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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

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

DGX agent

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

researcharxiv-cs-ai

7 Jul 2026

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

Derivations of Error-State Kalman Filter Kinematics for Globally Applicable Aided Inertial Navigation Systems

arXiv:2607.03211v1 Announce Type: new Abstract: Global navigation systems require state estimation algorithms that handle Earth's curvature, Earth's rotation, and gravitational variations. These facto

researcharxiv-cs-ro

DETECT-3B-Omni is Agnostic of Content and Demographics

arXiv:2607.03418v1 Announce Type: cross Abstract: A trustworthy and GDPR-compliant deepfake audio detector must base its decisions on acoustic artifacts, not on what is being said or who is speaking.

Detecting Hallucinations in Retrieval-Augmented Generation through Grounding-Aware Sensitivity by Perturbation (GASP)

arXiv:2607.04223v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does

Developing an LLM-Based Feedback System Grounded in Evidence-Centered Design to Support Physics Problem Solving

arXiv:2512.10785v3 Announce Type: replace-cross Abstract: Generative AI offers new opportunities for individualized and adaptive learning, e.g., through large language model (LLM)-based feedback syste

Dimension Reduction for Curves: Simplified and Generalized

arXiv:2607.03112v1 Announce Type: cross Abstract: We revisit random projections for reducing the dimension of high-dimensional polygonal curves. Drawing from the toolbox of randomized linear algebra,

Direct Time-of-Flight Measurement Accuracy Improvement With Perimeter-Gated SPADs

arXiv:2607.02546v1 Announce Type: cross Abstract: Direct time of flight (dToF) measurements are susceptible to errors because of system-level and circuit-level timing jitters. In addition, device-leve

DistillH-Mamba: A Hypergraph-Mamba-Based Knowledge Distillation Model for Efficient Impact Fall Detection

arXiv:2607.03156v1 Announce Type: new Abstract: Falls among the elderly represent a significant public health concern due to their prevalence, consequences, and societal burden. While deep learning ha

Distribution Matching Distillation Meets Reinforcement Learning

arXiv:2511.13649v5 Announce Type: replace Abstract: Distribution Matching Distillation (DMD) facilitates efficient inference by distilling multi-step diffusion models into few-step variants. Concurren

Diversity-aware View Partitioning for Scalable VGGT

arXiv:2607.01885v2 Announce Type: replace Abstract: Geometry transformers such as VGGT achieve strong performance by jointly reasoning over multiple views with global attention. However, scaling them

Do All Visual Tokens Matter Equally? Object-Evidence Preserving Token Merging for Vision-Language Retrieval

arXiv:2607.04605v1 Announce Type: cross Abstract: Multi-vector vision-language retrieval preserves fine-grained visual evidence through maximum-similarity late interaction, but dense image-side tokens

Do Flat Minima Improve Sparse Novel View Synthesis?

arXiv:2511.17918v2 Announce Type: replace Abstract: Despite the success of recent novel view synthesis methods, they tend to struggle in sparse-view settings. This poor generalization to unseen viewpo

Does It Fail to See or Fail to Know? Attributing Errors in Vision-Language Models

arXiv:2607.04683v1 Announce Type: cross Abstract: Vision-language models (VLMs) perform well on visual question answering with high-quality images but struggle when questions require knowledge beyond

researcharxiv-cs-cl

DOGE shut down USAID, established by JFK in 1961. Before being shut down, the US Agency for International Development (USAID) was the larges…

DOGE shut down USAID, established by JFK in 1961. Before being shut down, the US Agency for International Development (USAID) was the largest funding agency for humanitarian and development aid worldw

researchyann-lecun--x

Don't Commit Alone: Joint Token Commitment in Diffusion Large Language Models

arXiv:2607.04469v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) commit multiple tokens per denoising step by decoding each selected position independently from the shared conte

researcharxiv-cs-cl

Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking

arXiv:2607.03093v1 Announce Type: cross Abstract: Thinking has emerged as a critical capability for Large Language Models (LLMs) tackling complex tasks. However, its reactive nature, where reasoning i

DOSE-I: A Multimodal Biosignal Dataset of Procedural Sedation for Endoscopy -- Technical Report

arXiv:2607.02570v1 Announce Type: cross Abstract: In this document, we describe characteristics and technical details of the multimodal biosignal dataset DOSE-I of procedural sedation for endoscopy pu

Double Fuzzy Probabilistic Interval Linguistic Term Set and a Dynamic Fuzzy Decision Making Model based on Markov Process with tts Application in Multiple Criteria Group Decision Making

arXiv:2111.15255v2 Announce Type: replace-cross Abstract: The probabilistic linguistic term has been proposed to deal with probability distributions in provided linguistic evaluations. However, becaus

DRBA: Dynamic Robotic Balance Assistant -- An assist-as-needed gait and balance rehabilitation robot for versatile training

arXiv:2607.03027v1 Announce Type: new Abstract: The decline of human balance control due to aging and pathological conditions increases fall risk, a major concern in geriatric care and rehabilitation.

researcharxiv-cs-ro