---
title: "Who cites Liangyu Chen — 387 papers of our library"
description: "387 papers of the Elamur Research library cite the works of Liangyu Chen, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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---
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# Who cites Liangyu Chen

393 citations of their works from 387 papers of our library (of the 149,439 whose bibliographies we read) · 136 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Liangyu Chen](https://md.research.elamur.ai/free/en/authors/liangyu-chen/)

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1. **[MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://md.research.elamur.ai/free/en/papers/yue-2023-mmmu-massive-multi-discipline-multimodal/) [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/)**Xiang Yue, Yuansheng Ni, Kai Zhang, et al.*2023 · 1,005 citations*
2. **[Evaluating Object Hallucination in Large Vision-Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-object-hallucination-large-vision/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yifan Li, Yifan Du, Kun Zhou, et al.*2023 · 1,004 citations*
3. [**A Survey on In-context Learning arXiv:2301.00234**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 978 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/)
4. [**Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond arXiv:2308.12966**Jinze Bai, Shuai Bai, Shusheng Yang, et al.*2023 · 970 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-vl-versatile-vision-language/)
5. **[Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://md.research.elamur.ai/free/en/papers/lin-2023-video-llava-learning-united-visual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bin Lin, Yang Ye, Bin Zhu, et al.*2023 · 628 citations*
6. **[s1: Simple test-time scaling](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 608 citations*
7. **[MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/fu-2023-mme-comprehensive-evaluation-benchmark-multimodal/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Chaoyou Fu, Peixian Chen, Yunhang Shen, et al.*2023 · 554 citations*
8. **[How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://md.research.elamur.ai/free/en/papers/chen-2024-far-gpt-4v-closing/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhe Chen, Weiyun Wang, Hao Tian, et al.*2024 · 541 citations*
9. **[Instruction Tuning for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.*2023 · 520 citations*
10. **[MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://md.research.elamur.ai/free/en/papers/li-2023-mvbench-comprehensive-multi-modal-video/) [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/)**Kunchang Li, Yali Wang, Yinan He, et al.*2023 · 463 citations*
11. **[ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://md.research.elamur.ai/free/en/papers/chen-2023-sharegpt4v-improving-large-multi-modal/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Lin Chen, Jinsong Li, Xiaoyi Dong, et al.*2023 · 454 citations*
12. [**InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks arXiv:2312.14238**Zhe Chen, Jiannan Wu, Wenhai Wang, et al.*2023 · 435 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/)
13. [**Qwen3-VL Technical Report arXiv:2511.21631**Shuai Bai, Yuxuan Cai, Ruizhe Chen, et al.*2025 · 370 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen3-vl-technical-report/)
14. [**MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities arXiv:2308.02490**Weihao Yu, Zhengyuan Yang, Linjie Li, et al.*2023 · 344 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-mm-vet-evaluating-large-multimodal/)
15. [**LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention arXiv:2303.16199**Renrui Zhang, Jiaming Han, Chris Liu, et al.*2023 · 333 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-llama-adapter-efficient-fine-tuning/)
16. **[MM-LLMs: Recent Advances in MultiModal Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2024-mm-llms-recent-advances-multimodal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Duzhen Zhang, Yahan Yu, Jiahua Dong, et al.*2024 · 320 citations*
17. [**SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension arXiv:2307.16125**Bohao Li, Rui Wang, Guangzhi Wang, et al.*2023 · 265 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-seed-bench-benchmarking-multimodal-llms/)
18. [**Wan: Open and Advanced Large-Scale Video Generative Models arXiv:2503.20314**Team Wan, Ang Wang, Baole Ai, et al.*2025 · 260 citations*](https://md.research.elamur.ai/free/en/papers/wan-2025-wan-open-advanced-large-scale/)
19. **[InternVideo2: Scaling Foundation Models for Multimodal Video Understanding](https://md.research.elamur.ai/free/en/papers/wang-2024-internvideo2-scaling-foundation-models/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Yi Wang, Kunchang Li, Xinhao Li, et al.*2024 · 253 citations*
20. [**Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding arXiv:2306.02858**Hang Zhang, Xin Li, Lidong Bing*2023 · 238 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-video-llama-instruction-tuned-audio/)
21. **[RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback](https://md.research.elamur.ai/free/en/papers/yu-2023-rlhf-v-towards-trustworthy-mllms/) [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/)**Tianyu Yu, Yuan Yao, Haoye Zhang, et al.*2023 · 217 citations*
22. **[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*
23. [**Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic arXiv:2306.15195**Keqin Chen, Zhao Zhang, Weili Zeng, et al.*2023 · 210 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-shikra-unleashing-multimodal-llm-s/)
24. **[Woodpecker: Hallucination Correction for Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/yin-2023-woodpecker-hallucination-correction-multimodal-large/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Shukang Yin, Chaoyou Fu, Sirui Zhao, et al.*2023 · 206 citations*
25. [**Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning arXiv:2306.14565**Fuxiao Liu, Kevin Lin, Linjie Li, et al.*2023 · 189 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-mitigating-hallucination-large-multi-modal/)
26. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
27. [**Aligning Large Multimodal Models with Factually Augmented RLHF arXiv:2309.14525**Zhiqing Sun, Sheng Shen, Shengcao Cao, et al.*2023 · 176 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-aligning-large-multimodal-models-factually/)
28. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models arXiv:2503.16419**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 157 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/)
29. **[MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2023-mm-safetybench-benchmark-safety-evaluation/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Xin Liu, Yichen Zhu, Jindong Gu, et al.*2023 · 152 citations*
30. **[MLVU: Benchmarking Multi-task Long Video Understanding](https://md.research.elamur.ai/free/en/papers/zhou-2024-mlvu-benchmarking-multi-task-long/) [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/)**Junjie Zhou, Yan Shu, Bo Zhao, et al.*2024 · 140 citations*
31. [**SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents arXiv:2401.10935**Kanzhi Cheng, Qiushi Sun, Yougang Chu, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2024-seeclick-harnessing-gui-grounding-advanced/)
32. [**Analyzing and Mitigating Object Hallucination in Large Vision-Language Models arXiv:2310.00754**Yiyang Zhou, Chenhang Cui, Jaehong Yoon, et al.*2023 · 134 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-analyzing-mitigating-object-hallucination-large/)
33. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
34. [**L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning arXiv:2503.04697**Pranjal Aggarwal, Sean Welleck*2025 · 125 citations*](https://md.research.elamur.ai/free/en/papers/aggarwal-2025-l1-controlling-long-reasoning-model/)
35. [**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/)
36. [**Hallucination of Multimodal Large Language Models: A Survey arXiv:2404.18930**Zechen Bai, Pichao Wang, Tianjun Xiao, et al.*2024 · 120 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-hallucination-multimodal-large-language-models/)
37. **[LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-lmms-eval-reality-check-evaluation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaichen Zhang, Bo Li, Peiyuan Zhang, et al.*2025 · 115 citations*
38. **[TokenSkip: Controllable Chain-of-Thought Compression in LLMs](https://md.research.elamur.ai/free/en/papers/xia-2025-tokenskip-controllable-chain-thought-compression/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Heming Xia, Chak Tou Leong, Wenjie Wang, et al.*2025 · 106 citations*
39. [**ChartLlama: A Multimodal LLM for Chart Understanding and Generation arXiv:2311.16483**Yucheng Han, Chi Zhang, Xin Chen, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/han-2023-chartllama-multimodal-llm-chart-understanding/)
40. [**LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding arXiv:2306.17107**Yanzhe Zhang, Ruiyi Zhang, Jiuxiang Gu, et al.*2023 · 98 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-llavar-enhanced-visual-instruction-tuning/)
41. [**Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models arXiv:2601.18734**Siyan Zhao, Zhihui Xie, Mengchen Liu, et al.*2026 · 97 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2026-self-distilled-reasoner-policy-self/)
42. [**ColPali: Efficient Document Retrieval with Vision Language Models arXiv:2407.01449**Manuel Faysse, Hugues Sibille, Tony Wu, et al.*2024 · 96 citations*](https://md.research.elamur.ai/free/en/papers/faysse-2024-colpali-efficient-document-retrieval-vision/)
43. [**InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model arXiv:2401.16420**Xiaoyi Dong, Pan Zhang, Yuhang Zang, et al.*2024 · 94 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-internlm-xcomposer2-mastering-free/)
44. **[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*
45. [**GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning arXiv:2507.01006**GLM-V Team, Wenyi Hong, Wenmeng Yu, et al.*2025 · 89 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-v-glm-4-5v-glm/)
46. [**InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition arXiv:2309.15112**Pan Zhang, Xiaoyi Dong, Bin Wang, et al.*2023 · 87 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-internlm-xcomposer-vision-language/)
47. [**Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration arXiv:2306.09093**Chenyang Lyu, Minghao Wu, Longyue Wang, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/lyu-2023-macaw-llm-multi-modal-language/)
48. [**Reinforcement Learning via Self-Distillation arXiv:2601.20802**Jonas Hubotter, Frederike Lubeck, Lejs Behric, et al.*2026 · 74 citations*](https://md.research.elamur.ai/free/en/papers/hubotter-2026-reinforcement-learning-self-distillation/)
49. [**Dynamic Early Exit in Reasoning Models arXiv:2504.15895**Chenxu Yang, Qingyi Si, Yongjie Duan, et al.*2025 · 74 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-dynamic-early-exit-reasoning-models/)
50. **[Training Language Models to Reason Efficiently](https://md.research.elamur.ai/free/en/papers/arora-2025-training-language-models-reason-efficiently/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Daman Arora, Andrea Zanette*2025 · 74 citations*
51. **[MANTIS: Interleaved Multi-Image Instruction Tuning](https://md.research.elamur.ai/free/en/papers/jiang-2024-mantis-interleaved-multi-image-instruction/) [Transactions on Machine Learning Research](https://md.research.elamur.ai/free/en/venues/transactions-on-machine-learning-research/)**Dongfu Jiang, Xuan He, Huaye Zeng, et al.*2024 · 74 citations*
52. [**Long Context Transfer from Language to Vision arXiv:2406.16852**Peiyuan Zhang, Kaichen Zhang, Bo Li, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-long-context-transfer-language-vision/)
53. [**Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models arXiv:2305.14705**Sheng Shen, Le Hou, Yanqi Zhou, et al.*2023 · 70 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-mixture-experts-meets-instruction-tuning/)
54. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
55. **[CoT-Valve: Length-Compressible Chain-of-Thought Tuning](https://md.research.elamur.ai/free/en/papers/ma-2025-cot-valve-length-compressible-chain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyin Ma, Guangnian Wan, Runpeng Yu, et al.*2025 · 64 citations*
56. [**Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models arXiv:2402.02207**Yongshuo Zong, Ondrej Bohdal, Tingyang Yu, et al.*2024 · 60 citations*](https://md.research.elamur.ai/free/en/papers/zong-2024-safety-fine-tuning-almost-no/)
57. [**Investigating the Catastrophic Forgetting in Multimodal Large Language Models arXiv:2309.10313**Yuexiang Zhai, Shengbang Tong, Xiao Li, et al.*2023 · 59 citations*](https://md.research.elamur.ai/free/en/papers/zhai-2023-investigating-catastrophic-forgetting-multimodal-large/)
58. [**MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning arXiv:2309.07915**Haozhe Zhao, Zefan Cai, Shuzheng Si, et al.*2023 · 58 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-mmicl-empowering-vision-language-model/)
59. [**A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond arXiv:2503.21614**Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/qu-2025-survey-efficient-reasoning-large-reasoning/)
60. **[InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD](https://md.research.elamur.ai/free/en/papers/dong-2024-internlm-xcomposer2-4khd-pioneering/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xiaoyi Dong, Pan Zhang, Yuhang Zang, et al.*2024 · 54 citations*
61. **[LightThinker: Thinking Step-by-Step Compression](https://md.research.elamur.ai/free/en/papers/zhang-2025-lightthinker-thinking-step-step-compression/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jintian Zhang, Yuqi Zhu, Mengshu Sun, et al.*2025 · 50 citations*
62. [**MiniMax-01: Scaling Foundation Models with Lightning Attention arXiv:2501.08313**MiniMax, Aonian Li, Bangwei Gong, et al.*2025 · 50 citations*](https://md.research.elamur.ai/free/en/papers/minimax-2025-minimax-01-scaling-foundation/)
63. **[ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents](https://md.research.elamur.ai/free/en/papers/wang-2025-vidorag-visual-document-retrieval-augmented/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qiuchen Wang, Ruixue Ding, Zehui Chen, et al.*2025 · 39 citations*
64. [**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/)
65. [**Efficient Reasoning Models: A Survey arXiv:2504.10903**Sicheng Feng, Gongfan Fang, Xinyin Ma, et al.*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/feng-2025-efficient-reasoning-models-survey/)
66. **[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*
67. [**Qwen3.5-Omni Technical Report arXiv:2604.15804**Qwen Team*2026 · 29 citations*](https://md.research.elamur.ai/free/en/papers/qwen-team-2026-qwen3-5-omni-technical/)
68. [**InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models arXiv:2503.06692**Yuchen Yan, Yongliang Shen, Yang Liu, et al.*2025 · 29 citations*](https://md.research.elamur.ai/free/en/papers/yan-2025-inftythink-breaking-length-limits-long/)
69. **[Peacock: A Family of Arabic Multimodal Large Language Models and Benchmarks](https://md.research.elamur.ai/free/en/papers/alwajih-2024-peacock-family-arabic-multimodal-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fakhraddin Alwajih, El Moatez Billah Nagoudi, Gagan Bhatia, et al.*2024 · 28 citations*
70. [**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/)
71. **[VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/wang-2025-vrag-rl-empower-vision-perception/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Qiuchen Wang, Ruixue Ding, Yu Zeng, et al.*2025 · 23 citations*
72. **[REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark](https://md.research.elamur.ai/free/en/papers/wasserman-2025-real-mm-rag-real-world/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Navve Wasserman, Roi Pony, Oshri Naparstek, et al.*2025 · 22 citations*
73. [**Scaling Latent Reasoning via Looped Language Models arXiv:2510.25741**Rui-Jie Zhu, Zixuan Wang, Kai Hua, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-scaling-latent-reasoning-looped-language/)
74. **[M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework](https://md.research.elamur.ai/free/en/papers/chia-2025-m-longdoc-benchmark-multimodal-super/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yew Ken Chia, Liying Cheng, Hou Pong Chan, et al.*2025 · 21 citations*
75. **[MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification](https://md.research.elamur.ai/free/en/papers/sun-2025-mm-verify-enhancing-multimodal-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Linzhuang Sun, Hao Liang, Jingxuan Wei, et al.*2025 · 19 citations*
76. [**Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs arXiv:2507.09477**Yangning Li, Weizhi Zhang, Yuyao Yang, et al.*2025 · 17 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-towards-agentic-rag-deep-reasoning/)
77. **[SimpleDoc: Multi-Modal Document Understanding with Dual-Cue Page Retrieval and Iterative Refinement](https://md.research.elamur.ai/free/en/papers/jain-2025-simpledoc-multi-modal-document-understanding/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chelsi Jain, Yiran Wu, Yifan Zeng, et al.*2025 · 16 citations*
78. **[FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/jiang-2026-foe-forest-errors-makes-first/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kehan Jiang, Haonan Dong, Zhaolu Kang, et al.*2026 · 15 citations*
79. [**Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models arXiv:2505.03469**Bin Yu, Hang Yuan, Haotian Li, et al.*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/yu-2025-long-short-chain-thought-mixture/)
80. [**Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models arXiv:2312.01714**Bingshuai Liu, Chenyang Lyu, Zijun Min, et al.*2023 · 13 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-retrieval-augmented-multi-modal-chain/)
81. **[Fast and Effective On-Policy Distillation from Reasoning Prefixes](https://md.research.elamur.ai/free/en/papers/zhang-2026-fast-effective-policy-distillation-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dongxu Zhang, Zhichao Yang, Sepehr Janghorbani, et al.*2026 · 12 citations*
82. **[MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval](https://md.research.elamur.ai/free/en/papers/wu-2025-molorag-bootstrapping-document-understanding-multi/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xixi Wu, Yanchao Tan, Nan Hou, et al.*2025 · 12 citations*
83. [**TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression arXiv:2506.02678**Zhong-Zhi Li, Xiao Liang, Zihao Tang, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-tl-dr-too-long-re/)
84. **[Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning](https://md.research.elamur.ai/free/en/papers/yu-2026-benchmarking-vision-language-models-chinese/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haiyang Yu, Yuchuan Wu, Fan Shi, et al.*2026 · 7 citations*
85. [**Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thought arXiv:2510.04230**Guijin Son, Donghun Yang, Hitesh Laxmichand Patel, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/son-2025-pushing-multilingual-reasoning-models-language/)
86. **[The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis](https://md.research.elamur.ai/free/en/papers/wei-2026-evolution-thought-tracking-llm-overthinking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zihao Wei, Liang Pang, Jiahao Liu, et al.*2026 · 6 citations*
87. **[Doc-React: Multi-page Heterogeneous Document Question-answering](https://md.research.elamur.ai/free/en/papers/wu-2025-doc-react-multi-page-heterogeneous/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junda Wu, Yu Xia, Tong Yu, et al.*2025 · 6 citations*
88. **[PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning](https://md.research.elamur.ai/free/en/papers/hu-2026-pacore-learning-scale-test-time/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingcheng Hu, Yinmin Zhang, Shijie Shang, et al.*2026 · 5 citations*
89. **[LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding](https://md.research.elamur.ai/free/en/papers/sourati-2026-lad-rag-layout-aware-dynamic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhivar Sourati, Zheng Wang, Marianne Menglin Liu, et al.*2026 · 4 citations*
90. **[Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment](https://md.research.elamur.ai/free/en/papers/yang-2026-which-reasoning-trajectories-teach-students/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuming Yang, Mingyoung Lai, Wanxu Zhao, et al.*2026 · 4 citations*
91. **[Controllable LLM Reasoning via Sparse Autoencoder-Based Steering](https://md.research.elamur.ai/free/en/papers/fang-2026-controllable-llm-reasoning-sparse-autoencoder/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yi Fang, Wenjie Wang, Mingfeng Xue, et al.*2026 · 3 citations*
92. **[DeepPrune: Parallel Scaling without Inter-trace Redundancy](https://md.research.elamur.ai/free/en/papers/tu-2026-deepprune-parallel-scaling-without-inter/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shangqing Tu, Yaxuan Li, Yushi Bai, et al.*2026 · 3 citations*
93. **[MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis](https://md.research.elamur.ai/free/en/papers/yu-2026-mathagent-adversarial-evolution-constraint-graphs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zixiong Yu, Jun Rao, Guhan Chen, et al.*2026 · 3 citations*
94. **[SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking](https://md.research.elamur.ai/free/en/papers/huang-2026-sat-balancing-reasoning-accuracy-efficiency/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weiyang Huang, Xuefeng Bai, Kehai Chen, et al.*2026 · 3 citations*
95. **[Do LLMs Encode Functional Importance of Reasoning Tokens ?](https://md.research.elamur.ai/free/en/papers/singh-2026-do-llms-encode-functional-importance/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Janvijay Singh, Dilek Hakkani-Tür*2026 · 2 citations*
96. **[DocLens: A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding](https://md.research.elamur.ai/free/en/papers/zhu-2026-doclens-tool-augmented-multi-agent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dawei Zhu, Rui Meng, Jiefeng Chen, et al.*2026 · 2 citations*
97. **[Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework](https://md.research.elamur.ai/free/en/papers/yan-2026-sculpting-vector-space-efficient-multi/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yibo Yan, Mingdong Ou, Yi Cao, et al.*2026 · 2 citations*
98. [**STEP3-VL-10B Technical Report arXiv:2601.09668**Ailin Huang, Chengyuan Yao, Chunrui Han, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/huang-2026-step3-vl-10b-technical/)
99. **[Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/liu-2026-think-how-think-mitigating-overthinking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yongjiang Liu, Haoxi Li, Xiaosong Ma, et al.*2026 · 2 citations*
100. **[KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation](https://md.research.elamur.ai/free/en/papers/tatarinov-2026-kg-mulqa-framework-kg-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nikita Tatarinov, Vidhyakshaya Kannan, Haricharana Srinivasa, et al.*2026 · 1 citation*

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