Search: “tutorials”

Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases

arXiv:2607.01362v1 Announce Type: cross Abstract: Quantum mechanical (QM) cluster models provide an effective framework for mechanistic studies of enzymatic reactions but remain computationally demand

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Tutorials

Evolutionary Wave Function Collapse

DGX agent

arXiv:2607.02082v1 Announce Type: cross Abstract: Wave Function Collapse (WFC) is a widely used procedural content generation method that learns local adjacency constraints from example inputs to gene

tutorialsarxiv-cs-ai

3 Jul 2026

G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models

arXiv:2607.02491v1 Announce Type: new Abstract: In this work, we focus on SE-RRMs, a symbol-equivariant instantiation of RRMs that exhibits improved extrapolation to larger problem sizes. We propose a

How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size

arXiv:2607.01487v1 Announce Type: new Abstract: We propose a scaling law that takes into account model size and training data while explicitly splitting the latter into training steps and batch size (

Imagining the Sense of Touch: Touch-Informed Manipulation via Imagined Tactile Representations

arXiv:2607.01684v1 Announce Type: new Abstract: Tactile sensing can substantially improve contact-rich robotic manipulation, yet its practical deployment remains limited by the fragility, calibration

tutorialsarxiv-cs-ro

Learning 3D-Gaussian Simulators from RGB Videos

arXiv:2503.24009v3 Announce Type: replace-cross Abstract: Realistic simulation is critical for applications ranging from robotics to animation. Learned simulators have emerged as a possibility to capt

Learning Consumer Preferences from Bundle Sales Data

arXiv:2209.04942v2 Announce Type: replace-cross Abstract: Problem definition: This paper studies the problem of estimating consumer preferences from bundle sales data. Product bundling is a widely use

LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection

arXiv:2602.08638v2 Announce Type: replace-cross Abstract: As a fundamental data mining task, unsupervised time series anomaly detection (TSAD) aims to build a model for identifying abnormal timestamps

Non-synchronism in Global Usage of Research Methods in Library and Information Science from 1990 to 2019

arXiv:2607.01833v1 Announce Type: cross Abstract: The global development of Library and Information Science (LIS) is influenced by various factors such as the economy, society, culture, discipline, tr

On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry

arXiv:2603.27631v2 Announce Type: replace Abstract: Self-supervised pre-training, where large corpora of unlabeled data are used to learn representations for downstream fine-tuning, has become a corne

Optimal Stabilizer Testing and Learning with Limited Quantum Memory

arXiv:2607.02444v1 Announce Type: cross Abstract: We study stabilizer state testing and learning with limited coherent quantum memory. Here an algorithm sequentially receives copies of an unknown n-qu

Scaling Laws for Grid-Based Approximate Nearest Neighbor Search in High Dimensions

arXiv:2607.01283v1 Announce Type: cross Abstract: Grid-based approaches to approximate nearest neighbor (ANN) search have been absent from modern scaling analyses. We present a systematic characteriza

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

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

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

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

tutorialsarxiv-cs-ro

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

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

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

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

tutorialsarxiv-cs-ro

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