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Workshop Mon, Apr 27, 2026 • 5:00 AM – 1:00 PM PDT 201 A/B

ReALM-GEN: Real-World Constrained and Preference-Aligned Flow- and Diffusion-based Generative Models

Paris Giampouras ⋅ Morteza Mardani ⋅ Yingzhen Li ⋅ Giannis Daras ⋅ Johann Wenckstern ⋅ Charlotte Bunne ⋅ Johann Wenckstern
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Abstract

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Schedule

Timezone: America/Los_Angeles
5:00 AM
Opening Remarks
Paris Giampouras
5:05 AM
Discrete Diffusion Language Models
Volodymyr Kuleshov
6:05 AM
Coffee Break
→ UniGuide: Learning Guidance Policies for Multi-Objective Diffusion Sampling
Mahmoud Hegazy ⋅ Navid Bagheri Shouraki ⋅ Eric Moulines ⋅ Aymeric Dieuleveut ⋅ Michael Jordan ⋅ Yazid Janati el idrissi
→ Guided Star-Shaped Masked Diffusion
Viacheslav Meshchaninov ⋅ Egor Shibaev ⋅ Artem Makoian ⋅ Ivan Klimov ⋅ Nikita Balagansky ⋅ Daniil Gavrilov ⋅ Aibek Alanov ⋅ Dmitry P. Vetrov
→ WIND: Weather Inverse Diffusion for Zero-Shot Atmospheric Modeling
Michael Aich ⋅ Andreas Fürst ⋅ Florian Sestak ⋅ Carlos Ruiz-Gonzalez ⋅ Niklas Boers ⋅ Johannes Brandstetter
→ Divide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models
Abhi Gupta ⋅ Polina Barabanshchikova ⋅ Vikas Garg ⋅ Samuel Kaski ⋅ Tommi Jaakkola
→ Enhancing Reasoning for Diffusion LLMs via Distribution Matching Policy Optimization
Yuchen Zhu ⋅ Wei Guo ⋅ Jaemoo Choi ⋅ Petr Molodyk ⋅ Bo Yuan ⋅ Molei Tao ⋅ Yongxin Chen
→ Optimizing Remasking Schedules for Reasoning in Discrete Diffusion Models
Radostin Cholakov ⋅ Zeyneb Kaya ⋅ Nicole H. Ma
→ MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models
Hojung Jung ⋅ Rodrigo Hormazabal ⋅ Jaehyeong Jo ⋅ Youngrok Park ⋅ Kyunggeun Roh ⋅ Se-Young Yun ⋅ Sehui Han ⋅ Dae-Woong Jeong
→ MacroGuide: Topological Guidance for Macrocycle Generation
Alicja Maksymiuk ⋅ Alexandre Duplessis ⋅ Michael Bronstein ⋅ Alexander Tong ⋅ Fernanda Duarte ⋅ Ismail I Ceylan
→ AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidance
Tong Chen ⋅ Yinuo Zhang ⋅ Sophia Vincoff ⋅ Pranam Chatterjee
→ Aligning Diffusion Language Models via Unpaired Preference Optimization
Vaibhav Jindal ⋅ Hejian Sang ⋅ Chun-Mao Lai ⋅ Yanning Chen ⋅ Zhipeng Wang
→ TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation
Hanqun Cao ⋅ Aastha Pal ⋅ Sophia Tang ⋅ Yinuo Zhang ⋅ Jingjie Zhang ⋅ Pheng-Ann Heng ⋅ Pranam Chatterjee
→ EntRGi: Entropy Aware Reward Guidance for Diffusion Language Models
Atula T Neerkaje ⋅ Litu Rout ⋅ Constantine Caramanis ⋅ Sanjay Shakkottai ⋅ sujay sanghavi
→ Bad-OOD: Discovering Harmful Synthetic Diffusion Outliers via Confidence Calibration
Donglin Ni ⋅ Zhixin Feng ⋅ Ke Li ⋅ Yue Zhang ⋅ Yonggang Qi
→ Inference-Time CLIP Embedding Manipulation for Compositional Text-to-Image Alignment
Sujung Hong ⋅ Tae Eun Choi ⋅ Youngjun Jun ⋅ Chanyong Yoon ⋅ Seong Jae Hwang
→ CMAD: Cooperative Multi-Agent Diffusion via Stochastic Optimal Control
Riccardo Barbano ⋅ Alexander Denker ⋅ Runchang Li ⋅ Zeljko Kereta ⋅ Francisco Vargas
→ Learning Illumination Control in Diffusion Models
Nishit Anand ⋅ Manan Suri ⋅ Christopher Metzler ⋅ Dinesh Manocha ⋅ Ramani Duraiswami
→ Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
Jaemoo Choi ⋅ Yuchen Zhu ⋅ Wei Guo ⋅ Petr Molodyk ⋅ Bo Yuan ⋅ Jinbin Bai ⋅ Yi Xin ⋅ Molei Tao ⋅ Yongxin Chen
→ A Unified Density Operator View of Flow Control and Merging
Riccardo De Santi ⋅ Malte Franke ⋅ Ya-Ping Hsieh ⋅ Andreas Krause
→ Constraint-Aware Flow Matching via Randomized Exploration
Zhengyan Huan ⋅ Jacob Boerma ⋅ Liping Liu ⋅ Shuchin Aeron
→ Decoupling Tilting from Transport: Stable Online Alignment of Flow and Diffusion Policies
Chubin Zhang ⋅ Zhenglin Wan ⋅ Feng Chen ⋅ Fuchao Yang ⋅ Lang Feng ⋅ Yaxin Zhou ⋅ Xingrui Yu ⋅ Yang You ⋅ Ivor Tsang ⋅ Bo An
→ Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage
Xin Ju ⋅ Jiachen Yao ⋅ anima anandkumar ⋅ Sally Benson ⋅ Gege Wen
→ Generative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control
Zunzhe Zhang ⋅ Runhan Huang ⋅ Yicheng Liu ⋅ Shaoting Zhu ⋅ Linzhan Mou ⋅ Hang Zhao
→ Diffusion Policy Optimization without Drifting Apart
Haozhe Jiang ⋅ Haiwen Feng ⋅ Jiantao Jiao ⋅ Angjoo Kanazawa ⋅ Nika Haghtalab
→ Activation Steering for Masked Diffusion Language Models
Adi Shnaidman ⋅ Erin Feiglin ⋅ Osher Yaari ⋅ Efrat Mentel ⋅ Amit LeVi ⋅ Raz Lapid
→ Protein generation with embedding learning for motif diversification
Kevin Michalewicz ⋅ Chen Jin ⋅ Philip Teare ⋅ Tom Diethe ⋅ Mauricio Barahona ⋅ Barbara Bravi ⋅ Asher Mullokandov
→ Disentangled Compositional Diffusion for Controllable Scientific Data Generation
Nandan Madhuj ⋅ Meet Parikh ⋅ Anirban Samaddar ⋅ Yixuan Sun ⋅ Sandeep Madireddy ⋅ Jian-Xun Wang
→ Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
Zifan Wang ⋅ Riccardo De Santi ⋅ Xiaoyu Mo ⋅ Michael Zavlanos ⋅ Andreas Krause ⋅ Karl H. Johansson
→ Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps
Peter Holderrieth ⋅ Douglas Chen ⋅ Luca Eyring ⋅ Ishin Shah ⋅ Giridharan Anantharaman ⋅ Yutong (Kelly) He ⋅ Zeynep Akata ⋅ Tommi Jaakkola ⋅ Nicholas Boffi ⋅ Max Simchowitz
→ DDNO: Discrete Diffusion Noise Optimization
Luca Eyring ⋅ Vincent Pauline ⋅ Stefan Bauer ⋅ Zeynep Akata ⋅ Alexey Dosovitskiy
→ GlyphFashion: Fine-Grained Text-Aware Fashion Image Editing via Diffusion Models
Yanting Zhang ⋅ Jingyi Guo ⋅ Huanwen Zheng ⋅ Cairong Yan ⋅ Yonggang Qi ⋅ Gaoang Wang
→ Di3PO - Diptych Diffusion DPO for Targeted Improvements in Image
Sanjana Reddy ⋅ Ishaan Malhi ⋅ Sally Ma ⋅ Praneet Dutta
8:15 AM
Lunch Break
11:15 AM
Coffee Break
12:00 PM
Poster Session 2
→ Self-Improving Vision-Language-Action Models with Data Generation via Residual RL
Wenli Xiao ⋅ Haotian Lin ⋅ Andy Peng ⋅ Haoru Xue ⋅ Tairan He ⋅ Zhengyi Luo ⋅ Yuqi Xie ⋅ Fengyuan Hu ⋅ Jim Fan ⋅ Guanya Shi ⋅ Yuke Zhu
→ LieFlower: Controlling Target Protein Dynamics via Lie-Guided Discrete Flows
Manu Ponnapati Lynch ⋅ Tong Chen ⋅ Yinuo Zhang ⋅ Pranam Chatterjee
→ Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective
Yinan Huang ⋅ Hans Hao-Hsun Hsu ⋅ Junran Wang ⋅ Bo Dai ⋅ Pan Li
→ Full-length mRNA Design with Reward-Guided Masked Diffusion Model Fine-Tuning
Sawan Patel ⋅ Sophia Tang ⋅ Pranam Chatterjee ⋅ Sherwood Yao
→ Representation Alignment for Inverse Problems with Diffusion and Flow-Based Models
Loukas Sfountouris ⋅ Giannis Daras ⋅ Paris Giampouras
→ From Noise to Control: Parameterized Diffusion Policies
Renhao Zhang ⋅ Haotian Fu ⋅ Mingxi Jia ⋅ George D Konidaris ⋅ Yilun Du ⋅ Bruno Silva
→ Verifier-Threshold: An Efficient Test-Time Scaling Approach for Image Generation
Vignesh Sundaresha ⋅ Akash Haridas ⋅ Vikram Appia ⋅ Lav R Varshney
→ Stochastic Few-step Models
Romeo Passaro ⋅ Zander Blasingame ⋅ Michael Bronstein ⋅ Alexander Tong
→ Guess & Guide: Gradient-Free Zero-Shot Diffusion Guidance
Abduragim Shtanchaev ⋅ Albina Ilina ⋅ Yazid Janati el idrissi ⋅ Arip Asadulaev ⋅ Martin Takáč ⋅ Eric Moulines
→ Time-to-Move: Training-Free Motion-Controlled Video Generation via Dual-Clock Denoising
Assaf Singer ⋅ Noam rotstein ⋅ Amir Mann ⋅ Ron Kimmel ⋅ Or Litany
→ When Test-Time Guidance Is Enough: Fast Image and Video Editing with Diffusion Guidance
Ahmed Ghorbel ⋅ Badr Moufad ⋅ Navid Bagheri Shouraki ⋅ Alain Oliviero Durmus ⋅ Thomas Hirtz ⋅ Eric Moulines ⋅ Jimmy Olsson ⋅ Yazid Janati el idrissi
→ Flexible Locomotion Learning with Diffusion Model Predictive Control
Runhan Huang ⋅ Haldun Balim ⋅ Heng Yang ⋅ Yilun Du
→ RFG: Test-Time Scaling for Diffusion Large Language Model Reasoning with Reward-Free Guidance
Tianlang Chen ⋅ Minkai Xu ⋅ Jure Leskovec ⋅ Stefano Ermon
→ Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models
Donghoon Ahn ⋅ Jiwon Kang ⋅ Sanghyun Lee ⋅ Minjae Kim ⋅ Jaewon Min ⋅ Wooseok Jang ⋅ Sangwu Lee ⋅ Sayak Paul ⋅ Susung Hong ⋅ Seungryong Kim
→ Diffusion Alignment Beyond KL: Variance Minimisation as Effective Policy Optimiser
Zijing Ou ⋅ Jacob Si ⋅ Junyi Zhu ⋅ Ondrej Bohdal ⋅ Mete Ozay ⋅ Taha Ceritli ⋅ Yingzhen Li
→ Variational Entropic Optimal Transport
Roman Dyachenko ⋅ Nikita Gushchin ⋅ Kirill Sokolov ⋅ Petr Mokrov ⋅ Evgeny Burnaev ⋅ Aleksandr Korotin
→ Flow-Factory: A unified framework for easy reinforcement learning in Flow-Matching models
Bowen Ping ⋅ Chengyou Jia ⋅ Minnan Luo ⋅ Hangwei Qian ⋅ Ivor Tsang
→ Diffuse and Steer: Corrective Sampling for Stable 3D Molecular Diffusion
Fang Wan ⋅ Wenhan Gao ⋅ Jingxiang Qu ⋅ Yi Liu
→ Discrete Diffusion Inference-Time Control With Nested Sequential Monte Carlo
Lohithsai Yadala Chanchu ⋅ Hany Abdulsamad ⋅ Christian A. Naesseth
→ Categorical Reparameterization with Denoising Diffusion models
Samson Gourevitch ⋅ Eric Moulines ⋅ Alain Oliviero Durmus ⋅ Jimmy Olsson ⋅ Yazid Janati el idrissi
→ Accelerating Diffusion Language Models via Inverse Distillation
David Li ⋅ Nikita Gushchin ⋅ Dmitry Abulkhanov ⋅ Eric Moulines ⋅ Ivan Oseledets ⋅ Maxim Panov ⋅ Aleksandr Korotin
→ Adapting Diffusion Policies to Novel Environments via Policy-Steered Optimization
Rory Thompson ⋅ Sergio Orozco ⋅ Eric Rosen ⋅ Karl Schmeckpeper ⋅ George D Konidaris
→ pCoMole: Pareto-Constrained Molecule Editing with Discrete Flows
Tong Chen ⋅ Maximilian Holsman ⋅ Lin Zhao ⋅ Yinuo Zhang ⋅ Pranam Chatterjee
→ Action-Informed Belief via Latent Flow Matching for Memory-Intensive Robotic Tasks
Dawei Wang ⋅ Xingrui Yu ⋅ Zhenglin Wan ⋅ Ivor Tsang
→ A2D2: Finetuning Any-Length Discrete Diffusion for Adaptive Decoding
Sophia Tang ⋅ Yuchen Zhu ⋅ Molei Tao ⋅ Pranam Chatterjee
→ CausalSliders: Graph-Guided LoRA Interventions for Causally Consistent Image Editing
Aditi Tiwari ⋅ Akshit Bhalla ⋅ Darshan Prasad ⋅ Heng Ji
→ MOOSE: Targeted Small Molecule Generation via Multi-Objective-Guided Discrete Diffusion
Elizabeth Mahood ⋅ William Pattie ⋅ Ethan Jones ⋅ Sophia Vincoff ⋅ Adi Mashiach ⋅ Pranam Chatterjee
→ From Diffusion to Flow: Efficient Motion Generation in MotionGPT3
Jaymin Ban ⋅ JiHong Jeon ⋅ SangYeop Jeong
→ Understanding Sampler Stochasticity in Training Diffusion Models for RLHF
Jiayuan Sheng ⋅ Hanyang Zhao ⋅ Haoxian Chen ⋅ David Yao ⋅ Wenpin Tang
→ Diffusion Priors for Lightweight Personal- ized Image Generation
Gabriel Patron ⋅ Zhiwei Xu ⋅ Ishan Kapnadak ⋅ Felipe Maia Polo
→ Guided Transfer Learning for Discrete Diffusion Models
Julian Kleutgens ⋅ Claudio Battiloro ⋅ Lingkai Kong ⋅ Benjamin F Grewe ⋅ Francesca Dominici ⋅ Mauricio Tec
→ Diffusion-Driven Latent Guided Sampling for Neural Combinatorial Optimization
Sobihan Surendran ⋅ Adeline Fermanian ⋅ Sylvain Le Corff
→ Adaptive Order Policies for Masked Diffusion
Mohsin Hasan ⋅ Jama Hussein Mohamud ⋅ Mirco Ravanellu ⋅ Yoshua Bengio