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Test time scaling is currently one of the most active research areas that shows promise after training time scaling has reached its limits.
Understanding deep architectures using a recursive convolutional network
David Eigen Jason Rolfe Rob Fergus Yann LeCun · 2014
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Adam: A method for stochastic optimization
Jimmy Lei Ba Diederik P. Kingma · 2015
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Delving deeper into convolutional networks for learning video representations
Nicolas Ballas, L. Yao, Christopher Joseph Pal, and Aaron C. Courville · 2016
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Adaptive computation time for recurrent neural networks
A. Graves · 2016
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Metareg: Towards domain generalization using meta-regularization
Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa · 2018
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Light gated recurrent units for speech recognition
Mirco Ravanelli, Philemon Brakel, Maurizio Omologo, and Yoshua Bengio · 2018
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Benchmarking neural network robustness to common corruptions and surface variations
Dan Hendrycks and Thomas G Dietterich · 2019
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Differentiable adaptive computation time for visual reasoning
Cristobal Eyzaguirre and Alvaro Soto · 2020
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A simple way to make neural networks robust against diverse image corruptions
Evgenia Rusak, Lukas Schott, Roland S Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, and Wieland Brendel · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, and Moritz Hardt · 2020
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Pondernet: Learning to ponder
Andrea Banino, Jan Balaguer, and Charles Blundell · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Can you learn an algorithm? generalizing from easy to hard problems with recurrent networks
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, and Tom Goldstein · 2021
Cited alongside, same era.
End-to-end algorithm synthesis with recurrent networks: Logical extrapolation without overthinking
Aprit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, and Tom Goldstein · 2022
Cited alongside, same era.
Test-time training with masked autoencoders
Yossi Gandelsman, Yu Sun, Xinlei Chen, and Alexei Efros · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Training large language models to reason in a continuous latent space
Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian · 2024
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Beyond a*: Better planning with transformers via search dynamics bootstrapping
Lucas Lehnert, Sainbayar Sukhbaatar, DiJia Su, Qinqing Zheng, Paul McVay, Michael Rabbat, and Yuandong Tian · 2024
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Vila: On pre-training for visual language models
Ji Lin, Hongxu Yin, Wei Ping, Pavlo Molchanov, Mohammad Shoeybi, and Song Han · 2024
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OpenAI · 2024
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Learning to (learn at test time): Rnns with expressive hidden states
Yu Sun, Xinhao Li, Karan Dalal, Jiarui Xu, Arjun Vikram, Genghan Zhang, Yann Dubois, Xinlei Chen, Xiaolong Wang, Sanmi Koyejo, et al · 2024
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Resurrecting recurrent neural networks for long sequences
Antonio Orvieto, Samuel L Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De · 2023
Cited alongside, same era.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik R Narasimhan · 2023
Cited alongside, same era.
xlstm: Extended long short-term memory
Maximilian Beck, Korbinian Pöppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, Günter Klambauer, Johannes Brandstetter, and Sepp Hochreiter · 2024
Cited alongside, same era.
Video generation models as world simulators
Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh · 2024
Cited alongside, same era.
Griffin: Mixing gated linear recurrences with local attention for efficient language models
Soham De, Samuel L Smith, Anushan Fernando, Aleksandar Botev, George Cristian-Muraru, Albert Gu, Ruba Haroun, Leonard Berrada, Yutian Chen, Srivatsan Srinivasan, et al · 2024
Cited alongside, same era.
Insight-v: Exploring long-chain visual reasoning with multimodal large language models
Yuhao Dong, Zuyan Liu, Hai-Long Sun, Jingkang Yang, Winston Hu, Yongming Rao, and Ziwei Liu · 2024
Cited alongside, same era.
Later among the works it cites.
Adaptive recurrent vision performs zero-shot computation scaling to unseen difficulty levels
Vijay Veerabadran, Srinivas Ravishankar, Yuan Tang, Ritik Raina, and Virginia de Sa · 2024
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Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution
Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al · 2024
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Lars: Latent reasoning skills for chain-of-thought reasoning
Zifan Xu, Haozhu Wang, Dmitriy Bespalov, Xian Wu, Peter Stone, and Yanjun Qi · 2024
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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.
s1: Simple test-time scaling, 2025
Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto · 2025
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Llamav-o1: Rethinking step-by-step visual reasoning in llms
Omkar Thawakar, Dinura Dissanayake, Ketan More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, et al · 2025
Closest in time.