Search: “research”

Cascaded Diffusion Framework for Probabilistic Coarse-to-Fine Hand Pose Estimation

arXiv:2510.00527v2 Announce Type: replace Abstract: Deterministic models for 3D hand pose reconstruction, whether single-staged or cascaded, struggle with pose ambiguities caused by self-occlusions an

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Causal DAG Identification for Count Data via Poisson Thinning Structural Equation Models

DGX agent

arXiv:2609.06098v1 Announce Type: cross Abstract: Count-valued variables arise in many scientific and applied settings, yet explicit structural models that allow full identification of causal DAGs fro

researcharxiv-cs-lg

9 Sept 2026

CausalChapter: Improving Long-Video Chaptering with Interventional Dependency Modeling

arXiv:2609.08686v1 Announce Type: new Abstract: Long-form instructional videos require automatic chaptering to support browsing, navigation, and knowledge access. Recent long-context language models c

Certain companies train their AIs to plagiarise work by unnamed scholars, extending an AI history permeated by plagiarism and misattribution…

Certain companies train their AIs to plagiarise work by unnamed scholars, extending an AI history permeated by plagiarism and misattributions. It started a long time ago. In the 1900s, the old method

researchdavid-ha--x

Certified Topological Interaction in Neural Representations: Class Disentanglement Is Mostly Pairwise

arXiv:2609.08561v1 Announce Type: new Abstract: Class disentanglement (the separation of a representation's class-conditional point clouds along depth and over training) is usually read off descriptiv

Clean Accuracy Does Not Guarantee Provenance Robustness: A Prospective Codec-Stress Evaluation of Audio Attribution

arXiv:2609.07981v1 Announce Type: cross Abstract: Audio provenance attribution - which system produced a synthetic utterance - is reported at near-ceiling accuracy on clean benchmarks, yet audio reach

Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers

arXiv:2504.15928v3 Announce Type: replace Abstract: Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied acro

Closed-Form of the Local Galactic Potential and Stellar Distribution Function from Gaia DR3

arXiv:2609.09011v1 Announce Type: cross Abstract: The local dark matter density determines the strength of the signal expected in direct-detection experiments, yet published estimates from stellar mot

CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

arXiv:2609.07779v1 Announce Type: cross Abstract: As AI-code assistant tools become widespread, automatic assessment of the correctness of generated code becomes a significant challenge. Code LLMs are

Comparison of D-Wave Quantum Annealing and Gibbs Monte Carlo for Sampling from a Probability Distribution of a Restricted Boltzmann Machine

arXiv:2508.10228v3 Announce Type: replace Abstract: A local-valley (LV) centered approach to assessing the quality of sampling from Restricted Boltzmann Machines (RBMs) was applied to the latest gener

Compensating for Scarce Historical Images in Cross-Domain Cultural Heritage Retrieval Using Synthetic Aging

arXiv:2609.08766v1 Announce Type: new Abstract: Cultural heritage collections often contain contemporary and historical visual records of the same physical object. Linking these records is difficult b

Concentrate After Imagination: Text-Conditioned Evidence Grounding for Partially Relevant Video Retrieval

arXiv:2609.08999v1 Announce Type: new Abstract: Partially Relevant Video Retrieval (PRVR) retrieves untrimmed videos when queries describe only short moments. Although recent methods improve local rep

Conditional Timed Partial Orders: An Expressive and Interpretable Framework for Robot Task Specification and Planning

arXiv:2609.07905v1 Announce Type: new Abstract: Timed Partial Orders (TPOs), originally proposed for workflows, provide an interpretable framework for robot task specification with planning algorithms

Congratulations to Tristan Buckmaster and Levent Alpöge for developing the AI assisted techniques for solving an important math problem. Con…

Congratulations to Tristan Buckmaster and Levent Alpöge for developing the AI assisted techniques for solving an important math problem. Congratulations to OpenAI for not letting any good customer tra

researchyann-lecun--x

Connecting Score Matching, Maximum Likelihood, and Expectation-Maximization in Mixed Linear Regression

arXiv:2609.05688v1 Announce Type: new Abstract: We study variance-preserving diffusion of the response in mixed linear regression (MLR) with unknown mixing weights. Our analysis separates the statisti

Constant-Stepsize Stochastic Approximation: Finite-Time Convergence, Gaussian Approximation, and Tail Bounds

arXiv:2602.13960v2 Announce Type: replace Abstract: Constant-stepsize stochastic approximation (SA) is widely used in learning for computational efficiency, yet the distribution of the iterates is typ

Constitutive State-Space Modeling of Path-Dependent Plasticity: A Resolution-Consistent and Parallelizable Computational Framework

arXiv:2609.07294v1 Announce Type: new Abstract: Data-driven constitutive models for path-dependent plasticity are commonly formulated using nonlinear recurrent neural networks, whose sequential state

Constrained Bayesian Optimization for Hierarchical Federated Learning in IoT Networks for Plant Disease Classification

arXiv:2609.06830v1 Announce Type: new Abstract: The deployment of Hierarchical Federated Learning (HFL) in resource-constrained Internet of Things (IoT) environments requires careful configuration to

Constrained Online Learning with Noisy Constraint Values

arXiv:2609.06921v1 Announce Type: new Abstract: We study constrained online convex optimization with adversarial constraints when constraint values and gradients are observed through unbiased noise. G

ContextFlow: In-Context Flow Matching for Robot Manipulation

arXiv:2609.06852v1 Announce Type: new Abstract: Although highly effective in vision and language domains, applying in-context learning to robotics remains challenging. Existing autoregressive in-conte

Continual Learning Mechanisms Compose for Long-Horizon Memorization

arXiv:2609.06986v1 Announce Type: new Abstract: Language models may need to internalize information that arrives over time and retain it through many subsequent updates. To study this challenge, we in

Continuous Token-Level Spatio-Temporal Context Modeling for Visual Object Tracking

arXiv:2609.07070v1 Announce Type: new Abstract: Spatio-temporal context has become increasingly crucial for visual tracking. However, most existing approaches extract spatio-temporal cues via discrete

Contrastive Knowledge Distillation for Anomaly Detection in Multi-Illumination/Focus Display Images

arXiv:2609.05520v1 Announce Type: new Abstract: In this paper, we tackle automatic anomaly detection in multi-illumination and multi-focus display images. The minute defects on the display surface are

CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements

arXiv:2609.07498v1 Announce Type: cross Abstract: Transferring human hand demonstrations to robotic grippers has recently emerged as a cost-effective solution for robot learning. However, existing met

CoVeR: Coverage-Based Token Pruning for Multi-View 3D Reasoning in VLMs

arXiv:2609.08345v1 Announce Type: new Abstract: Representing a 3D scene as multi-view images allows 2D VLMs to reason in 3D by reusing priors from pre-training, sidestepping the scarcity of annotated

Coverage Path Planning for Redundant Manipulators using Generalized Spanning Trees

arXiv:2609.08409v1 Announce Type: new Abstract: Surface coverage with task-redundant manipulators is challenging because each surface point may admit multiple inverse kinematics (IK) solutions, and co

CS-CLIP: Compositional Scene Graph-guided CLIP for Robust Compositional Reasoning

arXiv:2609.08242v1 Announce Type: new Abstract: Vision-language models (VLMs) demonstrate strong performance across compositional reasoning benchmarks, which require reasoning over semantic perturbati

CUNO: Curriculum and Preference Optimization for Stable Graph Unlearning under Mass Deletion

arXiv:2609.08244v1 Announce Type: new Abstract: Graph unlearning removes the influence of designated training data from a trained graph model without retraining from scratch. However, existing methods

Curriculum Learning as Transport: Understanding Curricula with Wasserstein Geodesics

arXiv:2609.09099v1 Announce Type: new Abstract: Curriculum learning is governed by several coupled design choices---how difficulty is defined, how examples are ordered, how much exposure each level re

CVT-GS: Learning to Simplify 3D Gaussian Splatting with Centroidal Voronoi Tessellation

arXiv:2609.08730v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time novel view synthesis, rendering high-fidelity scenes often rel

Data Efficient Sample Selection for In-Context Learning

arXiv:2609.06670v1 Announce Type: new Abstract: The In-context learning (ICL) paradigm aids large language models (LLMs) to adapt to new tasks without need for fine-tuning. However, selecting an optim

Deception in Reach-Avoid Game with Unknown Heterogeneous Attackers Speed Information

arXiv:2609.06953v1 Announce Type: cross Abstract: This letter investigates a reach-avoid game involving two Attackers and one Defender, where the Attackers aim to maximize the number reaching the targ

Decision-Aware Suffix Prediction and Reasoning of Business Processes

arXiv:2609.06169v1 Announce Type: new Abstract: Suffix prediction forecasts the remaining sequence of events of a running case until completion. Most approaches rely on neural networks trained on even

Decoder-Side Semantic Conditioning for Low-Bitrate Neural Speech Compression

arXiv:2512.21653v2 Announce Type: replace-cross Abstract: Speech codecs are usually optimized for waveform fidelity, allocating bits to acoustic detail that can be inferred from linguistic structure.

Deep Barycentric Regression for Optimal Transport Map Estimation and its Statistical Optimality

arXiv:2609.06598v1 Announce Type: new Abstract: The optimal transport (OT) map provides a geometric transformation for aligning probability distributions and has become a useful tool in machine learni

Deep belief networks are exact

arXiv:2609.05572v1 Announce Type: cross Abstract: We prove that every strictly positive probability distribution on ({-1,1}^n) is represented exactly by a sigmoid belief network with finite parameters

Deep Learning for Biopsy-Free Subtyping of Basal Cell Carcinoma from Dermatoscopic Images

arXiv:2609.07180v1 Announce Type: new Abstract: Basal Cell Carcinoma (BCC) is the most common type of skin cancer, accounting for nearly 80% of skin cancer di- agnoses. Its optimal clinical management

Deep learning from the crowd Fundamentals of morphological galaxy classification

arXiv:2609.06316v1 Announce Type: cross Abstract: Aims. The objective of this work is to adapt a deep neural network model to perform galaxy morphological classification trained from crowd annotations

Denoising Models Develop Human-Like Perceptual Illusion Representations Across Architectures

arXiv:2607.17138v2 Announce Type: replace Abstract: Deep neural networks trained on natural images are shown to produce outputs consistent with human observers for brightness illusions. While this phe

Detecting and explaining clinical-omics inconsistencies to improve patient cohort stratification: an application to Parkinson's disease

arXiv:2507.03656v2 Announce Type: replace Abstract: Discrepancies between clinical diagnoses and omics profiles within a characterized cohort may reflect misdiagnosis, hidden subgroups or prodromal di

Diagnosing and Dynamically Filtering Occupancy World Models for Active Mapping

arXiv:2609.06820v1 Announce Type: cross Abstract: Active mapping requires a robot to select camera viewpoints that efficiently reconstruct an unknown 3D scene. To reason about unobserved regions, rece

Diagonal Attenuation: A Finite-Sample Correction for PCA

arXiv:2609.05796v1 Announce Type: cross Abstract: Principal component analysis (PCA) can rotate away from its population target when a covariance matrix is estimated from limited data. We introduce di

Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform

arXiv:2508.04800v2 Announce Type: replace-cross Abstract: We introduce a novel privatization framework for high-dimensional controlled variable selection. Our framework enables rigorous False Discover

Diffuse2Seg: Diffusion Models Can Segment Anything Without Supervision

arXiv:2609.06491v1 Announce Type: new Abstract: Open-world entity segmentation aims to predict masks for arbitrary objects across domains and at multiple granularities, from parts to whole objects. In

Diffusion Model in Latent Space for Medical Image Segmentation Task

arXiv:2512.01292v4 Announce Type: replace Abstract: Medical image segmentation is crucial for clinical diagnosis and treatment planning. Traditional methods typically produce a single segmentation mas

Diffusion models for eye-gaze trajectory generation using position and velocity representations

arXiv:2609.05522v1 Announce Type: new Abstract: Eye-tracking data are expensive to collect, requiring specialized hardware and controlled laboratory conditions, and difficult to share because of priva

Directed mixed membership stochastic blockmodel

arXiv:2101.02307v4 Announce Type: replace-cross Abstract: Mixed membership modeling for undirected networks has been extensively explored in network science over the past few years. Despite the substa

Discovering Natural Transformation Vulnerabilities in Black-Box Vision Models

arXiv:2609.07110v1 Announce Type: new Abstract: Natural adversarial examples (NAEs) reveal that vision models can fail under realistic semantic changes beyond norm-bounded perturbations. However, gene