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A.~McEwen, S.~Byrne, C.~Hansen, I.~Daubar, S.~Sutton, C.~Dundas,
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  A.~Fennema, J.~Grant, V.~Gulick, K.~Herkenhoff, R.~Heyd, R.~Leis, L.~Ojha,
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M.~C. Malin, J.~F. Bell~III, B.~A. Cantor, M.~A. Caplinger, W.~M. Calvin, R.~T.
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\bibitem{Murchie07CRISM}
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\bibitem{Smith01MOLA}
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\bibitem{Wang26marsretrieval}
S.~Wang, Y.~Wang, and H.~Wei, ``Marsretrieval: Benchmarking vision-language
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\bibitem{Cai25LLM}
W.~Cai, J.~Jiang, F.~Wang, J.~Tang, S.~Kim, and J.~Huang, ``A survey on mixture
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\bibitem{Lewis20RAG}
P.~Lewis, E.~Perez, A.~Piktus, F.~Petroni, V.~Karpukhin, N.~Goyal,
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\bibitem{Zhou24hallucination}
Y.~Zhou, Z.~Liu, J.~Jin, J.-Y. Nie, and Z.~Dou, ``Metacognitive
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  New York, NY, USA: Association for Computing Machinery, 2024, p. 1453–1463.

\bibitem{Pan24KGandLLM}
S.~Pan, L.~Luo, Y.~Wang, C.~Chen, J.~Wang, and X.~Wu, ``Unifying large language
  models and knowledge graphs: A roadmap,'' \emph{IEEE Trans. on Knowl. and
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\bibitem{Wu25MultiRAG}
W.~Wu, H.~Wang, B.~Li, P.~Huang, X.~Zhao, and L.~Liang, ``Multirag: A
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\bibitem{Wang25Astute}
F.~Wang, X.~Wan, R.~Sun, J.~Chen, and S.~O. Arik, ``Astute {RAG}: Overcoming
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\bibitem{lien26hyperrag}
W.-S. Lien, Y.-K. Chan, H.-L. Hsiao, B.-K. Ruan, M.-F. Chiang, C.-A. Chen,
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  arXiv:2602.14470}, 2026.

\bibitem{luo25hyperrag}
H.~Luo, G.~Chen, Y.~Zheng, X.~Wu, Y.~Guo, Q.~Lin, Y.~Feng, Z.~Kuang, M.~Song,
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\bibitem{madhu26hyprag}
H.~Madhu, N.~Bui, A.~Maatouk, L.~Tassiulas, S.~Krishnaswamy, M.~Yang,
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\bibitem{liu26truthfulrag}
S.~Liu, Y.-M. Shang, and X.~Zhang, ``Truthfulrag: Resolving factual-level
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\bibitem{tang26diagnosing}
J.~Tang, K.~Wang, H.~Lu, H.~Chen, K.~Chen, Z.~Sun, Q.~Li, L.~Lyu, G.~Nan, and
  Z.~Zeng, ``Diagnosing knowledge conflict in multimodal long-chain
  reasoning,'' \emph{arXiv preprint arXiv:2602.14518}, 2026.

\bibitem{Li25SubGraphRAG}
M.~Li, S.~Miao, and P.~Li, ``Simple is effective: The roles of graphs and large
  language models in knowledge-graph-based retrieval-augmented generation,'' in
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  0.4em\relax OpenReview.net, 2025.

\bibitem{yang18hotpotqa}
Z.~Yang, P.~Qi, S.~Zhang, Y.~Bengio, W.~Cohen, R.~Salakhutdinov, and C.~D.
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  Empirical Methods in Natural Language Processing}, E.~Riloff, D.~Chiang,
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  Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018,
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\bibitem{ho202WikiMultiHopQA}
X.~Ho, A.-K. Duong~Nguyen, S.~Sugawara, and A.~Aizawa, ``Constructing a
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\bibitem{Harsh23IRCoT}
H.~Trivedi, N.~Balasubramanian, T.~Khot, and A.~Sabharwal, ``Interleaving
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  A.~Rogers, J.~Boyd-Graber, and N.~Okazaki, Eds.\hskip 1em plus 0.5em minus
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  2023, pp. 10\,014--10\,037.

\bibitem{Chan24RQRAG}
\BIBentryALTinterwordspacing
C.~Chan, C.~Xu, R.~Yuan, H.~Luo, W.~Xue, Y.~Guo, and J.~Fu, ``{RQ-RAG:}
  learning to refine queries for retrieval augmented generation,'' \emph{CoRR},
  vol. abs/2404.00610, 2024. [Online]. Available:
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\BIBentrySTDinterwordspacing

\bibitem{Zhou24MetaRAG}
Y.~Zhou, Z.~Liu, J.~Jin, J.-Y. Nie, and Z.~Dou, ``Metacognitive
  retrieval-augmented large language models,'' in \emph{Proceedings of the ACM
  Web Conference 2024}, ser. WWW '24.\hskip 1em plus 0.5em minus 0.4em\relax
  New York, NY, USA: Association for Computing Machinery, 2024, p. 1453–1463.

\end{thebibliography}
