The following is the list of all accepted papers of the DynaFront workshop, held at NeurpIPS 2025.

A New Approach to Controlling Linear Dynamical Systems - Anand Paresh Brahmbhatt, Gon Buzaglo, Sofiia Druchyna, Elad Hazan 

A State Space Dynamics Perspective on Differentially Private Decentralized Learning - Antti Koskela, Tejas Kulkarni 

A Stochastic Differential Equation Framework for Multi-Objective LLM Interactions: Dynamical Systems Analysis with Code Generation Applications - Shivani Shukla, Himanshu Joshi 

Adaptive Federated Learning via Dynamical System Model - Aayushya Agarwal

Adaptive kernel selection for Stein Variational Gradient Descent - Moritz Melcher, Simon Weissmann, Ashia C. Wilson, Jakob Zech 

Almost sure convergence analysis of regularized SGD - Sebastian Kassing, Simon Weissmann, Leif Döring 

ALPs: Adaptive Lookahead Policy Gradients for Multi-Agent Reinforcement Learning - Aniket Sanyal, Baraah A. M. Sidahmed, Rebekka Burkholz, Tatjana Chavdarova 

An empirical study of Fictitious Play for estimating Nash equilibria in first-price auctions with correlated values - Benjamin Heymann, Panayotis Mertikopoulos 

An ODE method approach for proving convergence and stability of Q-learning in Hierarchical Reinforcement Learning - Massimiliano Manenti, Andrea Iannelli 

Automated Algorithm Design via Nevanlinna-Pick Interpolation - Ibrahim Kurban Ozaslan, Tryphon Georgiou, Mihailo Jovanovic 

Cautious Optimism: A Meta-Algorithm for Near-Constant Regret in General Games - Ashkan Soleymani, Georgios Piliouras, Gabriele Farina 

Clustering in Self-Attention Dynamics with Wasserstein-Fisher-Rao Gradient Flows - Ziang Chen, Yury Polyanskiy, Philippe Rigollet

Data Generation without Function Estimation - Hadi Daneshmand, Ashkan Soleymani 

Decoding Silence through Neural Estimation in Stochastic Dynamical Systems - Shubham Aggarwal, Dipankar Maity, Tamer Basar 

Deep Reinforcement Learning For Nash Equilibria in Non-Renewable Resource Differential Games - El Mehdi Nezahi, Loubna Benabbou, Hassan Benchekroun 

Design and Analysis of Accelerated Algorithms for Temporal Difference Methods using Dynamical Systems - Anushree Rankawat, Pierre-Luc Bacon 

Distributed Low-Communication Training with Decoupled Momentum Optimization - Sasho Nedelkoski, Alexander Acker, Odej Kao, Soeren Becker, Dominik Scheinert 

Distribution Dynamics in Stochastic Optimization: A Decision-Dependent Formulation - Zhiyu He, Saverio Bolognani, Florian Dorfler, Michael Muehlebach 

Fictitious Play in Product Markov Games With Kullback-Leibler Control Cost - Khaled Nakhleh, Sarper Aydin, Ceyhun Eksin, Sabit Ekin 

From Randomized Hamiltonian Flow to Fast Stochastic Optimization - Austin Feng, Qiang Fu, Xiuyuan Wang, Andre Wibisono 

Game Dynamics in Multi-agent Performative Prediction: A Case Study in Mortgage Competition - Guanghui Wang, Krishna Acharya, Lokranjan Lakshmikanthan, Vidya Muthukumar, Juba Ziani 

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures - Mo Zhou, Weihang Xu, Maryam Fazel, Simon Shaolei Du 

High-dimensional Mean-Field Games by Particle-based Flow Matching - Xiuyuan Cheng, Junghwan Lee, Yao Xie, Jiajia Yu 

IFlowNets: Extending Generative Samplers to Learn Strategies in Incomplete Information Games - Conor Artman, Nicholas Di 

Implicit Bias and Loss of Plasticity in Matrix Completion: Depth Promotes Low-Rank Solutions - Baekrok Shin, Chulhee Yun 

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models - Wenlong Mou

Landing with the Score: Riemannian Optimization through Denoising - Andrey Kharitenko, Zebang Shen, Riccardo De Santi, Niao He, Florian Dorfler 

Larger Datasets Can Be Repeated More: A Theoretical Analysis of Multi-Epoch Scaling in Linear Regression - Tingkai Yan, Haodong Wen, Binghui Li, Kairong Luo, Wenguang Chen, Kaifeng Lyu 

Last-Iterate Guarantees in Noisy Games via BNN Dynamics - Tuo Zhang, Leonardo Stella 

Learning by solving differential equations - Benoit Dherin, Michael Munn, Hanna Mazzawi, Michael Wunder, Sourabh Medapati, Javier Gonzalvo 

Learning Velocity Prior-Guided Hamiltonian-Jacobi Flows with Unbalanced Optimal Transport - Amy Xiang Wang

Lyapunov–function-based framework for smooth strongly convex strongly concave min–max optimization algorithms - Mansi Rankawat, Damien Scieur, Simon Lacoste-Julien 

Metriplectic Conditional Flow Matching for Dissipative Dynamics - Ali Baheri, Lars Lindemann 

On Separation Between Best-Iterate, Random-Iterate, and Last-Iterate Convergence of Learning in Games - Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng 

On the Mixing Time of Unadjusted Hamiltonian Monte Carlo in KL Divergence - Nawaf Bou-Rabee, Siddharth Mitra, Andre Wibisono 

Optimal Algorithms for Bandit Learning in Matching Markets - Tejas Pagare, Agniv Bandyopadhyay, Sandeep Kumar Juneja 

PAC-Bayes Generalization bounds for Score Based Diffusion Models - Avrajit Ghosh, Rongrong Wang 

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts - Zeman Li, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, Vahab Mirrokni 

Preference Graphs and the Attractors of Regularized Learning in Games - Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos 

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective - Weixin Wang, Haoyang Zheng, Guang Lin, Wei Deng, Pan Xu 

Schrödinger Bridge as Robustified Optimal Transport Flows - Jinxin Wang, Ya-Ping Hsieh, Bahar Taskesen 

Stochastic Lie Bracket Approximations for Zeroth-Order Optimization on Manifolds - Mahmoud Abdelgalil, Jorge I Poveda 

Task-Level Insights from Eigenvalues across Sequence Models - Jelena Trisovic, Rahel Rickenbach, Alexandre Didier, Melanie Zeilinger, Jerome Sieber 

Unifying Distributed Optimization, Algorithmic Stability, and Privacy-Preserving Learning through Converse Lyapunov Theory - Guner Dilsad ER, Michael Muehlebach 

Wasserstein Fisher Rao Gradient Flows: Operating Splitting & Convergence Speed - Sahani Pathiraja, Francesca Romana Crucinio 

When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis - Xiang Li, Zebang Shen, Ya-Ping Hsieh, Niao He