inference time along two complementary dimensions: sequential depth for iterative refinement and parallel width for coverage expansion. However, naive scaling causes context contamination and scaling inefficiency, leading to diminishing or negative returns despite increased computation. To address these limitations, we propose SPARC-RAG, a framework that coordinates joint depth–width scaling around a unified context management mechanism—a shared global context state on which specialized control agents regulate parallel exploration and sequential stopping. To optimize scaling behavior, we further introduce a lightweight fine-tuning method with process-level verifiable preferences, which improves the efficiency of sequential scaling and effectiveness of parallel scaling. Across five single- and multi-hop QA benchmarks, SPARC-RAG outperforms the strongest reproduced baseline by an average of 3.8 F1 points at 7B and 9.8 at 32B, with the largest gains on challenging multi-hop tasks.
%U https://aclanthology.org/2026.emnlp-main.1865/
%P 42480-42508
Markdown (Informal)
[SPARC-RAG: Adaptive Sequential–Parallel Scaling with Context Management for Retrieval-Augmented Generation](https://aclanthology.org/2026.emnlp-main.1865/) (Yang et al., EMNLP 2026)
ACL
- Yuxin Yang, Gangda Deng, Ömer Faruk Akgül, Nima Chitsazan, Yash Govilkar, Akasha Tigalappanavara, Shi-Xiong Zhang, Sambit Sahu, and Viktor Prasanna. 2026. SPARC-RAG: Adaptive Sequential–Parallel Scaling with Context Management for Retrieval-Augmented Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 42480–42508, Budapest, Hungary. Association for Computational Linguistics.