Search: “tutorials”

Diversity-Enriched Option-Critic

arXiv:2011.02565v2 Announce Type: replace-cross Abstract: Temporal abstraction allows reinforcement learning agents to represent knowledge and develop strategies over different temporal scales. The op

DM-KG: A Novel Method for Boosting Spatial Cognition of Vision-Language Models in Street View Imagery

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DGX agent

arXiv:2607.12319v1 Announce Type: new Abstract: As vision-language models (VLMs) are increasingly deployed in geospatial question answering and visual scene understanding, improving their spatial cogn

tutorialsarxiv-cs-cv

15 Jul 2026

Falsifying Causal Graphs With Outlier Events

arXiv:2607.12145v1 Announce Type: cross Abstract: True causal relationships are rarely known, and inferring causal graphs from data is hard. A fundamental challenge is how to assess whether a given ca

Fisher Rank Inflation: A Spectral Signature of Memorization under Label Noise

arXiv:2607.12438v1 Announce Type: new Abstract: Deep networks trained with label noise often learn clean structure before memorizing corrupted labels. We show that this transition leaves a spectral si

GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization

arXiv:2607.11941v1 Announce Type: new Abstract: Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and struc

Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks

arXiv:2607.11875v2 Announce Type: replace-cross Abstract: We present a theoretical framework to explain the emergence of inductive reasoning abilities in Transformer language models. While previous wo

Language Identification with Succinct Machine-Independent Traces

arXiv:2607.12443v1 Announce Type: new Abstract: Motivated by the power of large language models, there has been renewed interest in the Gold-Angluin model of language identification in the limit, with

tutorialsarxiv-cs-cl

Learning to Discretize: Diffusion-Based Adaptive Mesh with Spectral Guidance

arXiv:2607.11974v1 Announce Type: cross Abstract: Most neural partial differential equation (PDE) surrogates learn how fields evolve after a grid has already been chosen. However, before any operator

MaxSAT-Based Feedback for Guiding Vision-Language Models in Sudoku

arXiv:2607.12711v1 Announce Type: new Abstract: Vision--Language Models (VLMs) have recently demonstrated promising performance on structured visual reasoning tasks, including grid-based puzzles. Howe

MBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Models

arXiv:2607.12782v1 Announce Type: new Abstract: Hyperspectral foundation models learn transferable spectral-spatial representations from large-scale unlabeled data. They provide an effective paradigm

Near-Optimal Learning of Gaussian Sobolev Operators

arXiv:2607.11921v1 Announce Type: cross Abstract: A key question in operator learning is how to design surrogate operators with provable approximation guarantees in reasonable computational time. Wher

PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models

arXiv:2602.04029v2 Announce Type: replace-cross Abstract: Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases. However, the divers

QDEvo: A Multi-Objective Quality-Diversity Framework for Automated Heuristic Design

arXiv:2607.11916v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) with evolutionary computation has emerged as a powerful paradigm for automated heuristic design in com

Quantification of Credal Uncertainty: A Distance-Based Approach

arXiv:2603.27270v2 Announce Type: replace Abstract: Credal sets, i.e., closed convex sets of probability measures, provide a natural framework to represent aleatoric and epistemic uncertainty in machi

ReLope: KL-Regularized LoRA Probes for Multimodal LLM Routing

arXiv:2603.24787v2 Announce Type: replace Abstract: Routing has emerged as a promising strategy for balancing performance and cost in large language model (LLM) systems that combine lightweight models

Self-Regulated Reading with AI Support: An Eight-Week Study with Students

arXiv:2602.09907v2 Announce Type: replace-cross Abstract: College students increasingly use AI chatbots to support academic reading, yet we lack granular understanding of how these interactions shape

Signal-Guided Optimization for Machine Unlearning

arXiv:2607.11975v1 Announce Type: cross Abstract: Current machine unlearning methods predominantly rely on global, coarse-grained intervention strategies. They lack precise pilot signals to guide the

Sparse Autoencoders for Interpretable Out-of-Distribution Detection

arXiv:2607.12094v1 Announce Type: cross Abstract: Reliable detection of out-of-distribution (OOD) samples is crucial for the safe deployment of machine learning models. Neural networks often produce o

UMSS: Towards Unsupervised Multi-modal Semantic Segmentation

arXiv:2607.12372v1 Announce Type: new Abstract: Multimodal semantic segmentation (MSS) is essential for robust perception in complex environments, yet its potential remains largely untapped because of

Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks

arXiv:2510.11917v2 Announce Type: replace Abstract: Dementia disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit overlapping electrophysiological signatures in EEG tha

Accurate Portraits of Scientific Resources and Knowledge Service Components

arXiv:2204.04883v2 Announce Type: replace-cross Abstract: With the advent of the cloud computing era, the cost of creating, capturing, and managing information has gradually decreased. The amount of d

Applying JEPA-Style Predictive Learning to JA4-Derived Network Fingerprints

arXiv:2607.08465v1 Announce Type: new Abstract: I-JEPA and V-JEPA learn by matching latent predictions to target encoder outputs rather than regenerating the original input, and this has worked well f

Architecture Generalization with MetaNCA

arXiv:2607.07743v1 Announce Type: cross Abstract: Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information. Biological

Enhancing In-context Panoramic Generation via Geometric-aware Pretraining

arXiv:2607.08765v1 Announce Type: new Abstract: In this work, we present Canvas360, a two-stage framework for in-context panoramic generation that combines geometry-aware pretraining with downstream t

Frequency-Domain Multi-Modality Transportation Modeling

arXiv:2607.08475v1 Announce Type: new Abstract: Multi-modality transportation refers to urban systems composed of multiple transportation modes, such as traffic flow and public transit, whose dynamics

LOGOS: Language-guided Oriented Object Detection in Aerial Scenes

arXiv:2607.08004v1 Announce Type: new Abstract: Object detection in geospatial scenes, such as satellite and aerial imagery, poses significant challenges due to the varying orientations and densities

Neural non-canonical Hamiltonian dynamics for long-time simulations

arXiv:2510.01788v2 Announce Type: replace Abstract: This work focuses on learning non-canonical Hamiltonian dynamics from data, where long-term predictions require the preservation of structure both i

Optimal Learning Rate Scaling Depends on Data in Deep Scalar Linear Networks

arXiv:2607.07884v1 Announce Type: new Abstract: In this short note we consider the gradient descent dynamics of deep scalar linear networks, f(x) = prod_{l=1}^L w_l x, which enjoy exact time-course so

Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions

arXiv:2603.02204v2 Announce Type: replace Abstract: Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with

Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence

arXiv:2607.08641v1 Announce Type: new Abstract: Over the last few years, there has been an increased interest in making machine learning models more interpretable. Although a great deal of effort goes

Switch-Reasoner: Learn When to Think in Multitask Mixtures via Reinforcement Learning

arXiv:2607.08572v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) often follow a fixed Think-then-Answer paradigm, which is inefficient in heterogeneous multitask settings becau

TrackStudio: An Integrated Toolkit for Markerless Tracking

arXiv:2511.07624v3 Announce Type: replace Abstract: Markerless motion tracking has advanced rapidly in the past 10 years and currently offers powerful opportunities for behavioural, clinical, and biom

VectorizationLLM: Smart Vectorization Based AI Assistant

arXiv:2607.07846v1 Announce Type: new Abstract: VectorizationLLM is a specialized Large Language Model based on Google open-weight LLMs. The model is designed to assist students to learn smart vectori

Write-Protected Discrete Bottlenecks for Language-Grounded World Models: A Structural Limitation and Sufficient Fix

arXiv:2607.08312v1 Announce Type: new Abstract: How should language interface with a world model's discrete symbol system? The dominant paradigm -- end-to-end injection of LLM/VLM features into robot

A Continual Learning Framework for Adaptive Control of Modular Soft Robots

arXiv:2607.06740v1 Announce Type: cross Abstract: Soft robots have attracted significant attention in applications such as medical intervention, rehabilitation, and robotic manipulation due to their i

A Theory of Contrastive Learning with Natural Images

arXiv:2607.07470v1 Announce Type: new Abstract: Why does contrastive learning with simple images and augmentations yield useful representations for downstream tasks? We address this question by analyt

Adversarial Rademacher Complexity of Deep Neural Networks

arXiv:2211.14966v2 Announce Type: replace Abstract: Deep neural networks (DNNs) are highly vulnerable to adversarial attacks. Ideally, a robust model should perform well on both perturbed training dat

An Adaptive Differentially Private Federated Learning Framework

arXiv:2602.06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy. However, in practical deployments,

An Edge-aware Prompt-enhanced SAM for Ultrasound Image Segmentation

arXiv:2607.07240v1 Announce Type: new Abstract: Ultrasound image segmentation is essential for delineating anatomical structures and lesions, providing the foundation for accurate diagnosis. While the

Bayesian Optimization of Genetic Algorithm Hyperparameters in a Multi-Fidelity Framework for Efficient Lattice Material Design

arXiv:2607.07289v1 Announce Type: cross Abstract: This study presents a multi-fidelity framework for the systematic optimization of genetic algorithm (GA) hyperparameters. The framework integrates thr

Can We Really Learn One Representation to Optimize All Rewards?

arXiv:2602.11399v2 Announce Type: replace-cross Abstract: As unsupervised pretraining becomes increasingly ubiquitous in reinforcement learning, a more thorough theoretical understanding of these meth

Creating Power Distribution Network Layouts Using Generative Adversarial Networks and Image-Based Representations

arXiv:2607.06622v1 Announce Type: cross Abstract: Utilities increasingly rely on planning and operational tools to cope with the increased penetrations of distributed energy resources, yet the lack of

Ensemble Deep Learning Approaches for AI-Altered Video Detection

arXiv:2607.06872v1 Announce Type: new Abstract: The increasing accessibility of artificial intelligence has led to a rapid rise in AI-generated videos, making it more difficult to distinguish between

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review

arXiv:2508.12265v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable progress in reasoning across diverse domains. However, effective reasoning in real-world t

tutorialsarxiv-cs-cl

From system models to class models: An in-context learning paradigm

arXiv:2308.13380v3 Announce Type: replace-cross Abstract: Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavio

Generative Diffusion Models of Stochastic Graph Signals

arXiv:2607.06833v1 Announce Type: new Abstract: Sampling stochastic signals supported on a graph underlies many graph machine learning tasks, including recommender systems, forecasting in financial ma

HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies

arXiv:2512.05693v2 Announce Type: replace-cross Abstract: Generalist vision--language--action (VLA) policies are typically trained on heterogeneous mixtures of robot demonstrations spanning diverse em

MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning

arXiv:2607.06974v1 Announce Type: new Abstract: Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in

tutorialsarxiv-cs-cl