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Reasoning, the process of devising and executing complex goal-oriented action sequences, remains a critical challenge in AI.
On the measure of intelligence (abstraction and reasoning corpus), 2019
François Chollet · 1911
Earlier work this paper cites.
Complexity results for planning
Tom Bylander · 1991
Earlier work this paper cites.
Hierarchical recurrent neural networks for long-term dependencies
Salah Hihi and Yoshua Bengio · 1995
Earlier work this paper cites.
The distinct modes of vision offered by feedforward and recurrent processing
Victor AF Lamme and Pieter R Roelfsema · 2000
Earlier work this paper cites.
Gamma, alpha, delta, and theta oscillations govern cognitive processes
György Buzsáki · 2000
Earlier work this paper cites.
An integrative theory of prefrontal cortex function
Earl K. Miller and Jonathan D. Cohen · 2001
Earlier work this paper cites.
Efficient backprop
Yann LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 2002
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Real-time computing without stable states: a new framework for neural computation based on perturbations
Wolfgang Maass · 2002
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Rhythms of the Brain
György Buzsáki · 2006
Earlier work this paper cites.
Social cognitive neuroscience: a review of core processes
Matthew D Lieberman · 2007
Earlier work this paper cites.
The brain’s default network: anatomy, function, and relevance to disease
Randy L Buckner, Jessica R Andrews-Hanna, and Daniel L Schacter · 2008
Earlier work this paper cites.
Theta–gamma coupling increases during the learning of item–context associations
Adriano BL Tort, Robert W Komorowski, Joseph R Manns, Nancy J Kopell, and Howard Eichenbaum · 2009
Earlier work this paper cites.
Thinking, fast and slow (farrar, straus and giroux, new york), 2011
Daniel Kahneman and P Egan · 2011
Earlier work this paper cites.
Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons
Lars Buesing, Johannes Bill, Bernhard Nessler, and Wolfgang Maass · 2011
Earlier work this paper cites.
Canonical microcircuits for predictive coding
Andre M Bastos, W Martin Usrey, Rick A Adams, George R Mangun, Pascal Fries, and Karl J Friston · 2012
Earlier work this paper cites.
A large-scale model of the functioning brain
Chris Eliasmith, Terrence C Stewart, Xuan Choo, Trevor Bekolay, Travis DeWolf, Yichuan Tang, and Daniel Rasmussen · 2012
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller · 2013
Earlier work this paper cites.
Dynamic search on the gpu
Mubbasir Kapadia, Francisco Garcia, Cory D. Boatright, and Norman I. Badler · 2013
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The importance of mixed selectivity in complex cognitive tasks
Mattia Rigotti, Omri Barak, Melissa R. Warden, Xiao-Jing Wang, Nathaniel D. Daw, Earl K. Miller, and Stefano Fusi · 2013
Earlier work this paper cites.
Context-dependent computation by recurrent dynamics in prefrontal cortex
Valerio Mante, David Sussillo, Krishna V. Shenoy, and William T. Newsome · 2013
Earlier work this paper cites.
A hierarchy of intrinsic timescales across primate cortex
John D Murray, Alberto Bernacchia, David J Freedman, Ranulfo Romo, Jonathan D Wallis, Xinying Cai, Camillo Padoa-Schioppa, Tatiana Pasternak, Hyojung Seo, Daeyeol Lee, et al · 2014
Earlier work this paper cites.
Theta–gamma cross-frequency coupling relates to the level of human intelligence
Anja Pahor and Norbert Jaušovec · 2014
Earlier work this paper cites.
Neural turing machines, 2014
Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
Earlier work this paper cites.
A clockwork rnn
Jan Koutník, Klaus Greff, Faustino J. Gomez, and Jürgen Schmidhuber · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
The brain’s default mode network
Marcus E Raichle · 2015
Earlier work this paper cites.
Cognitive effort: A neuroeconomic approach
Andrew Westbrook and Todd S Braver · 2015
Earlier work this paper cites.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
Benjamin Scellier and Yoshua Bengio · 2016
Earlier work this paper cites.
Hybrid computing using a neural network with dynamic external memory
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, et al · 2016
Earlier work this paper cites.
Neural GPUs learn algorithms
Lukasz Kaiser and Ilya Sutskever · 2016
Earlier work this paper cites.
Adaptive computation time for recurrent neural networks
Alex Graves · 2016
Earlier work this paper cites.
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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Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
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Recurrent relational networks
Rasmus Berg Palm, Ulrich Paquet, and Ole Winther · 2017
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Large-scale gradients in human cortical organization
Julia M Huntenburg, Pierre-Louis Bazin, and Daniel S Margulies · 2018
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Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
Cited alongside, same era.
Can convolutional neural networks crack sudoku puzzles?
Kyubyong Park · 2018
Cited alongside, same era.
Premise order matters in reasoning with large language models
Xinyun Chen, Ryan A. Chi, Xuezhi Wang, and Denny Zhou · 2024
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Preemptive answer "attacks" on chain-of-thought reasoning
Rongwu Xu, Zehan Qi, and Wei Xu · 2024
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Training large language models to reason in a continuous latent space
Xuan Shen, Yizhou Wang, Xiangxi Shi, Yanzhi Wang, Pu Zhao, and Jiuxiang Gu · 2024
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Language is primarily a tool for communication rather than thought
Evelina Fedorenko, Steven T Piantadosi, and Edward AF Gibson · 2024
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Deepnet: Scaling transformers to 1,000 layers
Hongyu Wang, Shuming Ma, Li Dong, Shaohan Huang, Dongdong Zhang, and Furu Wei · 2024
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Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Lukasz Kaiser · 2018
Cited alongside, same era.
Backpropagation through time and the brain
Timothy P Lillicrap and Adam Santoro · 2019
Cited alongside, same era.
Deep equilibrium models
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2019
Cited alongside, same era.
Root mean square layer normalization
Biao Zhang and Rico Sennrich · 2019
Cited alongside, same era.
Backpropagation and the brain
Timothy P Lillicrap, Adam Santoro, Luke Marris, Colin J Akerman, and Geoffrey Hinton · 2020
Cited alongside, same era.
A solution to the learning dilemma for recurrent networks of spiking neurons
Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, and Wolfgang Maass · 2020
Cited alongside, same era.
Feedback control guides credit assignment in recurrent neural networks
Klara Kaleb, Barbara Feulner, Juan Gallego, and Claudia Clopath · 2024
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Arc prize 2024: Technical report
Francois Chollet, Mike Knoop, Gregory Kamradt, and Bryan Landers · 2024
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Implicit bias of adamw: L inf norm constrained optimization
Shuo Xie and Zhiyuan Li · 2024
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Llama 3: State-of-the-art open weight language models
Meta AI · 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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Scaling exponents across parameterizations and optimizers
Katie E Everett, Lechao Xiao, Mitchell Wortsman, Alexander A Alemi, Roman Novak, Peter J Liu, Izzeddin Gur, Jascha Sohl-Dickstein, Leslie Pack Kaelbling, Jaehoon Lee, and Jeffrey Pennington · 2024
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Learning iterative reasoning through energy diffusion
Yilun Du, Jiayuan Mao, and Josh Tenenbaum · 2024
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Tri Dao and Albert Gu · 2024
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Transformers in DLOGTIME-uniform TC 0 \text{TC}^{0}
David Chiang · 2025
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Reasoning beyond language: A comprehensive survey on latent chain-of-thought reasoning, 2025
Xinghao Chen, Anhao Zhao, Heming Xia, Xuan Lu, Hanlin Wang, Yanjun Chen, Wei Zhang, Jian Wang, Wenjie Li, and Xiaoyu Shen · 2025
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Arc-agi-2: A new challenge for frontier ai reasoning systems
Francois Chollet, Mike Knoop, Gregory Kamradt, Bryan Landers, and Henry Pinkard · 2025
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Simplifying deep temporal difference learning, 2025
Matteo Gallici, Mattie Fellows, Benjamin Ellis, Bartomeu Pou, Ivan Masmitja, Jakob Nicolaus Foerster, and Mario Martin · 2025
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Grokking at the edge of numerical stability
Lucas Prieto, Melih Barsbey, Pedro A. M. Mediano, and Tolga Birdal · 2025
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jax.nn.initializers.lecun_normal
JAX Developers · 2025
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https://hodoku.sourceforge.net/en/tech_singles.php
Single-digit techniques · 2025
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Tdoku: A fast sudoku solver and generator
Tom Dillion · 2025
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Sudoku-bench: Evaluating creative reasoning with sudoku variants
Jeffrey Seely, Yuki Imajuku, Tianyu Zhao, Edoardo Cetin, and Llion Jones · 2025
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Luke Darlow, Ciaran Regan, Sebastian Risi, Jeffrey Seely, and Llion Jones · 2025
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Dualformer: Controllable fast and slow thinking by learning with randomized reasoning traces, 2025
DiJia Su, Sainbayar Sukhbaatar, Michael Rabbat, Yuandong Tian, and Qinqing Zheng · 2025
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Arc-agi without pretraining, 2025
Isaac Liao and Albert Gu · 2025
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Rarely categorical, always high-dimensional: how the neural code changes along the cortical hierarchy
Lorenzo Posani, Shuqi Wang, Samuel P Muscinelli, Liam Paninski, and Stefano Fusi · 2025
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Scaling up test-time compute with latent reasoning: A recurrent depth approach, 2025
Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, and Tom Goldstein · 2025
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Reinforcement learning for reasoning in large language models with one training example, 2025
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