--- title: "UniversalRAG — paper by Yeo et al. (2026) — summary and ideas" description: "The authors propose UniversalRAG, an any-to-any framework with separate corpora per modality and… 76 references, cited by 1 paper here." authors: ["Woongyeong Yeo", "Kangsan Kim", "Soyeong Jeong", "Jinheon Baek", "Sung Ju Hwang"] year: 2026 venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)" source: "https://aclanthology.org/2026.acl-long.177/" doi: "10.18653/v1/2026.acl-long.177" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/yeo-2026-universalrag-retrieval-augmented-generation-over/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/yeo-2026-universalrag-retrieval-augmented-generation-over/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/4/) / UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities # UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities **Authors:** Woongyeong Yeo, Kangsan Kim, [Soyeong Jeong](https://md.research.elamur.ai/free/en/authors/soyeong-jeong/), [Jinheon Baek](https://md.research.elamur.ai/free/en/authors/jinheon-baek/) and [Sung Ju Hwang](https://md.research.elamur.ai/free/en/authors/sung-ju-hwang/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.177/) · [PDF](https://aclanthology.org/2026.acl-long.177.pdf) · [DOI 10.18653/v1/2026.acl-long.177](https://doi.org/10.18653/v1/2026.acl-long.177) · [OpenAlex](https://openalex.org/W4417299405) **Citations:** 3 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/yeo-2026-universalrag-retrieval-augmented-generation-over/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary Most retrieval-augmented generation (RAG) systems search one corpus in one modality, although real questions may need text, images, video or a mix, at very different levels of detail. The authors propose UniversalRAG, an any-to-any framework with separate corpora per modality and granularity (paragraphs, documents, tables, images, short clips, full videos) plus a no-retrieval option. They show that placing everything in a shared multimodal embedding space creates a modality gap: text queries mostly fetch text, whatever is actually needed. Instead, a router picks the modality–granularity corpora (possibly several) that a query requires, and dedicated retrievers search only those. A formal proposition explains why this wins when modality bias is strong. Training-free and trained routers are both studied, the latter learning from automatically derived labels. On 10 benchmarks, UniversalRAG beats modality-specific and unified baselines on average, with better efficiency and out-of-distribution robustness. This is a step toward one assistant that works over heterogeneous knowledge bases. ## Key points - A t-SNE visualization of a unified multimodal embedding space shows items clustering by modality rather than by meaning. This motivates routing over separate corpora instead of a single aggregated index. - Proposition 1 models unified-space similarity as a modality-match bonus plus relevance plus noise. When the modality bonus outweighs variation in relevance, routing finds items of the needed modality more reliably. - Text and video are each split into two granularity levels: paragraph vs. full document, and short clip vs. full video. Images stay whole, and tables form their own corpus. - New modalities can be added by extending the router alone, without changing existing modality-specific retrievers. - Limitations: - No benchmark provides gold modality or granularity labels per query, so router training data is annotated automatically and may be noisy. - Human-annotated routing data and finer granularity levels are left for future work. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [Korea Advanced Institute of Science and Technology](https://md.research.elamur.ai/free/en/universities/korea-advanced-institute-of-science-and-technology/) — University, Daejeon, South Korea · 51 works · 3,167 citations 📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works 🧭 Topics (1): [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 473 works ## Cite Cite as: Woongyeong Yeo, Kangsan Kim, Soyeong Jeong, Jinheon Baek and Sung Ju Hwang. 2026. UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{yeo2026universalrag, title = {{UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities}}, author = {Woongyeong Yeo, Kangsan Kim, Soyeong Jeong, Jinheon Baek and Sung Ju Hwang}, booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, year = {2026}, doi = {10.18653/v1/2026.acl-long.177}, url = {https://doi.org/10.18653/v1/2026.acl-long.177} } ``` - [← previous Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities](https://md.research.elamur.ai/free/en/papers/oh-2026-uncertainty-quantification-llm-agents-foundations/) - [next → Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text](https://md.research.elamur.ai/free/en/papers/xu-2026-unlocking-implicit-experience-synthesizing-tool/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/4/) ## References 76 references, 40 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Mohammad Mahdi Abootorabi, Amirhosein Zobeiri, Mahdi Dehghani, Mohammadali Mohammadkhani, Bardia Mohammadi, Omid Ghahroodi, Mahdieh Soleymani Baghshah, and Ehsaneddin Asgari. 2025. Ask in any modality: A comprehensive survey on multimodal retrieval-augmented generation. In Findings of the Associatio… [find](https://scholar.google.com/scholar?q=Mohammad+Mahdi+Abootorabi%2C+Amirhosein+Zobeiri%2C+Mahdi+Dehghani%2C+Mohammadali+Mohammadkhani%2C+Bardia+Mohammadi%2C+Omid+Ghahroodi%2C+Mahdieh+Soleymani+Baghshah%2C+and+Ehsaneddin+Asgari%2E+2025%2E+Ask+in+any+modality) 2. Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023. [Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-vl-versatile-vision-language/). arXiv preprint arXiv:2308.12966. 3. Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, Wei Ding, Chang Gao, Chunjiang Ge, and 1 others. 2025. [Qwen3-vl technical report](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen3-vl-technical-report/). arXiv preprint arXiv:2511.21631. 4. Valeriia Bolotova-Baranova, Vladislav Blinov, Sofya Filippova, Falk Scholer, and Mark Sanderson. 2023. WikiHowQA: A comprehensive benchmark for multi-document non-factoid question answering. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pa… [find](https://scholar.google.com/scholar?q=Valeriia+Bolotova%2DBaranova%2C+Vladislav+Blinov%2C+Sofya+Filippova%2C+Falk+Scholer%2C+and+Mark+Sanderson%2E+2023%2E+WikiHowQA%3A+A+comprehensive+benchmark+for+multi%2Ddocument+non%2Dfactoid+question+answering%2E+In+Procee) 5. Yang Chen, Hexiang Hu, Yi Luan, Haitian Sun, Soravit Changpinyo, Alan Ritter, and Ming-Wei Chang. 2023a. [Can pre-trained vision and language models answer visual information-seeking questions?](https://md.research.elamur.ai/free/en/papers/chen-2023-can-pre-trained-vision-language/) In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 14948–1496… 6. Daniel Bolya, Po-Yao Huang, Peize Sun, Jang Hyun Cho, Andrea Madotto, Chen Wei, Tengyu Ma, Jiale Zhi, Jathushan Rajasegaran, Hanoona Abdul Rasheed, Junke Wang, Marco Monteiro, Hu Xu, Shiyu Dong, Nikhila Ravi, Shang-Wen Li, Piotr Dollar, and Christoph Feichtenhofer. 2025. Perception encoder: The best… [find](https://scholar.google.com/scholar?q=Daniel+Bolya%2C+Po%2DYao+Huang%2C+Peize+Sun%2C+Jang+Hyun+Cho%2C+Andrea+Madotto%2C+Chen+Wei%2C+Tengyu+Ma%2C+Jiale+Zhi%2C+Jathushan+Rajasegaran%2C+Hanoona+Abdul+Rasheed%2C+Junke+Wang%2C+Marco+Monteiro%2C+Hu+Xu%2C+Shiyu+Dong%2C+Nikhi) 7. Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, Bin Li, Ping Luo, Tong Lu, Yu Qiao, and Jifeng Dai. 2024c. [InternVL: scaling up vision foundation models and aligning for generic visual-linguistic tasks](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/). In IEEE/CVF Conference on Co… 8. Brandon Castellano. 2014. PySceneDetect. [find](https://scholar.google.com/scholar?q=Brandon+Castellano%2E+2014%2E+PySceneDetect%2E) 9. Yingshan Chang, Guihong Cao, Mridu Narang, Jianfeng Gao, Hisami Suzuki, and Yonatan Bisk. 2022. Webqa: Multihop and multimodal QA. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pages 16474–16483. IEEE. [find](https://scholar.google.com/scholar?q=Yingshan+Chang%2C+Guihong+Cao%2C+Mridu+Narang%2C+Jianfeng+Gao%2C+Hisami+Suzuki%2C+and+Yonatan+Bisk%2E+2022%2E+Webqa%3A+Multihop+and+multimodal+QA%2E+In+IEEE%2FCVF+Conference+on+Computer+Vision+and+Pattern+Recognition%2C+CV) 10. Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2024a. [Benchmarking large language models in retrieval-augmented generation](https://md.research.elamur.ai/free/en/papers/chen-2023-benchmarking-large-language-models-retrieval/). In Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenth… 11. Tong Chen, Hongwei Wang, Sihao Chen, Wenhao Yu, Kaixin Ma, Xinran Zhao, Hongming Zhang, and Dong Yu. 2024b. [Dense X retrieval: What retrieval granularity should we use?](https://md.research.elamur.ai/free/en/papers/chen-2024-dense-x-retrieval-what-retrieval/) In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15159–15177, Miami, Florida, USA.… 12. Wenhu Chen, Hexiang Hu, Xi Chen, Pat Verga, and William Cohen. 2022. [MuRAG: Multimodal retrieval-augmented generator for open question answering over images and text](https://md.research.elamur.ai/free/en/papers/chen-2022-murag-multimodal-retrieval-augmented-generator/). In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5558–5570, Abu Dhabi, United Arab Em… 13. Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Yang Wang. 2020. [HybridQA: A dataset of multi-hop question answering over tabular and textual data](https://md.research.elamur.ai/free/en/papers/chen-2020-hybridqa-dataset-multi-hop-question/). In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1026–1036, Online. Association for Computationa… 14. Zhiguo Chen, Xun Jiang, Xing Xu, Zuo Cao, Yijun Mo, and Heng Tao Shen. 2023b. Joint searching and grounding: Multi-granularity video content retrieval. In Proceedings of the 31st ACM International Conference on Multimedia, MM ’23, page 975–983, New York, NY, USA. Association for Computing Machinery. [find](https://scholar.google.com/scholar?q=Zhiguo+Chen%2C+Xun+Jiang%2C+Xing+Xu%2C+Zuo+Cao%2C+Yijun+Mo%2C+and+Heng+Tao+Shen%2E+2023b%2E+Joint+searching+and+grounding%3A+Multi%2Dgranularity+video+content+retrieval%2E+In+Proceedings+of+the+31st+ACM+International+Con) 15. Christopher Clark, Jieyu Zhang, Zixian Ma, Jae Sung Park, Mohammadreza Salehi, Rohun Tripathi, Sangho Lee, Zhongzheng Ren, Chris Dongjoo Kim, Yinuo Yang, and 1 others. 2026. Molmo2: Open weights and data for vision-language models with video understanding and grounding. arXiv preprint arXiv:2601.106… [find](https://scholar.google.com/scholar?q=Christopher+Clark%2C+Jieyu+Zhang%2C+Zixian+Ma%2C+Jae+Sung+Park%2C+Mohammadreza+Salehi%2C+Rohun+Tripathi%2C+Sangho+Lee%2C+Zhongzheng+Ren%2C+Chris+Dongjoo+Kim%2C+Yinuo+Yang%2C+and+1+others%2E+2026%2E+Molmo2%3A+Open+weights+and+d) 16. Wanqing Cui, Keping Bi, Jiafeng Guo, and Xueqi Cheng. 2024. MORE: Multi-mOdal REtrieval augmented generative commonsense reasoning. In Findings of the Association for Computational Linguistics: ACL 2024, pages 1178–1192, Bangkok, Thailand. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Wanqing+Cui%2C+Keping+Bi%2C+Jiafeng+Guo%2C+and+Xueqi+Cheng%2E+2024%2E+MORE%3A+Multi%2DmOdal+REtrieval+augmented+generative+commonsense+reasoning%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+20) 17. Hanxing Ding, Liang Pang, Zihao Wei, Huawei Shen, and Xueqi Cheng. 2025. Rowen: Adaptive retrieval-augmented generation for hallucination mitigation in llms. In Proceedings of the 2025 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific… [find](https://scholar.google.com/scholar?q=Hanxing+Ding%2C+Liang+Pang%2C+Zihao+Wei%2C+Huawei+Shen%2C+and+Xueqi+Cheng%2E+2025%2E+Rowen%3A+Adaptive+retrieval%2Daugmented+generation+for+hallucination+mitigation+in+llms%2E+In+Proceedings+of+the+2025+Annual+Internat) 18. Matthijs Douze, Alexandr Guzhva, Chengqi Deng, Jeff Johnson, Gergely Szilvasy, Pierre-Emmanuel Mazaré, Maria Lomeli, Lucas Hosseini, and Hervé Jégou. 2025. [The faiss library](https://md.research.elamur.ai/free/en/papers/douze-2024-faiss-library/). IEEE Transactions on Big Data, pages 1–17. 19. Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Caifeng Shan, and 2 others. 2025. [Video-mme: The first-ever comprehensive evaluati…](https://md.research.elamur.ai/free/en/papers/fu-2024-video-mme-first-ever-comprehensive/) 20. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2023. [Retrieval-augmented generation for large language models: A survey](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/). arXiv preprint arXiv:2312.10997. 21. Gemini Team. 2023. [Gemini: a family of highly capable multimodal models](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/). arXiv preprint arXiv:2312.11805. 22. Zirui Guo, Xubin Ren, Lingrui Xu, Jiahao Zhang, and Chao Huang. 2025. Rag-anything: All-in-one rag framework. arXiv preprint arXiv:2510.12323. — [arXiv:2510.12323](https://arxiv.org/abs/2510.12323) [find](https://scholar.google.com/scholar?q=Zirui+Guo%2C+Xubin+Ren%2C+Lingrui+Xu%2C+Jiahao+Zhang%2C+and+Chao+Huang%2E+2025%2E+Rag%2Danything%3A+All%2Din%2Done+rag+framework%2E+arXiv+preprint+arXiv%3A2510%2E12323%2E+%E2%80%94+%5BarXiv%3A2510%2E12323%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2510%2E12323%29) 23. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. [Measuring massive multitask language understanding](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/). In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. 24. Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020. [Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps](https://md.research.elamur.ai/free/en/papers/ho-2020-constructing-multi-hop-qa-dataset/). In Proceedings of the 28th International Conference on Computational Linguistics, pages 6609–6625, Barcelona, Spain (Online). International Comm… 25. Wenbo Hu, Jia-Chen Gu, Zi-Yi Dou, Mohsen Fayyaz, Pan Lu, Kai-Wei Chang, and Nanyun Peng. 2025. Mrag-bench: Vision-centric evaluation for retrieval-augmented multimodal models. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. [find](https://scholar.google.com/scholar?q=Wenbo+Hu%2C+Jia%2DChen+Gu%2C+Zi%2DYi+Dou%2C+Mohsen+Fayyaz%2C+Pan+Lu%2C+Kai%2DWei+Chang%2C+and+Nanyun+Peng%2E+2025%2E+Mrag%2Dbench%3A+Vision%2Dcentric+evaluation+for+retrieval%2Daugmented+multimodal+models%2E+In+The+Thirteenth+Intern) 26. Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2025. [A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/). ACM Trans. Inf. Syst., 43(2). 27. Soyeong Jeong, Kangsan Kim, Jinheon Baek, and Sung Ju Hwang. 2025. VideoRAG: Retrieval-augmented generation over video corpus. In Findings of the Association for Computational Linguistics: ACL 2025, pages 21278–21298, Vienna, Austria. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Soyeong+Jeong%2C+Kangsan+Kim%2C+Jinheon+Baek%2C+and+Sung+Ju+Hwang%2E+2025%2E+VideoRAG%3A+Retrieval%2Daugmented+generation+over+video+corpus%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+2025%2C+p) 28. Xingyu Ji, Aditya Parameswaran, and Madelon Hulsebos. 2024. TARGET: Benchmarking table retrieval for generative tasks. In NeurIPS 2024 Third Table Representation Learning Workshop. [find](https://scholar.google.com/scholar?q=Xingyu+Ji%2C+Aditya+Parameswaran%2C+and+Madelon+Hulsebos%2E+2024%2E+TARGET%3A+Benchmarking+table+retrieval+for+generative+tasks%2E+In+NeurIPS+2024+Third+Table+Representation+Learning+Workshop%2E) 29. Ting Jiang, Minghui Song, Zihan Zhang, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang, Deqing Wang, and Fuzhen Zhuang. 2024a. E5-v: Universal embeddings with multimodal large language models. arXiv preprint arXiv:2407.12580. — [arXiv:2407.12580](https://arxiv.org/abs/2407.12580) [find](https://scholar.google.com/scholar?q=Ting+Jiang%2C+Minghui+Song%2C+Zihan+Zhang%2C+Haizhen+Huang%2C+Weiwei+Deng%2C+Feng+Sun%2C+Qi+Zhang%2C+Deqing+Wang%2C+and+Fuzhen+Zhuang%2E+2024a%2E+E5%2Dv%3A+Universal+embeddings+with+multimodal+large+language+models%2E+arXiv+pr) 30. Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023. [Active retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/jiang-2023-active-retrieval-augmented-generation/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7969–7992, Singapore. Association fo… 31. Ziyan Jiang, Xueguang Ma, and Wenhu Chen. 2024b. Longrag: Enhancing retrieval-augmented generation with long-context llms. arXiv preprint arXiv:2406.15319. — [arXiv:2406.15319](https://arxiv.org/abs/2406.15319) [find](https://scholar.google.com/scholar?q=Ziyan+Jiang%2C+Xueguang+Ma%2C+and+Wenhu+Chen%2E+2024b%2E+Longrag%3A+Enhancing+retrieval%2Daugmented+generation+with+long%2Dcontext+llms%2E+arXiv+preprint+arXiv%3A2406%2E15319%2E+%E2%80%94+%5BarXiv%3A2406%2E15319%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2) 32. Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017. [TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1601–1611, Vancouver… 33. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. [Dense passage retrieval for open-domain question answering](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769–6781, Onli… 34. Shayekh Bin Islam, Md Asib Rahman, K S M Tozammel Hossain, Enamul Hoque, Shafiq Joty, and Md Rizwan Parvez. 2024. Open-RAG: Enhanced retrieval augmented reasoning with open-source large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 14231– 14244, Mia… [find](https://scholar.google.com/scholar?q=Shayekh+Bin+Islam%2C+Md+Asib+Rahman%2C+K+S+M+Tozammel+Hossain%2C+Enamul+Hoque%2C+Shafiq+Joty%2C+and+Md+Rizwan+Parvez%2E+2024%2E+Open%2DRAG%3A+Enhanced+retrieval+augmented+reasoning+with+open%2Dsource+large+language+model) 35. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. [Natural…](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/) 36. Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, and Jong Park. 2024. [Adaptive-RAG: Learning to adapt retrieval-augmented large language models through question complexity](https://md.research.elamur.ai/free/en/papers/jeong-2024-adaptive-rag-learning-adapt-retrieval/). In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Hu… 37. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020. [Retrieval-augmented generation for knowledge-intensive NLP tasks](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). In Advances in Neural Information Proc… 38. Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. 2025a. [LLaVA-onevision: Easy visual task transfer](https://md.research.elamur.ai/free/en/papers/li-2024-llava-onevision-easy-visual-task/). Transactions on Machine Learning Research. 39. Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Lou, Limin Wang, and Yu Qiao. 2024. [Mvbench: A comprehensive multi-modal video understanding benchmark](https://md.research.elamur.ai/free/en/papers/li-2023-mvbench-comprehensive-multi-modal-video/). In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, Jun… 40. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023. [Visual instruction tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-visual-instruction-tuning/). In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023. 41. Pei Liu, Xin Liu, Ruoyu Yao, Junming Liu, Siyuan Meng, Ding Wang, and Jun Ma. 2025a. Hm-rag: Hierarchical multi-agent multimodal retrieval augmented generation. In Proceedings of the 33rd ACM International Conference on Multimedia, MM ’25, page 2781–2790, New York, NY, USA. Association for Computing… [find](https://scholar.google.com/scholar?q=Pei+Liu%2C+Xin+Liu%2C+Ruoyu+Yao%2C+Junming+Liu%2C+Siyuan+Meng%2C+Ding+Wang%2C+and+Jun+Ma%2E+2025a%2E+Hm%2Drag%3A+Hierarchical+multi%2Dagent+multimodal+retrieval+augmented+generation%2E+In+Proceedings+of+the+33rd+ACM+Internat) 42. Mingxin Li, Yanzhao Zhang, Dingkun Long, Keqin Chen, Sibo Song, Shuai Bai, Zhibo Yang, Pengjun Xie, An Yang, Dayiheng Liu, and 1 others. 2026. Qwen3-vl-embedding and qwen3-vl-reranker: A unified framework for state-of-the-art multimodal retrieval and ranking. arXiv preprint arXiv:2601.04720. — [arXiv:2601.04720](https://arxiv.org/abs/2601.04720) [find](https://scholar.google.com/scholar?q=Mingxin+Li%2C+Yanzhao+Zhang%2C+Dingkun+Long%2C+Keqin+Chen%2C+Sibo+Song%2C+Shuai+Bai%2C+Zhibo+Yang%2C+Pengjun+Xie%2C+An+Yang%2C+Dayiheng+Liu%2C+and+1+others%2E+2026%2E+Qwen3%2Dvl%2Dembedding+and+qwen3%2Dvl%2Dreranker%3A+A+unified+frame) 43. Zuhong Liu, Charles-Elie Simon, and Fabien Caspani. 2025b. Passage segmentation of documents for extractive question answering. In Advances in Information Retrieval: 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6–10, 2025, Proceedings, Part III, page 345–352, Ber… [find](https://scholar.google.com/scholar?q=Zuhong+Liu%2C+Charles%2DElie+Simon%2C+and+Fabien+Caspani%2E+2025b%2E+Passage+segmentation+of+documents+for+extractive+question+answering%2E+In+Advances+in+Information+Retrieval%3A+47th+European+Conference+on+Inform) 44. Yangning Li, Yinghui Li, Xinyu Wang, Zhen Zhang, Xinran Zheng, Yong Jiang, Hui Wang, Hai-Tao Zheng, Fei Huang, and Jingren Zhou. 2025b. Benchmarking multimodal retrieval augmented generation with dynamic VQA dataset and self-adaptive planning agent. In AAAI 2025 Workshop on Preventing and Detecting… [find](https://scholar.google.com/scholar?q=Yangning+Li%2C+Yinghui+Li%2C+Xinyu+Wang%2C+Zhen+Zhang%2C+Xinran+Zheng%2C+Yong+Jiang%2C+Hui+Wang%2C+Hai%2DTao+Zheng%2C+Fei+Huang%2C+and+Jingren+Zhou%2E+2025b%2E+Benchmarking+multimodal+retrieval+augmented+generation+with+dyna) 45. Andrés Marafioti, Orr Zohar, Miquel Farré, Merve Noyan, Elie Bakouch, Pedro Cuenca, Cyril Zakka, Loubna Ben Allal, Anton Lozhkov, Nouamane Tazi, and 1 others. 2025. Smolvlm: Redefining small and efficient multimodal models. arXiv preprint arXiv:2504.05299. [find](https://scholar.google.com/scholar?q=Andr%C3%A9s+Marafioti%2C+Orr+Zohar%2C+Miquel+Farr%C3%A9%2C+Merve+Noyan%2C+Elie+Bakouch%2C+Pedro+Cuenca%2C+Cyril+Zakka%2C+Loubna+Ben+Allal%2C+Anton+Lozhkov%2C+Nouamane+Tazi%2C+and+1+others%2E+2025%2E+Smolvlm%3A+Redefining+small+and+effic) 46. Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, and James Y. Zou. 2022. Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, N… [find](https://scholar.google.com/scholar?q=Weixin+Liang%2C+Yuhui+Zhang%2C+Yongchan+Kwon%2C+Serena+Yeung%2C+and+James+Y%2E+Zou%2E+2022%2E+Mind+the+gap%3A+Understanding+the+modality+gap+in+multi%2Dmodal+contrastive+representation+learning%2E+In+Advances+in+Neural+I) 47. Chin-Yew Lin. 2004. [ROUGE: A package for automatic evaluation of summaries](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/). In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics. 48. Sheng-Chieh Lin, Chankyu Lee, Mohammad Shoeybi, Jimmy Lin, Bryan Catanzaro, and Wei Ping. 2025. Mm-embed: Universal multimodal retrieval with multimodal LLMS. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. [find](https://scholar.google.com/scholar?q=Sheng%2DChieh+Lin%2C+Chankyu+Lee%2C+Mohammad+Shoeybi%2C+Jimmy+Lin%2C+Bryan+Catanzaro%2C+and+Wei+Ping%2E+2025%2E+Mm%2Dembed%3A+Universal+multimodal+retrieval+with+multimodal+LLMS%2E+In+The+Thirteenth+International+Conferenc) 49. Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. [TruthfulQA: Measuring how models mimic human falsehoods](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3214–3252, Dublin, Ireland. Association for Computational Linguistics… 50. Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. 2024. [Improved baselines with visual instruction tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-improved-baselines-visual-instruction-tuning/). In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024, pages 26286–26296. IEEE. 51. Minesh Mathew, Dimosthenis Karatzas, and C. V. Jawahar. 2021. [Docvqa: A dataset for VQA on document images](https://md.research.elamur.ai/free/en/papers/mathew-2020-docvqa-dataset-vqa-document-images/). In IEEE Winter Conference on Applications of Computer Vision, WACV 2021, Waikoloa, HI, USA, January 3-8, 2021, pages 2199–2208. IEEE. 52. Rui Meng, Ziyan Jiang, Ye Liu, Mingyi Su, Xinyi Yang, Yuepeng Fu, Can Qin, Raghuveer Thirukovalluru, Xuan Zhang, Zeyuan Chen, Ran Xu, Caiming Xiong, Yingbo Zhou, Wenhu Chen, and Semih Yavuz. 2026. VLM2vec-v2: Advancing multimodal embedding for videos, images, and visual documents. Transactions on Ma… [find](https://scholar.google.com/scholar?q=Rui+Meng%2C+Ziyan+Jiang%2C+Ye+Liu%2C+Mingyi+Su%2C+Xinyi+Yang%2C+Yuepeng+Fu%2C+Can+Qin%2C+Raghuveer+Thirukovalluru%2C+Xuan+Zhang%2C+Zeyuan+Chen%2C+Ran+Xu%2C+Caiming+Xiong%2C+Yingbo+Zhou%2C+Wenhu+Chen%2C+and+Semih+Yavuz%2E+2026%2E+VLM) 53. Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. 2019. Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 2… [find](https://scholar.google.com/scholar?q=Antoine+Miech%2C+Dimitri+Zhukov%2C+Jean%2DBaptiste+Alayrac%2C+Makarand+Tapaswi%2C+Ivan+Laptev%2C+and+Josef+Sivic%2E+2019%2E+Howto100m%3A+Learning+a+text%2Dvideo+embedding+by+watching+hundred+million+narrated+video+clips%2E) 54. OpenAI. 2024. [Gpt-4o system card](https://md.research.elamur.ai/free/en/papers/openai-2024-gpt-4o-system-card/). arXiv preprint arXiv:2410.21276. 55. OpenAI. 2025. [Openai gpt-5 system card](https://md.research.elamur.ai/free/en/papers/singh-2025-openai-gpt-5-system-card/). preprint arXiv:2601.03267. arXiv Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning transferable visual models… 56. July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 8748–8763. PMLR. [find](https://scholar.google.com/scholar?q=July+2021%2C+Virtual+Event%2C+volume+139+of+Proceedings+of+Machine+Learning+Research%2C+pages+8748%E2%80%938763%2E+PMLR%2E) 57. Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling. 2024. [Corrective retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/yan-2024-corrective-retrieval-augmented-generation/). arXiv preprint arXiv:2401.15884. 58. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. [SQuAD: 100,000+ questions for machine comprehension of text](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/). In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383–2392, Austin, Texas. Association for Computational Linguistics. 59. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. [HotpotQA: A dataset for diverse, explainable multi-hop question answering](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/). In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2369… 60. Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023. [In-context retrieval-augmented language models](https://md.research.elamur.ai/free/en/papers/ram-2023-context-retrieval-augmented-language-models/). Transactions of the Association for Computational Linguistics, 11:1316–1331. 61. Ruchit Rawal, Khalid Saifullah, Ronen Basri, David Jacobs, Gowthami Somepalli, and Tom Goldstein. 2025. CinePile: A long video question answering dataset and benchmark. In Workshop on Video-Language Models @ NeurIPS 2024. [find](https://scholar.google.com/scholar?q=Ruchit+Rawal%2C+Khalid+Saifullah%2C+Ronen+Basri%2C+David+Jacobs%2C+Gowthami+Somepalli%2C+and+Tom+Goldstein%2E+2025%2E+CinePile%3A+A+long+video+question+answering+dataset+and+benchmark%2E+In+Workshop+on+Video%2DLanguage+M) 62. Rotem Shalev-Arkushin, Rinon Gal, Amit H Bermano, and Ohad Fried. 2026. Imagerag: Dynamic image retrieval for reference-guided image generation. In The Fourteenth International Conference on Learning Representations, ICLR 2026, Rio de Janeiro, Brazil, April 23-27, 2026. [find](https://scholar.google.com/scholar?q=Rotem+Shalev%2DArkushin%2C+Rinon+Gal%2C+Amit+H+Bermano%2C+and+Ohad+Fried%2E+2026%2E+Imagerag%3A+Dynamic+image+retrieval+for+reference%2Dguided+image+generation%2E+In+The+Fourteenth+International+Conference+on+Learning+) 63. Sahel Sharifymoghaddam, Shivani Upadhyay, Wenhu Chen, and Jimmy Lin. 2025. UniRAG: Universal retrieval augmentation for large vision language models. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 2026–2039, Albuquerque, New Mexico. Association for Computational Ling… [find](https://scholar.google.com/scholar?q=Sahel+Sharifymoghaddam%2C+Shivani+Upadhyay%2C+Wenhu+Chen%2C+and+Jimmy+Lin%2E+2025%2E+UniRAG%3A+Universal+retrieval+augmentation+for+large+vision+language+models%2E+In+Findings+of+the+Association+for+Computational+L) 64. Xiaqiang Tang, Qiang Gao, Jian Li, Nan Du, Qi Li, and Sihong Xie. 2025. MBA-RAG: a bandit approach for adaptive retrieval-augmented generation through question complexity. In Proceedings of the 31st International Conference on Computational Linguistics, pages 3248–3254, Abu Dhabi, UAE. Association f… [find](https://scholar.google.com/scholar?q=Xiaqiang+Tang%2C+Qiang+Gao%2C+Jian+Li%2C+Nan+Du%2C+Qi+Li%2C+and+Sihong+Xie%2E+2025%2E+MBA%2DRAG%3A+a+bandit+approach+for+adaptive+retrieval%2Daugmented+generation+through+question+complexity%2E+In+Proceedings+of+the+31st+I) 65. Weihan Wang, Zehai He, Wenyi Hong, Yean Cheng, Xiaohan Zhang, Ji Qi, Ming Ding, Xiaotao Gu, Shiyu Huang, Bin Xu, Yuxiao Dong, and Jie Tang. 2025a. Lvbench: An extreme long video understanding benchmark. In IEEE/CVF International Conference on Computer Vision, ICCV 2025, Honolulu, Hawaii, USA, Octobe… [find](https://scholar.google.com/scholar?q=Weihan+Wang%2C+Zehai+He%2C+Wenyi+Hong%2C+Yean+Cheng%2C+Xiaohan+Zhang%2C+Ji+Qi%2C+Ming+Ding%2C+Xiaotao+Gu%2C+Shiyu+Huang%2C+Bin+Xu%2C+Yuxiao+Dong%2C+and+Jie+Tang%2E+2025a%2E+Lvbench%3A+An+extreme+long+video+understanding+benchmar) 66. Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, and 1 others. 2025b. [Internvl3](https://md.research.elamur.ai/free/en/papers/wang-2025-internvl3-5-advancing-open/). 5: Advancing open-source multimodal models in versatility, reasoning, and efficiency. arXiv preprint arXiv:2508.18265. 67. Yin Wu, Quanyu Long, Jing Li, Jianfei Yu, and Wenya Wang. 2025. Visual-rag: Benchmarking text-to-image retrieval augmented generation for visual knowledge intensive queries. arXiv preprint arXiv:2502.16636. — [arXiv:2502.16636](https://arxiv.org/abs/2502.16636) [find](https://scholar.google.com/scholar?q=Yin+Wu%2C+Quanyu+Long%2C+Jing+Li%2C+Jianfei+Yu%2C+and+Wenya+Wang%2E+2025%2E+Visual%2Drag%3A+Benchmarking+text%2Dto%2Dimage+retrieval+augmented+generation+for+visual+knowledge+intensive+queries%2E+arXiv+preprint+arXiv%3A2502%2E) 68. Zijun Yao, Weijian Qi, Liangming Pan, Shulin Cao, Linmei Hu, Liu Weichuan, Lei Hou, and Juanzi Li. 2025. [SeaKR: Self-aware knowledge retrieval for adaptive retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/yao-2025-seakr-self-aware-knowledge-retrieval/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Paper… 69. Xiang Yue, Yuansheng Ni, Tianyu Zheng, Kai Zhang, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang, and 3 others. 2024. [MMMU: A massive multi-discipline multimodal u…](https://md.research.elamur.ai/free/en/papers/yue-2023-mmmu-massive-multi-discipline-multimodal/) 70. Biao Zhang, Paul Suganthan, Gaël Liu, Ilya Philip-pov, Sahil Dua, Ben Hora, Kat Black, Gus Martins, Omar Sanseviero, Shreya Pathak, and 1 others. 2025a. T5gemma 2: Seeing, reading, and understanding longer. arXiv preprint arXiv:2512.14856. — [arXiv:2512.14856](https://arxiv.org/abs/2512.14856) [find](https://scholar.google.com/scholar?q=Biao+Zhang%2C+Paul+Suganthan%2C+Ga%C3%ABl+Liu%2C+Ilya+Philip%2Dpov%2C+Sahil+Dua%2C+Ben+Hora%2C+Kat+Black%2C+Gus+Martins%2C+Omar+Sanseviero%2C+Shreya+Pathak%2C+and+1+others%2E+2025a%2E+T5gemma+2%3A+Seeing%2C+reading%2C+and+understanding+l) 71. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020. BERTScore: Evaluating text generation with BERT. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. [find](https://scholar.google.com/scholar?q=Tianyi+Zhang%2C+Varsha+Kishore%2C+Felix+Wu%2C+Kilian+Q%2E+Weinberger%2C+and+Yoav+Artzi%2E+2020%2E+BERTScore%3A+Evaluating+text+generation+with+BERT%2E+In+8th+International+Conference+on+Learning+Representations%2C+ICLR+2) 72. Xin Zhang, Yanzhao Zhang, Wen Xie, Mingxin Li, Ziqi Dai, Dingkun Long, Pengjun Xie, Meishan Zhang, Wenjie Li, and Min Zhang. 2025b. Bridging modalities: Improving universal multimodal retrieval by multimodal large language models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CV… [find](https://scholar.google.com/scholar?q=Xin+Zhang%2C+Yanzhao+Zhang%2C+Wen+Xie%2C+Mingxin+Li%2C+Ziqi+Dai%2C+Dingkun+Long%2C+Pengjun+Xie%2C+Meishan+Zhang%2C+Wenjie+Li%2C+and+Min+Zhang%2E+2025b%2E+Bridging+modalities%3A+Improving+universal+multimodal+retrieval+by+mul) 73. Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, and 1 others. 2025c. [Qwen3 embedding: Advancing text embedding and reranking through foundation models](https://md.research.elamur.ai/free/en/papers/zhang-2025-qwen3-embedding-advancing-text/). arXiv preprint arXiv:2506.05176. 74. Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, and Shuming Shi. 2025d. [Siren’s song in the ai ocean: A survey on hallucination in large language models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/). Computational Linguistics, pages… 75. Zihan Zhang, Meng Fang, and Ling Chen. 2024. RetrievalQA: Assessing adaptive retrieval-augmented generation for short-form open-domain question answering. In Findings of the Association for Computational Linguistics: ACL 2024, pages 6963–6975, Bangkok, Thailand. Association for Computational Linguis… [find](https://scholar.google.com/scholar?q=Zihan+Zhang%2C+Meng+Fang%2C+and+Ling+Chen%2E+2024%2E+RetrievalQA%3A+Assessing+adaptive+retrieval%2Daugmented+generation+for+short%2Dform+open%2Ddomain+question+answering%2E+In+Findings+of+the+Association+for+Computatio) 76. Zijie Zhong, Hanwen Liu, Xiaoya Cui, Xiaofan Zhang, and Zengchang Qin. 2025. [Mix-of-granularity: Optimize the chunking granularity for retrieval-augmented generation](https://md.research.elamur.ai/free/en/papers/zhong-2025-mix-granularity-optimize-chunking-granularity/). In Proceedings of the 31st International Conference on Computational Linguistics, pages 5756–5774, Abu Dhabi, UAE. Association for Co… *Updated 31 October 2026*