A Machine Learning Approach to the Nirenberg Problem
arXiv:2602.12368v1 Announce Type: new Abstract: This work introduces the Nirenberg Neural Network: a numerical approach to the Nirenberg problem of prescribing Gaussian curvature on $S^2$ for metrics that are pointwise conformal to the round metric. Our mesh-free physics-informed neural network...
Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation
arXiv:2602.12379v1 Announce Type: new Abstract: Estimating longitudinal treatment effects is essential for sequential decision-making but is challenging due to treatment-confounder feedback. While Iterative Conditional Expectation (ICE) G-computation offers a principled approach, its recursive structure suffers from error propagation, corrupting the...
High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functions
arXiv:2602.12391v1 Announce Type: new Abstract: Level set estimation (LSE) classifies whether an unknown function's value exceeds a specified threshold for given inputs, a fundamental problem in many real-world applications. In active learning settings with limited initial data, we aim to...
Synthetic Interaction Data for Scalable Personalization in Large Language Models
arXiv:2602.12394v1 Announce Type: new Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization methods primarily focus on task-level optimization while largely overlooking user-specific preferences and latent constraints of individual users....
Computationally sufficient statistics for Ising models
arXiv:2602.12449v1 Announce Type: new Abstract: Learning Gibbs distributions using only sufficient statistics has long been recognized as a computationally hard problem. On the other hand, computationally efficient algorithms for learning Gibbs distributions rely on access to full sample configurations generated...
Regularized Meta-Learning for Improved Generalization
arXiv:2602.12469v1 Announce Type: new Abstract: Deep ensemble methods often improve predictive performance, yet they suffer from three practical limitations: redundancy among base models that inflates computational cost and degrades conditioning, unstable weighting under multicollinearity, and overfitting in meta-learning pipelines. We...
Tight Bounds for Logistic Regression with Large Stepsize Gradient Descent in Low Dimension
arXiv:2602.12471v1 Announce Type: new Abstract: We consider the optimization problem of minimizing the logistic loss with gradient descent to train a linear model for binary classification with separable data. With a budget of $T$ iterations, it was recently shown that...
A Theoretical Analysis of Mamba's Training Dynamics: Filtering Relevant Features for Generalization in State Space Models
arXiv:2602.12499v1 Announce Type: new Abstract: The recent empirical success of Mamba and other selective state space models (SSMs) has renewed interest in non-attention architectures for sequence modeling, yet their theoretical foundations remain underexplored. We present a first-step analysis of generalization...
On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs
arXiv:2602.12506v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision language models (VLMs). While RL-tuned VLMs improve on visual reasoning benchmarks, they...
Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games
arXiv:2602.12517v1 Announce Type: new Abstract: The intersection of Mean Field Games (MFGs) and Reinforcement Learning (RL) has fostered a growing family of algorithms designed to solve large-scale multi-agent systems. However, the field currently lacks a standardized evaluation protocol, forcing researchers...
Multi-Agent Model-Based Reinforcement Learning with Joint State-Action Learned Embeddings
arXiv:2602.12520v1 Announce Type: new Abstract: Learning to coordinate many agents in partially observable and highly dynamic environments requires both informative representations and data-efficient training. To address this challenge, we present a novel model-based multi-agent reinforcement learning framework that unifies joint...
Analytical Results for Two Exponential Family Distributions in Hierarchical Dirichlet Processes
arXiv:2602.12527v1 Announce Type: new Abstract: The Hierarchical Dirichlet Process (HDP) provides a flexible Bayesian nonparametric framework for modeling grouped data with a shared yet unbounded collection of mixture components. While existing applications of the HDP predominantly focus on the Dirichlet-multinomial...
Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models
arXiv:2602.12529v1 Announce Type: new Abstract: Reinforcement learning has emerged as a promising paradigm for aligning diffusion and flow-matching models with human preferences, yet practitioners face fragmented codebases, model-specific implementations, and engineering complexity. We introduce Flow-Factory, a unified framework that decouples...
AMPS: Adaptive Modality Preference Steering via Functional Entropy
arXiv:2602.12533v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) often exhibit significant modality preference, which is a tendency to favor one modality over another. Depending on the input, they may over-rely on linguistic priors relative to visual evidence, or...
Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference
arXiv:2602.12542v1 Announce Type: new Abstract: Deep learning models for clinical event prediction on electronic health records (EHR) often suffer performance degradation when deployed under different data distributions. While domain adaptation (DA) methods can mitigate such shifts, its "black-box" nature prevents...
Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling
arXiv:2602.12567v1 Announce Type: new Abstract: Federated learning on connected electric vehicles (BEVs) faces severe instability due to intermittent connectivity, time-varying client participation, and pronounced client-to-client variation induced by diverse operating conditions. Conventional FedAvg and many advanced methods can suffer from...
VI-CuRL: Stabilizing Verifier-Independent RL Reasoning via Confidence-Guided Variance Reduction
arXiv:2602.12579v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a dominant paradigm for enhancing Large Language Models (LLMs) reasoning, yet its reliance on external verifiers limits its scalability. Recent findings suggest that RLVR primarily functions...
Power Interpretable Causal ODE Networks: A Unified Model for Explainable Anomaly Detection and Root Cause Analysis in Power Systems
arXiv:2602.12592v1 Announce Type: new Abstract: Anomaly detection and root cause analysis (RCA) are critical for ensuring the safety and resilience of cyber-physical systems such as power grids. However, existing machine learning models for time series anomaly detection often operate as...
RelBench v2: A Large-Scale Benchmark and Repository for Relational Data
arXiv:2602.12606v1 Announce Type: new Abstract: Relational deep learning (RDL) has emerged as a powerful paradigm for learning directly on relational databases by modeling entities and their relationships across multiple interconnected tables. As this paradigm evolves toward larger models and relational...
Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction
arXiv:2602.12613v1 Announce Type: new Abstract: Temporal Graph Neural Networks (TGNNs) are pivotal in processing dynamic graphs. However, existing TGNNs primarily target one-time predictions for a given temporal span, whereas many practical applications require continuous predictions, that predictions are issued frequently...
Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection
arXiv:2602.12622v1 Announce Type: new Abstract: Internet of things (IoT) networks face increasing security threats due to their distributed nature and resource constraints. Although federated learning (FL) has gained prominence as a privacy-preserving framework for distributed IoT environments, current federated principal...
Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps
arXiv:2602.12624v1 Announce Type: new Abstract: Diffusion-based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited by high sampling costs. While prior work focuses on training objectives or individual solvers, the holistic design of...
Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL
arXiv:2602.12636v1 Announce Type: new Abstract: Designing suitable rewards poses a significant challenge in reinforcement learning (RL), especially for embodied manipulation. Trajectory success rewards are suitable for human judges or model fitting, but the sparsity severely limits RL sample efficiency. While...
Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics
arXiv:2602.12643v1 Announce Type: new Abstract: We present Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that unifies the efficiency of model-free methods with the representational strengths of model-based approaches, without incurring planning overhead. By embedding state-action pairs into a...
Uncovering spatial tissue domains and cell types in spatial omics through cross-scale profiling of cellular and genomic interactions
arXiv:2602.12651v1 Announce Type: new Abstract: Cellular identity and function are linked to both their intrinsic genomic makeup and extrinsic spatial context within the tissue microenvironment. Spatial transcriptomics (ST) offers an unprecedented opportunity to study this, providing in situ gene expression...
SLA2: Sparse-Linear Attention with Learnable Routing and QAT
arXiv:2602.12675v1 Announce Type: new Abstract: Sparse-Linear Attention (SLA) combines sparse and linear attention to accelerate diffusion models and has shown strong performance in video generation. However, (i) SLA relies on a heuristic split that assigns computations to the sparse or...
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