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Recent advancements in reasoning have significantly enhanced the capabilities of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) across diverse tasks.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Icdar2019 competition on scanned receipt ocr and information extraction
Zheng Huang, Kai Chen, Jianhua He, Xiang Bai, Dimosthenis Karatzas, Shijian Lu, and CV Jawahar · 2019
Earlier work this paper cites.
Cord: a consolidated receipt dataset for post-ocr parsing
Seunghyun Park, Seung Shin, Bado Lee, Junyeop Lee, Jaeheung Surh, Minjoon Seo, and Hwalsuk Lee · 2019
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Funsd: A dataset for form understanding in noisy scanned documents
Guillaume Jaume, Hazim Kemal Ekenel, and Jean-Philippe Thiran · 2019
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MathQA: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi · 2019
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Docvqa: A dataset for vqa on document images
Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar · 2021
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Visualmrc: Machine reading comprehension on document images
Ryota Tanaka, Kyosuke Nishida, and Sen Yoshida · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant · 2021
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
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Infographicvqa
Minesh Mathew, Viraj Bagal, Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny, and CV Jawahar · 2022
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ChartQA: A benchmark for question answering about charts with visual and logical reasoning
Ahmed Masry, Xuan Long Do, Jia Qing Tan, Shafiq Joty, and Enamul Hoque · 2022
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Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch · 2023
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H2o: Heavy-hitter oracle for efficient generative inference of large language models
Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, et al · 2023
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What makes pre-trained language models better zero-shot learners?
Jinghui Lu, Dongsheng Zhu, Weidong Han, Rui Zhao, Brian Mac Namee, and Fei Tan · 2023
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Language models can solve computer tasks
Geunwoo Kim, Pierre Baldi, and Stephen McAleer · 2023
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Large language models as commonsense knowledge for large-scale task planning
Zirui Zhao, Wee Sun Lee, and David Hsu · 2023
Cited alongside, same era.
Toolqa: A dataset for llm question answering with external tools
Yuchen Zhuang, Yue Yu, Kuan Wang, Haotian Sun, and Chao Zhang · 2023
Cited alongside, same era.
Reflexion: language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
Cited alongside, same era.
Camel: Communicative agents for "mind" exploration of large language model society
Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem · 2023
Cited alongside, same era.
Skeleton-of-thought: Large language models can do parallel decoding
Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, Huazhong Yang, and Yu Wang · 2023
Cited alongside, same era.
Modeling entities as semantic points for visual information extraction in the wild
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al · 2024
Later among the works it cites.
Layoutllm: Layout instruction tuning with large language models for document understanding
Chuwei Luo, Yufan Shen, Zhaoqing Zhu, Qi Zheng, Zhi Yu, and Cong Yao · 2024
Later among the works it cites.
Openai o1 hub, 2025
OpenAI · 2025
Closest in time.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
Closest in time.
Qwq: Reflect deeply on the boundaries of the unknown
Qwen Team · 2025
Closest in time.
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Zhibo Yang, Rujiao Long, Pengfei Wang, Sibo Song, Humen Zhong, Wenqing Cheng, Xiang Bai, and Cong Yao · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Cited alongside, same era.
Introducing qwen-7b: Open foundation and human-aligned models (of the state-of-the-arts), 2023
Qwen · 2023
Cited alongside, same era.
Causal language modeling can elicit search and reasoning capabilities on logic puzzles
Kulin Shah, Nishanth Dikkala, Xin Wang, and Rina Panigrahy · 2024
Cited alongside, same era.
Chain of preference optimization: Improving chain-of-thought reasoning in llms
Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, and Min Lin · 2024
Cited alongside, same era.
Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al · 2024
Cited alongside, same era.
Simpo: Simple preference optimization with a reference-free reward
Yu Meng, Mengzhou Xia, and Danqi Chen · 2024
Cited alongside, same era.
ByteDance Seed, Yufeng Yuan, Yu Yue, Mingxuan Wang, Xiaochen Zuo, Jiaze Chen, Lin Yan, Wenyuan Xu, Chi Zhang, Xin Liu, et al · 2025
Closest in time.
Gemini 2.5: Our most intelligent AI model
Google DeepMind · 2025
Closest in time.
Advancing sequential numerical prediction in autoregressive models, 2025
Xiang Fei, Jinghui Lu, Qi Sun, Hao Feng, Yanjie Wang, Wei Shi, An-Lan Wang, Jingqun Tang, and Can Huang · 2025
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To cot or not to cot? chain-of-thought helps mainly on math and symbolic reasoning
Zayne Rea Sprague, Fangcong Yin, Juan Diego Rodriguez, Dongwei Jiang, Manya Wadhwa, Prasann Singhal, Xinyu Zhao, Xi Ye, Kyle Mahowald, and Greg Durrett · 2025
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R1-v: Reinforcing super generalization ability in vision-language models with less than $3
Liang Chen, Lei Li, Haozhe Zhao, Yifan Song, and Vinci · 2025
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Han Wang, Yongjie Ye, Bingru Li, Yuxiang Nie, Jinghui Lu, Jingqun Tang, Yanjie Wang, and Can Huang · 2025
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Eve: Towards end-to-end video subtitle extraction with vision-language models
Haiyang Yu, Jinghui Lu, Yanjie Wang, Yang Li, Han Wang, Can Huang, and Bin Li · 2025
Closest in time.
Stop overthinking: A survey on efficient reasoning for large language models
Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen Zhong, Hanjie Chen, et al · 2025
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Efficient reasoning models: A survey
Sicheng Feng, Gongfan Fang, Xinyin Ma, and Xinchao Wang · 2025
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How well do llms compress their own chain-of-thought? a token complexity approach
Ayeong Lee, Ethan Che, and Tianyi Peng · 2025
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Reasoning models can be effective without thinking
Wenjie Ma, Jingxuan He, Charlie Snell, Tyler Griggs, Sewon Min, and Matei Zaharia · 2025
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Chain of draft: Thinking faster by writing less
Silei Xu, Wenhao Xie, Lingxiao Zhao, and Pengcheng He · 2025
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Reasoning to learn from latent thoughts
Yangjun Ruan, Neil Band, Chris J Maddison, and Tatsunori Hashimoto · 2025
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