---
title: "Who cites Retrieval-augmented Multi-modal — 10 citing papers"
description: "10 papers of the Elamur Research library cite «Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models»…"
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---
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# Who cites «Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models»

10 papers of our library cite it (of the 149,439 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models»](https://md.research.elamur.ai/free/en/papers/liu-2023-retrieval-augmented-multi-modal-chain/)

1. **[FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/zhang-2025-faithfulrag-fact-level-conflict-modeling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qinggang Zhang, Zhishang Xiang, Yilin Xiao, et al.*2025 · 27 citations*
2. **[Progressive Multimodal Reasoning via Active Retrieval](https://md.research.elamur.ai/free/en/papers/dong-2025-progressive-multimodal-reasoning-active-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanting Dong, Chenghao Zhang, Mengjie Deng, et al.*2025 · 10 citations*
3. **[IREASONER: Trajectory-Aware Intrinsic Reasoning Supervision for Self-Evolving Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/sunil-2026-ireasoner-trajectory-aware-intrinsic-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Meghana Sunil, Manikandarajan Venmathimaran, Muthu Subash Kavitha*2026 · 2 citations*

7 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/liu-2023-retrieval-augmented-multi-modal-chain/) — Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models