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Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances.
A method for obtaining digital signatures and public-key cryptosystems
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Neural networks and physical systems with emergent collective computational abilities
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Mixture density networks
Christopher M Bishop · 1994
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Dissociation of mechanisms underlying syllogistic reasoning
Vinod Goel, Christian Buchel, Chris Frith, and Raymond J Dolan · 2000
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Representativeness revisited: Attribute substitution in intuitive judgment
Daniel Kahneman, Shane Frederick, et al · 2002
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A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin · 2003
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Prefrontal cortex and flexible cognitive control: Rules without symbols
Nicolas P Rougier, David C Noelle, Todd S Braver, Jonathan D Cohen, and Randall C O’Reilly · 2005
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Dual processing in reasoning: Two systems but one reasoner
Wim De Neys · 2006
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Evidence for time-variant decision making
Jochen Ditterich · 2006
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, Fujie Huang, et al · 2006
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Recurrent neuronal circuits in the neocortex
Rodney J Douglas and Kevan AC Martin · 2007
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Mapping sentence form onto meaning: The syntax–semantic interface
Angela D Friederici and Jürgen Weissenborn · 2007
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Dual-process and dual-system theories of reasoning
Keith Frankish · 2010
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Uncertainty during anticipation modulates neural responses to aversion in human insula and amygdala
Issidoros C. Sarinopoulos, D. Grupe, Kristen L. Mackiewicz, J. Herrington, M. Lor, E. E. Steege, and J. Nitschke · 2010
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Thinking, fast and slow
Daniel Kahneman · 2011
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Dual-process theories of reasoning: Contemporary issues and developmental applications
Jonathan St BT Evans · 2011
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Differential representations of prior and likelihood uncertainty in the human brain
I. Vilares, J. D. Howard, Hugo L. Fernandes, J. Gottfried, and Konrad Paul Kording · 2012
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Auto-encoding variational bayes, 2013
Diederik P Kingma, Max Welling, et al · 2013
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Generative adversarial nets
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Using contents and containers to investigate problem solving strategies among toddlers
Zaid Alkouri · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Uncertainty and stress: Why it causes diseases and how it is mastered by the brain
A. Peters, B. McEwen, and Karl J. Friston · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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The" something something" video database for learning and evaluating visual common sense
Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al · 2017
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A conceptual introduction to hamiltonian monte carlo
Michael Betancourt · 2017
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Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2018
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Paving the way to eureka—introducing “dira” as an experimental paradigm to observe the process of creative problem solving
Frank Loesche, Jeremy Goslin, and Guido Bugmann · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Łukasz Kaiser · 2018
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Input complexity and out-of-distribution detection with likelihood-based generative models
Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F Núñez, and Jordi Luque · 2019
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Implicit generation and modeling with energy based models
Yilun Du and Igor Mordatch · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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The knowledge complexity of interactive proof-systems
Shafi Goldwasser, Silvio Micali, and Chales Rackoff · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2019
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Pytorch lightning
William A Falcon · 2019
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Improved contrastive divergence training of energy based models
Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch · 2020
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Generalized energy based models
Michael Arbel, Liang Zhou, and Arthur Gretton · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Scaling laws for autoregressive generative modeling
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith · 2020
Cited alongside, same era.
Ruslan Rakhimov, Denis Volkhonskiy, Alexey Artemov, Denis Zorin, and Evgeny Burnaev · 2020
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Predicting emergent abilities with infinite resolution evaluation
Shengding Hu, Xin Liu, Xu Han, Xinrong Zhang, Chaoqun He, Weilin Zhao, Yankai Lin, Ning Ding, Zebin Ou, Guoyang Zeng, et al · 2023
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Transformer flops
Adam Casson · 2023
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T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation
Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu · 2023
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Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models
Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, and Mehrdad Farajtabar · 2024
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Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al · 2024
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Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, and Le Song · 2020
Cited alongside, same era.
Energy-based reranking: Improving neural machine translation using energy-based models
Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, and Andrew McCallum · 2020
Cited alongside, same era.
Glu variants improve transformer, 2020
Noam Shazeer · 2020
Cited alongside, same era.
Masked autoencoders are scalable vision learners, 2021
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Residual energy-based models for text
Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc’Aurelio Ranzato, and Arthur Szlam · 2021
Cited alongside, same era.
Bayesformer: Transformer with uncertainty estimation
Karthik Abinav Sankararaman, Sinong Wang, and Han Fang · 2022
Cited alongside, same era.
Learning iterative reasoning through energy minimization
Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch · 2022
Cited alongside, same era.
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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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Uncertainty-based abstention in llms improves safety and reduces hallucinations
Christian Tomani, Kamalika Chaudhuri, Ivan Evtimov, Daniel Cremers, and Mark Ibrahim · 2024
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Out-of-distribution detection with a single unconditional diffusion model
Alvin Heng, Harold Soh, et al · 2024
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Introduction to latent variable energy-based models: a path toward autonomous machine intelligence
Anna Dawid and Yann LeCun · 2024
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A survey on the applications of zero-knowledge proofs
Ryan Lavin, Xuekai Liu, Hardhik Mohanty, Logan Norman, Giovanni Zaarour, and Bhaskar Krishnamachari · 2024
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Learning iterative reasoning through energy diffusion
Yilun Du, Jiayuan Mao, and Joshua B Tenenbaum · 2024
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Position: Llms can’t plan, but can help planning in llm-modulo frameworks
Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri, Lucas Paul Saldyt, and Anil B Murthy · 2024
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How to compute hessian-vector products?
Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, and Thomas Moreau · 2024
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Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al · 2024
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Redpajama: an open dataset for training large language models
Maurice Weber, Dan Fu, Quentin Anthony, Yonatan Oren, Shane Adams, Anton Alexandrov, Xiaozhong Lyu, Huu Nguyen, Xiaozhe Yao, Virginia Adams, et al · 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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Physics of language models: Part 2.1, grade-school math and the hidden reasoning process
Tian Ye, Zicheng Xu, Yuanzhi Li, and Zeyuan Allen-Zhu · 2024
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Byte latent transformer: Patches scale better than tokens
Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason Weston, Luke Zettlemoyer, et al · 2024
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Language models scale reliably with over-training and on downstream tasks
Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, Suchin Gururangan, Mitchell Wortsman, Rulin Shao, Jean Mercat, Alex Fang, Jeffrey Li, Sedrick Keh, et al · 2024
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Improving pretraining data using perplexity correlations
Tristan Thrush, Christopher Potts, and Tatsunori Hashimoto · 2024
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Scaling laws for predicting downstream performance in llms
Yangyi Chen, Binxuan Huang, Yifan Gao, Zhengyang Wang, Jingfeng Yang, and Heng Ji · 2024
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Scaling laws for downstream task performance of large language models
Berivan Isik, Natalia Ponomareva, Hussein Hazimeh, Dimitris Paparas, Sergei Vassilvitskii, and Sanmi Koyejo · 2024
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Faithfulness vs. plausibility: On the (un) reliability of explanations from large language models
Chirag Agarwal, Sree Harsha Tanneru, and Himabindu Lakkaraju · 2024
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Dino-wm: World models on pre-trained visual features enable zero-shot planning
Gaoyue Zhou, Hengkai Pan, Yann LeCun, and Lerrel Pinto · 2024
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The fineweb datasets: Decanting the web for the finest text data at scale
Guilherme Penedo, Hynek Kydlíček, Anton Lozhkov, Margaret Mitchell, Colin A Raffel, Leandro Von Werra, Thomas Wolf, et al · 2024
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Adaptive inference-time compute: Llms can predict if they can do better, even mid-generation
Rohin Manvi, Anikait Singh, and Stefano Ermon · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
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Energy transformer
Benjamin Hoover, Yuchen Liang, Bao Pham, Rameswar Panda, Hendrik Strobelt, Duen Horng Chau, Mohammed Zaki, and Dmitry Krotov · 2024
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When can transformers compositionally generalize in-context?
Seijin Kobayashi, Simon Schug, Yassir Akram, Florian Redhardt, Johannes von Oswald, Razvan Pascanu, Guillaume Lajoie, and João Sacramento · 2024
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Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu · 2024
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Yang Yan, Yu Lu, Renjun Xu, and Zhenzhong Lan · 2025
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Escapebench: Pushing language models to think outside the box
Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, and Heng Ji · 2025
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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
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Expanding rl with verifiable rewards across diverse domains
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The illusion of thinking: Understanding the strengths and limitations of reasoning models via the lens of problem complexity, 2025
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Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model?
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Inference-time scaling for diffusion models beyond scaling denoising steps
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Video-t1: Test-time scaling for video generation
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A general framework for inference-time scaling and steering of diffusion models
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Scaling up test-time compute with latent reasoning: A recurrent depth approach
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Letter to john von neumann, 1956
Kurt Gödel · 2025
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All roads lead to likelihood: The value of reinforcement learning in fine-tuning
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Long-context autoregressive video modeling with next-frame prediction
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Is best-of-n the best of them? coverage, scaling, and optimality in inference-time alignment
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Reasoning with latent thoughts: On the power of looped transformers
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Implicit reasoning in transformers is reasoning through shortcuts
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