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Synthetic Tabular Generators Fail to Preserve Behavioral Fraud Patterns: A Benchmark on Temporal, Velocity, and Multi-Account Signals Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates Automated co-design of high-performance thermodynamic cycles via graph-based hierarchical reinforcement learning Bias-Corrected Adaptive Conformal Inference for Multi-Horizon Time Series Forecasting Counterfactual Peptide Editing for Causal TCR--pMHC Binding Inference Binomial Gradient-Based Meta-Learning for Enhanced Meta-Gradient Estimation Enhancing Confidence Estimation in Telco LLMs via Twin-Pass CoT-Ensembling MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models Physics-informed reservoir characterization from bulk and extreme pressure events with a differentiable simulator Some Theoretical Limitations of t-SNE Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding Multi-Task LLM with LoRA Fine-Tuning for Automated Cancer Staging and Biomarker Extraction Text-Attributed Knowledge Graph Enrichment with Large Language Models for Medical Concept Representation Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation Models When Less Latent Leads to Better Relay: Information-Preserving Compression for Latent Multi-Agent LLM Collaboration BioTrain: Sub-MB, Sub-50mW On-Device Fine-Tuning for Edge-AI on Biosignals Linear Probe Accuracy Scales with Model Size and Benefits from Multi-Layer Ensembling Dataset-Level Metrics Attenuate Non-Determinism: A Fine-Grained Non-Determinism Evaluation in Diffusion Language Models WIN-U: Woodbury-Informed Newton-Unlearning as a retain-free Machine Unlearning Framework FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding Universality of Gaussian-Mixture Reverse Kernels in Conditional Diffusion Computational framework for multistep metabolic pathway design LEGO-MOF: Equivariant Latent Manipulation for Editable, Generative, and Optimizable MOF Design Learning Inference Concurrency in DynamicGate MLP Structural and Mathematical Justification Parameter-efficient Quantum Multi-task Learning Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges Self-Organizing Maps with Optimized Latent Positions A Bayesian Framework for Uncertainty-Aware Explanations in Power Quality Disturbance Classification Optimization with SpotOptim Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs Spectral Thompson sampling Online learning with noisy side observations Character Beyond Speech: Leveraging Role-Playing Evaluation in Audio Large Language Models via Reinforcement Learning Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate Composite Silhouette: A Subsampling-based Aggregation Strategy RPS: Information Elicitation with Reinforcement Prompt Selection UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization Beyond State Consistency: Behavior Consistency in Text-Based World Models Simulation-Based Optimisation of Batting Order and Bowling Plans in T20 Cricket Hardware-Efficient Neuro-Symbolic Networks with the Exp-Minus-Log Operator Drowsiness-Aware Adaptive Autonomous Braking System based on Deep Reinforcement Learning for Enhanced Road Safety MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals DiPO: Disentangled Perplexity Policy Optimization for Fine-grained Exploration-Exploitation Trade-Off Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization PRiMeFlow: Capturing Complex Expression Heterogeneity in Perturbation Response Modelling Unsupervised domain transfer: Overcoming signal degradation in sleep monitoring by increasing scoring realism Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification A Complete Symmetry Classification of Shallow ReLU Networks Momentum Further Constrains Sharpness at the Edge of Stochastic Stability Complex Interpolation of Matrices with an application to Multi-Manifold Learning Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. 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A Tight Characterization of Reward Poisoning in Linear MDPs
A Generalized Sinkhorn Algorithm for Mean-Field Schrödinger Bridge
Asmaa Eldesoukey, Yongxin Chen, Abhishek Halder · 2026-04-08 · via cs.LG updates on arXiv.org

The mean-field Schrödinger bridge (MFSB) problem concerns designing a minimum-effort controller that guides a diffusion process with nonlocal interaction to reach a given distribution from another by a fixed deadline. Unlike the standard Schrödinger bridge, the dynamical constraint for MFSB is the mean-field limit of a population of interacting agents with controls. It serves as a natural model for large-scale multi-agent systems. The MFSB is computationally challenging because the nonlocal interaction makes the problem nonconvex. We propose a generalization of the Hopf-Cole transform for MFSB and, building on it, design a Sinkhorn-type recursive algorithm to solve the associated system of integro-PDEs. Under mild assumptions on the interaction potential, we discuss convergence guarantees for the proposed algorithm. We present numerical examples with repulsive and attractive interactions to illustrate the theoretical contributions.