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Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole von Lilienfeld · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Probability-1
Albert N Shiryaev · 2016
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Recurrent relational networks
Rasmus Palm, Ulrich Paquet, and Ole Winther · 2018
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Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya Donti, Bryan Wilder, and Zico Kolter · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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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 · 2021
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Learning iterative reasoning through energy minimization
Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua B. Tenenbaum, and Sergey Levine · 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.
Laion-aesthetics
Christoph Schuhmann and Romain Beaumont · 2022
Cited alongside, same era.
Is conditional generative modeling all you need for decision making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, and Pulkit Agrawal · 2023
Cited alongside, same era.
Training diffusion models with reinforcement learning
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2023
Cited alongside, same era.
A configurable library for generating and manipulating maze datasets
Derivative-free guidance in continuous and discrete diffusion models with soft value-based decoding
Xiner Li, Yulai Zhao, Chenyu Wang, Gabriele Scalia, Gokcen Eraslan, Surag Nair, Tommaso Biancalani, Shuiwang Ji, Aviv Regev, Sergey Levine, et al · 2024
Later among the works it cites.
Diffusion model predictive control
Guangyao Zhou, Sivaramakrishnan Swaminathan, Rajkumar Vasudeva Raju, J Swaroop Guntupalli, Wolfgang Lehrach, Joseph Ortiz, Antoine Dedieu, Miguel Lázaro-Gredilla, and Kevin Murphy · 2024
Later among the works it cites.
Posterior inference in sequential models with soft value guidance
Rob Brekelmans · 2025
Closest in time.
Dynamic search for inference-time alignment in diffusion models
Xiner Li, Masatoshi Uehara, Xingyu Su, Gabriele Scalia, Tommaso Biancalani, Aviv Regev, Sergey Levine, and Shuiwang Ji · 2025
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Inference-time scaling for diffusion models beyond scaling denoising steps
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Michael Igorevich Ivanitskiy, Rusheb Shah, Alex F Spies, Tilman Räuker, Dan Valentine, Can Rager, Lucia Quirke, Chris Mathwin, Guillaume Corlouer, Cecilia Diniz Behn, et al · 2023
Cited alongside, same era.
Practical and asymptotically exact conditional sampling in diffusion models
Luhuan Wu, Brian Trippe, Christian Naesseth, David Blei, and John P Cunningham · 2023
Cited alongside, same era.
Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li · 2023
Cited alongside, same era.
Human preference score: Better aligning text-to-image models with human preference
Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li · 2023
Cited alongside, same era.
Diffusion forcing: Next-token prediction meets full-sequence diffusion
Boyuan Chen, Diego Martí Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitzmann · 2024
Cited alongside, same era.
Zihan Ding, Amy Zhang, Yuandong Tian, and Qinqing Zheng · 2024
Cited alongside, same era.
Learning iterative reasoning through energy diffusion
Yilun Du, Jiayuan Mao, and Joshua B. Tenenbaum · 2024
Cited alongside, same era.
Nanye Ma, Shangyuan Tong, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi Jaakkola, Xuhui Jia, et al · 2025
Closest in time.
Inference-time text-to-video alignment with diffusion latent beam search
Yuta Oshima, Masahiro Suzuki, Yutaka Matsuo, and Hiroki Furuta · 2025
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Ogbench: Benchmarking offline goal-conditioned rl
Seohong Park, Kevin Frans, Benjamin Eysenbach, and Sergey Levine · 2025
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A general framework for inference-time scaling and steering of diffusion models
Raghav Singhal, Zachary Horvitz, Ryan Teehan, Mengye Ren, Zhou Yu, Kathleen McKeown, and Rajesh Ranganath · 2025
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Inference-time alignment in diffusion models with reward-guided generation: Tutorial and review
Masatoshi Uehara, Yulai Zhao, Chenyu Wang, Xiner Li, Aviv Regev, Sergey Levine, and Tommaso Biancalani · 2025
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Spatial reasoning with denoising models
Christopher Wewer, Bartlomiej Pogodzinski, Bernt Schiele, and Jan Eric Lenssen · 2025
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Monte carlo tree diffusion for system 2 planning
Jaesik Yoon, Hyeonseo Cho, Doojin Baek, Yoshua Bengio, and Sungjin Ahn · 2025
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T-scend: Test-time scalable mcts-enhanced diffusion model
Tao Zhang, Jia-Shu Pan, Ruiqi Feng, and Tailin Wu · 2025
Closest in time.