Search: “research”

CaSPECT: Discovering Causally Homogeneous Subgroups via Directed Spectral Clustering

arXiv:2607.03364v1 Announce Type: cross Abstract: We propose extbf{CaSPECT}, a causal spectral clustering framework for discovering causally homogeneous subgroups from observational data. Rather than

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Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data

DGX agent

arXiv:2607.04527v1 Announce Type: cross Abstract: Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects. Although this orde

researcharxiv-cs-lg

7 Jul 2026

CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes

arXiv:2404.01299v3 Announce Type: replace-cross Abstract: Causal video question answering (QA) has garnered increasing interest, yet existing datasets often lack depth in causal reasoning. To address

CDST: Color Disentangled Style Transfer for Universal Style Reference Customization

arXiv:2506.13770v2 Announce Type: replace Abstract: We introduce Color Disentangled Style Transfer (CDST), a novel and efficient two-stream style transfer training paradigm which completely isolates c

Cell as Point: One-Stage Framework for Efficient Cell Tracking

arXiv:2411.14833v4 Announce Type: replace-cross Abstract: Conventional multi-stage cell tracking approaches rely heavily on detection or segmentation in each frame as a prerequisite, requiring substan

CenSynCMB: Centre Maps and Physics-Guided Synthesis for Microbleed Detection

arXiv:2607.05325v1 Announce Type: new Abstract: Cerebral microbleeds (CMBs) are MRI markers of small vessel disease and the microbleed component of amyloid related imaging abnormalities (ARIA-H), but

CertMix: Certified, Data-Efficient Metamaterial Design by Affine Mixing of Aligned Neural-Implicit Weight Spaces

arXiv:2607.04123v1 Announce Type: new Abstract: Inverse design of mechanical metamaterials seeks a periodic unit cell whose homogenized elastic properties meet a prescribed target, but current learnin

CGGS: Consistency-Augmented Geometric Gaussian Splatting for Ego-centric 3D Scene Generation

arXiv:2607.03819v1 Announce Type: cross Abstract: Challenges remain in ego-centric 3D scene generation due to limited view overlap and the dominant influence of individual perspectives on scene interp

ChainReaction: Causal Chain-Guided Reasoning for Modular and Explainable Causal-Why Video Question Answering

arXiv:2508.21010v3 Announce Type: replace-cross Abstract: Existing Causal-Why Video Question Answering (VideoQA) models often struggle with higher-order reasoning, relying on opaque, monolithic pipeli

Choosing a parallel heterogeneous ensemble method for tabular classification

arXiv:2607.05103v1 Announce Type: new Abstract: Parallel ensemble methods were compared on 56 small-to-medium tabular classification tasks drawn from OpenML CC18. A set of ``best practice'' recommenda

City landscape in sight: A crowdsourced framework for unlocking urban-scale window view perceptions from real estate imagery

arXiv:2606.15198v2 Announce Type: replace Abstract: City landscapes viewed through home windows influence quality of life, yet perceptions of actual window views at the urban scale remain understudied

City-Level 3D Surface Reconstruction with Viewpoint Orientation Partitioning and Scene Completion

arXiv:2607.03771v1 Announce Type: new Abstract: Multi-view 3D surface reconstruction is a longstanding challenge in computer vision. Although recent large-scale reconstruction methods based on 3D Gaus

Model Releases

CLABTOOLKIT: An Open-Source Toolkit for Routine Processing, Manipulation, and Visualization of Neuroimaging Data

model-releasesarxiv-cs-cv

arXiv:2607.02638v1 Announce Type: new Abstract: Neuroimaging research requires manipulating heterogeneous data structures, including raw MRI volumes, volumetric parcellations, cortical surface meshes,

CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space

arXiv:2512.08029v3 Announce Type: replace-cross Abstract: Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform. While

Closing Gaps in Emissions Monitoring with Climate TRACE

arXiv:2511.19277v2 Announce Type: replace Abstract: Global greenhouse gas emissions estimates are essential for monitoring and mitigation planning. Existing emissions datasets provide critical foundat

Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation

arXiv:2607.03522v1 Announce Type: new Abstract: Fine-tuning a single low-rank adapter on many domains at once is multi-task learning: the domains must be co-learned, and how they share the adapter dec

CodeJeNN: A simple C++ neural network generator for physics applications

arXiv:2607.02746v1 Announce Type: cross Abstract: Machine learning has shown speedups for numerical methods in physics applications, but integrating Python-based libraries into high-performance C++ so

CoFEND: A Cross-Modal Fusion End-to-End Network for Cold-Start Drug-Drug Interaction Prediction

arXiv:2607.02928v1 Announce Type: new Abstract: Cold-start drug-drug interaction (DDI) prediction for new drugs is critical for minimizing unexpected adverse drug reactions. The key challenge is to ca

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification

arXiv:2509.24181v2 Announce Type: replace Abstract: Active learning (AL) aims to build high-quality labeled datasets by iteratively selecting the most informative samples from an unlabeled pool under

Comparison of Loss Functions for Robust Deep Learning-based Echocardiography Segmentation when Learning with Partially Labelled Data from Multiple Domains

arXiv:2607.05008v1 Announce Type: cross Abstract: Echocardiography is the first imaging modality used for assessing cardiac function, and accurate segmentation of cardiac structures is essential for d

Complexity of Normalized Persistence Problems for Topological Data Analysis and Local Hamiltonians

arXiv:2607.03278v1 Announce Type: cross Abstract: Topological data analysis (TDA) is a machine learning technique that uses topology to extract patterns from data and has shown the potential to exhibi

Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models

arXiv:2602.24264v2 Announce Type: replace Abstract: Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems. Although mod

Compressed Computation under L^4 Loss is likely Computation in Superposition

arXiv:2607.04800v1 Announce Type: new Abstract: Neural networks are thought to represent concepts as directions in their activation space, and superposition lets them encode more concepts than they ha

CompressedVQA-AEV: Full-Reference and No-Reference Quality Assessment Models for Asymmetric Encoded Videos

arXiv:2607.04606v1 Announce Type: cross Abstract: This report presents our solutions to the QoMEX 2026 Grand Challenge on Video Quality Assessment for Asymmetric Encoded Videos, comprising a full-refe

Computing Monetary Risk Measures in Linear Time

arXiv:2607.05078v1 Announce Type: new Abstract: Monetary risk measures have gained popularity for expressing decision-makers' risk aversion. Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR), i

Conditional Clifford-Steerable CNNs for PDE Modeling

arXiv:2510.14007v2 Announce Type: replace-cross Abstract: We introduce Conditional Clifford-Steerable CNNs (C-CSCNNs), a unified framework that incorporates equivariance to arbitrary pseudo-Euclidean

Conditional Diffusion Guided Knowledge Transfer for Multi-Domain Knowledge Graph Completion

arXiv:2607.03154v1 Announce Type: cross Abstract: Multi-domain knowledge graph completion (MKGC) aims to improve missing triple prediction in a target KG by transferring knowledge from other support K

Consistent and Editable: A Balanced Framework for Text-Guided Video Editing

arXiv:2607.05056v1 Announce Type: new Abstract: Recently, diffusion models have achieved considerable success in the text-guided video editing domain. However, existing works often struggle to balance

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language

arXiv:2607.03882v1 Announce Type: cross Abstract: LLMs are increasingly deployed as post-hoc explainers of AI-generated outputs, yet it remains unclear whether they can reliably communicate probabilis

Constrained Flow Matching via Lagrangian Dual Flows

arXiv:2607.04513v1 Announce Type: cross Abstract: Flow matching is a powerful tool for generative modeling, but emerging applications in robotics, planning, and physics require inference-time constrai

Context-Constrained Transfer Learning for Tabular Foundation Models via Data Distillation

arXiv:2607.04809v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have demonstrated strong empirical performance as black-box inference engines through in-context learning. However, t

Context Tuning for In-Context Optimization

arXiv:2507.04221v3 Announce Type: replace-cross Abstract: We introduce Context Tuning, a simple and effective method to significantly enhance few-shot adaptation of large language models (LLMs) withou

ControlHair: Synergizing Physics Simulator and Video Diffusion for Controllable Dynamic Hair Rendering

arXiv:2509.21541v3 Announce Type: replace-cross Abstract: Hair simulation and rendering are challenging due to complex strand dynamics, diverse material properties, and intricate light-hair interactio

Convergence Analysis of the ProbAbilistic Gradient Estimator Algorithm for Weakly Convex Finite-Sum Optimization

arXiv:2509.00737v3 Announce Type: replace-cross Abstract: The ProbAbilistic Gradient Estimator algorithm (PAGE), a stochastic algorithm introduced by Li et al. in 2021, was designed to find stationary

Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks for the Poisson Equation

arXiv:2508.21571v2 Announce Type: replace Abstract: Physics informed neural networks (PINNs) represent a very popular class of neural solvers for partial differential equations. In practice, one often

Convergence Rate of a Functional Learning Method for Contextual Stochastic Optimization

arXiv:2603.13048v2 Announce Type: replace-cross Abstract: We consider a stochastic optimization problem involving two random variables: a context variable X and a dependent variable Y. The objective i

Conversational Human Audio-visual Talking Dialogue Generation

arXiv:2607.02799v1 Announce Type: new Abstract: Large-scale dyadic interactive audio-visual dialogue (DIAD) datasets provide fundamental data resources for developing humanoid interactive virtual agen

Coordinate Singularities Break Conformal Coverage for Gaze and Head Pose

arXiv:2607.02565v1 Announce Type: new Abstract: Conformal prediction provides distribution-free reliability guarantees for vision systems, but these guarantees depend on how prediction errors are meas

Correlation-Weighted Multi-Reward Optimization for Compositional Generation

arXiv:2603.18528v2 Announce Type: replace Abstract: Text-to-image models produce images that align well with natural language prompts, but compositional generation has long been a central challenge. M

Counterfactual Operator Relevance for PDE Discovery: Screening, Pruning, and Identifiability

arXiv:2506.20181v2 Announce Type: replace Abstract: We study operator relevance in data-driven partial differential equation (PDE) discovery. Sparse residual methods can select terms that improve resi

Cross-Modal Fusion of OCT and OCT angiography enface for Improved Diagnostics of Diabetic Retinopathy

arXiv:2607.03959v1 Announce Type: cross Abstract: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, highlighting the need for accurate and accessible screening tools. Optica

CSB: A Counting and Sampling tool for Bit-vectors

arXiv:2607.04142v1 Announce Type: cross Abstract: Satisfiability modulo theory (SMT) solvers have significantly advanced automated reasoning due to their effectiveness in solving problems across vario

CURE: Controllable Unified Image Restoration for Complex Degradations

arXiv:2607.03044v1 Announce Type: new Abstract: The presence of composite degradations poses a significant challenge, since the underlying corruption factors exhibit complex and interdependent interac

Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks

arXiv:2603.14830v3 Announce Type: replace Abstract: Dataset distillation, a training-aware data compression technique, has recently attracted increasing attention as an effective tool for mitigating c

DC-Motion: Decoupling Structure and Details via Discrete-Continuous Tokens for Human Motion Generation

arXiv:2606.14721v2 Announce Type: replace-cross Abstract: Text-to-motion generation requires modeling both global action structure and fine-grained motion dynamics from natural language. Existing appr

Decentralized Federated Learning by Partial Message Exchange

arXiv:2603.01730v2 Announce Type: replace Abstract: Decentralized federated learning (DFL) has emerged as a transformative server-free paradigm that enables collaborative learning over large-scale het

Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics

arXiv:2603.07108v2 Announce Type: replace-cross Abstract: Accurate and reliable forecasting of epidemic incidences is critical for public health preparedness, yet it remains a challenging task due to

Deep Learning Network-Temporal Models For Traffic Prediction

arXiv:2603.11475v2 Announce Type: replace Abstract: Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions. Exis