AI News

Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks (arxiv.org)

Weighted universal approximation of differentiable maps on infinite-dimensional manifolds (arxiv.org)

GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks (arxiv.org)

Tensorizing Engram: Sharing Latents Across N-Gram Embeddings is Beneficial in LLMs (arxiv.org)

Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence (arxiv.org)

INFUSER: Influence-Guided Self-Evolution Improves Reasoning (arxiv.org)

Vector Space of Cycles (arxiv.org)

MEC-Cox: Machine-Learning-Assisted Generalized Entropy Calibration for ATT Marginal Hazard-Ratio Estimation (arxiv.org)

Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models (arxiv.org)

LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry (arxiv.org)

Stable and Scalable Probabilistic Numerical Solvers for Stiff and High-Dimensional ODEs (arxiv.org)

Improving the sharpness in neural network-based parametric post-processing of ensemble forecasts (arxiv.org)

Rank Intervals for Leaderboards: A Hierarchical Framework for Model Evaluation (arxiv.org)

Generalization in Nonlinear Least Squares via Learned Feature Geometry (arxiv.org)

Estimate Collapsibility of Causal Effects in Completed Partial DAGs via Strong d-Convex Hulls (arxiv.org)

Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees (arxiv.org)

Accelerating Birkhoff Projection for Manifold-Constrained Hyper-Connections (arxiv.org)

MST-Direct at Scale: Multivariate and Conditional Geostatistical Simulation via Sinkhorn Optimal Transport (arxiv.org)

Vessel Traffic Flow Prediction on Sparse Data via Spatio-Temporal Graph Neural Networks with a Learnable Tweedie Head (arxiv.org)

A Framework for Evaluating and Benchmarking Concept Drift Detection Methods (arxiv.org)

Large-scale empirical tuning and comparison of default optimizers for variational inference (arxiv.org)

Instrumented data for causal scientific machine learning (arxiv.org)

Partially Performative Prediction (arxiv.org)

Inference for High-Dimensional Sparse Spectral Precision Matrices (arxiv.org)

Assessing model calibration with boosting trees (arxiv.org)

How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs (arxiv.org)

A Switching Beamformer for Highly Non-Stationary Environments (arxiv.org)

The Spectral Dynamics and Noise Geometry of Muon (arxiv.org)

When Are Neural Interaction Discoveries Real? Identifiability, Recoverability, and a Pre-Fit Diagnostic (arxiv.org)

Nonparametric undirected graphical model selection using diffusion models (arxiv.org)

Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement (arxiv.org)

CP-factorization for high dimensional tensor time series and double projection iterations (arxiv.org)

Intrinsic Selection and Particle Resampling for Inference-Time Scaling Beyond Domain Verifiability (arxiv.org)

sGPO: Trading Inference FLOPs for Training Efficiency in RLVR (arxiv.org)

Backward Coherence and Hidden-State Stability in Recurrent Neural Networks: A Quasi-Reverse-Martingale Theory (arxiv.org)

Capability-Aligned Hierarchical Learning for Tool-Augmented LLMs (arxiv.org)

Understanding Quantization-Aware Training: Gradients at Quantized Weights Bias to the Low-Loss Basin (arxiv.org)

Data augmented bootstrap: Unifying confidence interval construction by approximate invariance (arxiv.org)

Asymptotic Optimality of Thompson Sampling for Risk-Averse Bandits with Sub-Gaussian Rewards (arxiv.org)

BSTabDiff: Block-Subunit Diffusion Priors for High-Dimensional Tabular Data Generation (arxiv.org)

On Choosing the $\mu$ Parameter in Gaussian Differential Privacy (arxiv.org)

In-Context Learning for Latent Space Bayesian Optimization (arxiv.org)

Beyond Neural Collapse: Task-Intrinsic Geometry Governs Neural Representations in Modular Arithmetic (arxiv.org)

Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation (arxiv.org)

Query Lens: Interpreting Sparse Key-Value Features with Indirect Effects (arxiv.org)

ScaleSweep: Accurate NVFP4 Post-Training Quantization of LLMs via Block Scale Initialization (arxiv.org)

Graph Neural Networks for Predicting Solvability of Finite Groups (arxiv.org)

From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction (arxiv.org)

Position: Anthropomorphic Misalignment Research Needs Stronger Evidence (arxiv.org)

FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction (arxiv.org)