Search: “tutorials”

SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios

arXiv:2607.02343v1 Announce Type: cross Abstract: Humans can selectively attend to a target sound and estimate its direction in complex scenarios, whereas such selective localization remains challengi

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Tutorials

Visually Grounded Self-Reflection for Vision-Language Models via Reinforcement Learning

DGX agent

arXiv:2607.02490v1 Announce Type: new Abstract: Large vision-language models can reason over multimodal inputs by generating textual chains of thought (CoT). A key capability exhibited in CoT reasonin

tutorialsarxiv-cs-cl

3 Jul 2026

What Types of Human-AI Teams Exist?

arXiv:2607.02198v1 Announce Type: cross Abstract: Human-AI teaming has received increasing attention in the literature. However, the range of studies conducted in multiple domains make it difficult to

Why Can't I Open My Drawer? Mitigating Object-Driven Shortcuts in Zero-Shot Compositional Action Recognition

arXiv:2601.16211v3 Announce Type: replace-cross Abstract: Zero-Shot Compositional Action Recognition (ZS-CAR) requires recognizing novel verb-object combinations composed of previously observed primit

World Feedback for Clinical Agents: Diagnosing RL in FHIR Environments

arXiv:2607.01470v1 Announce Type: new Abstract: Clinical protocol-execution tasks -- checking a lab value, applying a threshold, placing a correctly structured FHIR order -- are natural candidates for

A General Approach to Visualizing Uncertainty in Statistical Graphics

arXiv:2508.00937v4 Announce Type: replace-cross Abstract: We present a general approach to visualizing uncertainty in static 2-D statistical graphics. If we treat a visualization as a function of its

AMBUSH: Collaborative Capture in Complex Environments with Neural Acceleration

arXiv:2607.01029v1 Announce Type: new Abstract: Collaborative capture of dynamic targets is common in nature as an essential strategy for weaker species against the strong. Similar concepts have shown

CogTax: A Four-Level Cognitive Taxonomy for Command-Line Computing Education

arXiv:2607.00140v1 Announce Type: cross Abstract: As computing education expands beyond traditional programming into operational domains such as systems administration and command-line environments, e

tutorialsarxiv-cs-cl

CORGI: Consistency-Aware 3D Dog Reconstruction from a Single Image in the Wild

arXiv:2607.00321v1 Announce Type: new Abstract: Reconstructing high-fidelity 3D models of highly articulated animals, such as dogs, from a single in-the-wild image remains a formidable challenge. In t

Decision-focused Sparse Tangent Portfolio Optimization

arXiv:2607.00581v1 Announce Type: new Abstract: Sparse tangent portfolio optimization aims to learn an interpretable, low-cardinality portfolio in the tangency direction of the mean-variance frontier.

Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning

arXiv:2607.00720v1 Announce Type: cross Abstract: Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data

Diffeomorphic Optimization

arXiv:2607.00947v1 Announce Type: new Abstract: Generative models learn data distributions that reside on a low-dimensional manifold within a higher-dimensional ambient space. Optimizing differentiabl

DriftScope: Measuring The Hidden Effects of Diffusion Model Adaptation

arXiv:2607.00183v1 Announce Type: new Abstract: Adapting pre-trained text-to-image diffusion models, whether to learn new visual concepts or erase unwanted ones, is routinely evaluated on its intended

From Spectral Methods to Sample Complexity Bounds for Fourier Neural Operators

arXiv:2607.00320v1 Announce Type: cross Abstract: We establish approximation and learning guarantees for Fourier neural operators (FNOs) applied to time-T solution operators of dissipative evolution e

GaussianGPT: Towards Autoregressive 3D Gaussian Scene Generation

arXiv:2603.26661v2 Announce Type: replace Abstract: Most recent advances in 3D generative modeling rely on diffusion or flow-matching formulations. We instead explore a fully autoregressive alternativ

Generative Model Proposal based Particle Filtering for Data Assimilation

arXiv:2607.01012v1 Announce Type: new Abstract: Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications. In the filtering setting,

Generative Modeling of Quantum Distribution with Functional Flow Matching

arXiv:2607.00301v1 Announce Type: new Abstract: The emergence of powerful deep generative models based on diffusion and flow matching has enabled the learning and modeling of complex distributions. Le

GenSP: Consistent Spherical Parameterization via Learning Shape Generative Models

arXiv:2607.00492v1 Announce Type: new Abstract: We introduce GenSP, a data-driven framework that learns consistent spherical parameterizations across a collection of genus-0 shapes. Instead of optimiz

Geometry-Preserving Neural Architectures on Manifolds with Boundary

arXiv:2602.03082v2 Announce Type: replace Abstract: A growing number of neural architectures have been proposed to enforce geometric constraints, including projection-based networks, exponential-map u

Harnessing the Latent Space: From Steering Vectors to Model Calibrators for Control and Trust

arXiv:2607.00083v1 Announce Type: cross Abstract: Language models have changed from unreliable text generators to highly-capable large models with trillions of parameters. Capability increases come ha

Learn Once, Edit Anywhere: Visual Direction Transfer for Diffusion Models

arXiv:2403.19645v2 Announce Type: replace Abstract: The rapid advancement of diffusion models has enabled the generation of high-fidelity images from textual prompts, yet achieving precise, disentangl

Learning Cardiac Motion Priors for Implicit Neural Representations

arXiv:2607.00955v1 Announce Type: cross Abstract: Implicit neural representations (INRs) are well suited to cardiac motion estimation, providing continuous, compact representations of motion fields. H

MetaOthello: A Controlled Study of Multiple World Models in Transformers

arXiv:2602.23164v2 Announce Type: replace Abstract: Foundation models must handle multiple generative processes, yet mechanistic interpretability largely studies capabilities in isolation; it remains

Muon as a Residual Connection

arXiv:2607.01124v1 Announce Type: cross Abstract: Muon has recently emerged as one of the most effective optimizers for training large neural networks, yet its empirical success has been explained fro

Optimal scaling of MCMC algorithms: exploiting the symmetry of the Metropolis-Hastings formula

arXiv:2607.00586v1 Announce Type: cross Abstract: We present a simple, yet general approach to study the scaling properties as the dimensionality of Metropolised MCMC sampling algorithms increases. Th

Restore3D: Breathing Life into Broken Objects with Shape and Texture Restoration

arXiv:2607.00522v1 Announce Type: new Abstract: Restoring incomplete or damaged 3D objects is crucial for cultural heritage preservation, occluded object reconstruction, and artistic design. Existing

Self-conditioned Flow Map Language Models via Fixed-point Flows

arXiv:2607.00714v1 Announce Type: cross Abstract: Self-conditioning is a core technique that enhances continuous flow-based language models, where the model learns to denoise generated text by conditi

Self-Improving Neural Pruning: A Graph Neural Network Framework for Scalable Mixed Bundle Pricing

arXiv:2509.22557v3 Announce Type: replace Abstract: Mixed bundle pricing is a classic revenue management problem arising in industries such as e-commerce, tourism, and video games. It refers to design

SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles

arXiv:2607.01105v1 Announce Type: new Abstract: We present SynLaD, a latent diffusion framework for small-molecule generation that unifies ligand-based drug design objectives (what to make) with synth

The Course of News Events: A Comparison of Bottom-Up and Top-Down Approaches for Collecting Text-Based Data about Disasters

arXiv:2607.00849v1 Announce Type: new Abstract: News articles are an important source of information on disaster impacts and adaptation. A key methodological challenge in socio-environmental studies i

tutorialsarxiv-cs-cl

Building a Multimodal Dataset of Academic Paper for Keyword Extraction

arXiv:2606.31069v1 Announce Type: new Abstract: Up to this point, keyword extraction task typically relies solely on textual data. Neglecting visual details and audio features from image and audio mod

tutorialsarxiv-cs-cl

CLIMB: Centroid-Based Hierarchical Memory for Online Continual Self-Supervised Learning

arXiv:2606.31275v1 Announce Type: cross Abstract: Online Continual Self-Supervised Learning (OCSSL) aims to learn representations from a continuous stream of unlabeled data, without knowledge of task

CoDex: Learning Compositional Dexterous Functional Manipulation without Demonstrations

arXiv:2606.31909v1 Announce Type: new Abstract: In this work, we study Compositional Dexterous Functional Object Manipulation (CD-FOM): tasks such as aiming and actuating a spray bottle on a plant or

CoMNet: A MedNeXt-CorrDiff Framework for Multi-Site Brain Tumor Segmentation

arXiv:2606.15305v2 Announce Type: replace Abstract: Accurate brain tumor segmentation from multiparametric magnetic resonance imaging (MRI) is critical for treatment planning, response assessment, and

Deductive Logic in Language Models: Horizontal vs Vertical Reasoning

arXiv:2510.09340v2 Announce Type: replace Abstract: Recent language models exhibit significant logical reasoning abilities, yet the mechanisms supporting deductive inference remain poorly understood.

FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning

arXiv:2606.32016v1 Announce Type: new Abstract: Multimodal graph foundation models aim to learn reusable knowledge from graphs enriched with text, images, attributes, and relational topology, thereby

FMA-Net++: Motion- and Exposure-Aware Joint Video Super-Resolution and Deblurring

arXiv:2512.04390v2 Announce Type: replace-cross Abstract: Joint video super-resolution and deblurring (VSRDB) requires both efficient long-range temporal modeling and robustness to frame-wise exposure

Human-as-Humanoid: Enabling Zero-Shot Humanoid Learning from Ego-Exo Human Videos with Human-Aligned Embodiments

arXiv:2606.32009v1 Announce Type: new Abstract: Vision-language-action (VLA) models across robot embodiments require high-quality observation--action supervision to learn deployable action distributio

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility

arXiv:2505.18521v2 Announce Type: replace Abstract: The substantial training cost of diffusion models hinders their deployment. Immiscible Diffusion recently showed that reducing diffusion trajectory

InstanceControl: Controllable Complex Image Generation without Instance Labeling

arXiv:2606.31924v1 Announce Type: new Abstract: Controllable image generation methods, such as ControlNet, have demonstrated a remarkable capacity to introduce visual conditions(e.g., depth maps) to g

Intrinsic decomposition and editing of 3D Gaussian splats

arXiv:2606.31637v1 Announce Type: cross Abstract: Intrinsic decomposition which expresses image colors as the product of diffuse albedo and shading, possibly augmented with view-dependent residuals ha

Introspective Coupling: Self-Explanation Training Tracks Behavioral Change Despite Fixed Supervision

arXiv:2606.32038v1 Announce Type: cross Abstract: When does training language models (LMs) to generate explanations of their predictions yield faithful introspection, rather than superficial imitation

Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics

arXiv:2601.22123v4 Announce Type: replace Abstract: Simulating the long-time evolution of Hamiltonian systems is limited by the small timesteps required for stable numerical integration. To overcome t

Low-dimensional topology of deep neural networks

arXiv:2606.31856v1 Announce Type: new Abstract: We study layered models, including feedforward networks, ResNets, and transformers, by limiting each layer to a width of d = 3, i.e., R^3 as representat

Multi-Robot Coordination for Planning under Context Uncertainty

arXiv:2603.13748v3 Announce Type: replace Abstract: Real-world robots often operate in settings where objective priorities depend on the underlying context of operation. When the underlying context is

MVP-Nav: Multi-layer Value Map Planner Navigator

arXiv:2606.31919v1 Announce Type: cross Abstract: Zero-shot Object Goal Navigation (ZSON) with RGB-only perception poses a fundamental challenge for embodied agents, as the absence of explicit depth i

Phantom: A Unified Face-Swap Deepfake Protection Framework with Latent and Spatial Constraints

arXiv:2606.31703v1 Announce Type: new Abstract: Face-swapping deepfakes pose an escalating threat to personal privacy by enabling unauthorized identity manipulation. While adversarial approaches have

PointSplat: Compact Gaussian Splatting via Human-Centric Prediction

arXiv:2606.32036v1 Announce Type: new Abstract: Producing 3D human representations from input views on the fly is essential for immersive live streaming systems, where representation compactness is as