---
title: "Who cites MANTIS: Interleaved Multi-Image Instruction Tuning"
description: "72 papers of the Elamur Research library cite «MANTIS: Interleaved Multi-Image Instruction Tuning», found in their bibliographies; the most…"
canonical: "https://research.elamur.ai/free/en/papers/jiang-2024-mantis-interleaved-multi-image-instruction/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [MANTIS: Interleaved Multi-Image Instruction Tuning](https://md.research.elamur.ai/free/en/papers/jiang-2024-mantis-interleaved-multi-image-instruction/) / cited by

# Who cites «MANTIS: Interleaved Multi-Image Instruction Tuning»

72 papers of our library cite it (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · [← back to «MANTIS: Interleaved Multi-Image Instruction Tuning»](https://md.research.elamur.ai/free/en/papers/jiang-2024-mantis-interleaved-multi-image-instruction/)

1. [**LLaVA-OneVision: Easy Visual Task Transfer arXiv:2408.03326**Bo Li, Yuanhan Zhang, Dong Guo, et al.*2024 · 477 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llava-onevision-easy-visual-task/)
2. [**Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling arXiv:2412.05271**Zhe Chen, Weiyun Wang, Yue Cao, et al.*2024 · 287 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-expanding-performance-boundaries-open/)
3. [**InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models arXiv:2504.10479**Jinguo Zhu, Weiyun Wang, Zhe Chen, et al.*2025 · 216 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-internvl3-exploring-advanced-training/)
4. **[Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models](https://md.research.elamur.ai/free/en/papers/deitke-2024-molmo-pixmo-open-weights-open/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Matt Deitke, Christopher Clark, Sangho Lee, et al.*2024 · 216 citations*
5. [**InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency arXiv:2508.18265**Weiyun Wang, Zhangwei Gao, Lixin Gu, et al.*2025 · 179 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-internvl3-5-advancing-open/)
6. **[LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding](https://md.research.elamur.ai/free/en/papers/wu-2024-longvideobench-benchmark-long-context-interleaved/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Haoning Wu, Dongxu Li, Bei Chen, et al.*2024 · 166 citations*
7. [**LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models arXiv:2407.07895**Feng Li, Renrui Zhang, Hao Zhang, et al.*2024 · 127 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llava-next-interleave-tackling-multi/)
8. [**MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark arXiv:2409.02813**Xiang Yue, Tianyu Zheng, Yuansheng Ni, et al.*2024 · 123 citations*](https://md.research.elamur.ai/free/en/papers/yue-2024-mmmu-pro-more-robust-multi/)
9. **[MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations](https://md.research.elamur.ai/free/en/papers/ma-2024-mmlongbench-doc-benchmarking-long-context/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yubo Ma, Yuhang Zang, Liangyu Chen, et al.*2024 · 92 citations*
10. [**mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models arXiv:2408.04840**Jiabo Ye, Haiyang Xu, Haowei Liu, et al.*2024 · 60 citations*](https://md.research.elamur.ai/free/en/papers/ye-2024-mplug-owl3-towards-long-image/)
11. **[MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale](https://md.research.elamur.ai/free/en/papers/guo-2025-mammoth-vl-eliciting-multimodal-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiawei Guo, Tianyu Zheng, Yizhi Li, et al.*2025 · 40 citations*
12. [**InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output arXiv:2407.03320**Pan Zhang, Xiaoyi Dong, Yuhang Zang, et al.*2024 · 39 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-internlm-xcomposer-2-5/)
13. **[Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering](https://md.research.elamur.ai/free/en/papers/si-2025-design2code-benchmarking-multimodal-code-generation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chenglei Si, Yanzhe Zhang, Ryan Li, et al.*2025 · 37 citations*
14. [**MiMo-VL Technical Report arXiv:2506.03569**Xiaomi LLM-Core Team, Zihao Yue, Zhenru Lin, et al.*2025 · 33 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-llm-mimo-vl-technical-report/)
15. **[LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating](https://md.research.elamur.ai/free/en/papers/deng-2025-longdocurl-comprehensive-multimodal-long-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chao Deng, Jiale Yuan, Pi Bu, et al.*2025 · 30 citations*
16. [**MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe arXiv:2509.18154**Tianyu Yu, Zefan Wang, Chongyi Wang, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/yu-2025-minicpm-v-4-5/)
17. **[OmniBench: Towards The Future of Universal Omni-Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-omnibench-towards-future-universal-omni/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yizhi Li, Yinghao Ma, Ge Zhang, et al.*2024 · 17 citations*
18. **[CMM-Math: A Chinese Multimodal Math Dataset To Evaluate and Enhance the Mathematics Reasoning of Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/liu-2024-cmm-math-chinese-multimodal-math/) [ACM International Conference on Multimedia (ACM MM)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-multimedia-acm-mm/)**Wentao Liu, Qianjun Pan, Yi Zhang, et al.*2024 · 13 citations*
19. [**MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction arXiv:2604.27393**Junbo Cui, Bokai Xu, Chongyi Wang, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/cui-2026-minicpm-o-4-5/)
20. **[Beyond Single View: A Comprehensive Benchmark for Medical Multimodal Large Language Models on Multi-Image Understanding](https://md.research.elamur.ai/free/en/papers/xu-2026-beyond-single-view-comprehensive-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dexuan Xu, Yuan Jiayin, Jianing Wang, et al.*2026*
21. **[From Attenuation to Attention: Variational Information Flow Manipulation for Fine-Grained Visual Perception](https://md.research.elamur.ai/free/en/papers/zhu-2026-attenuation-attention-variational-information-flow/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jilong Zhu, Yang Feng*2026*
22. **[MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models](https://md.research.elamur.ai/free/en/papers/wu-2026-mmra-benchmark-evaluating-multi-granularity/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Siwei Wu, King Zhu, Yu Bai, et al.*2026*
23. **[MMTabReal: Real-World Benchmark for Multimodal Table Understanding](https://md.research.elamur.ai/free/en/papers/titiya-2026-mmtabreal-real-world-benchmark-multimodal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Prasham Yatinkumar Titiya, Jainil Trivedi, Chitta Baral, et al.*2026*
24. **[S2H-DPO: Hardness-Aware Preference Optimization for Vision–Language Models](https://md.research.elamur.ai/free/en/papers/shukla-2026-s2h-dpo-hardness-aware-preference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nitish Shukla, Surgan Jandial, Arun Ross*2026*
25. **[Seeing the Other Side: Diagnostic Tasks for Viewpoint Reasoning in Vision–Language Models](https://md.research.elamur.ai/free/en/papers/takenaka-2026-seeing-other-side-diagnostic-tasks/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Makoto Takenaka, Hitomi Yanaka*2026*

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

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