Search: “models”

Learning Human-Intention Priors from Large-Scale Human Demonstrations for Robotic Manipulation

arXiv:2604.24681v1 Announce Type: new Abstract: Human videos contain rich manipulation priors, but using them for robot learning remains difficult because raw observations entangle scene understanding

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Research

Leveraging Human Feedback for Semantically-Relevant Skill Discovery

DGX agent

arXiv:2604.24127v1 Announce Type: cross Abstract: Unsupervised skill discovery in reinforcement learning aims to intrinsically motivate agents to discover diverse and useful behaviours. However, uncon

researcharxiv-cs-ai

28 Apr 2026

Magnetic Indoor Localization through CNN Regression and Rotation Invariance

arXiv:2604.22896v1 Announce Type: cross Abstract: Indoor positioning is an essential technology for a wide range of applications in GNSS-denied environments, including indoor navigation and IoT system

safetyarxiv-cs-lg

Maritime object classification with SAR imagery using quantum kernel methods

arXiv:2512.11367v2 Announce Type: replace-cross Abstract: Illegal, unreported, and unregulated (IUU) fishing causes global economic losses of 10-25 billion USD annually and undermines marine sustainab

MemeScouts@LT-EDI 2026: Asking the Right Questions -- Prompted Weak Supervision for Meme Hate Speech Detection

arXiv:2604.24179v1 Announce Type: cross Abstract: Detecting hate speech in memes is challenging due to their multimodal nature and subtle, culturally grounded cues such as sarcasm and context. While r

MeshLAM: Feed-Forward One-Shot Animatable Textured Mesh Avatar Reconstruction

arXiv:2604.22865v1 Announce Type: new Abstract: We introduce MeshLAM, a feed-forward framework for one-shot animatable mesh head reconstruction that generates high-fidelity, animatable 3D head avatars

mKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA

arXiv:2508.05318v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for expanding the knowledge capacity of Multimodal Large Language Mo

Applications

ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs

arXiv:2604.23053v1 Announce Type: new Abstract: Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial

applicationsarxiv-cs-lg

Tutorials

Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation

arXiv:2412.07160v3 Announce Type: replace Abstract: To equip artificial intelligence with a comprehensive understanding towards a temporal world, video and 4D panoptic scene graph generation abstracts

tutorialsarxiv-cs-cv

Multi-Plane HyperX: A Low-Latency and Cost-Effective Network for Large-Scale AI and HPC Systems

arXiv:2604.23519v1 Announce Type: cross Abstract: Multi-plane architectures have become increasingly prevalent in the Fat-Tree networks of AI data centers. By leveraging multiple ports on a single net

Multi-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusion

arXiv:2603.26729v2 Announce Type: replace-cross Abstract: The effective utilization of consistency is crucial for multi-view learning. GCNs leverage node connections to propagate information across th

Multispectral airborne laser scanning dataset for tree species classification: MS-ALS-SPECIES

arXiv:2604.24370v1 Announce Type: new Abstract: The shift from stand-level to individual-tree-level forest assessments supports improved biodiversity mapping, particularly in boreal ecosystems where t

MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples

arXiv:2511.10047v2 Announce Type: replace Abstract: Zero-shot anomaly classification (AC) and segmentation (AS) methods aim to identify and outline defects without using any labeled samples. In this p

Hardware

NeuroAPS-Net: Neuro-Anatomically Aware Point Cloud Representation for Efficient Alzheimer's Disease Classification

arXiv:2604.22883v1 Announce Type: cross Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disorder and a major cause of dementia. Structural MRI is widely used to analyze AD-relate

hardwarearxiv-cs-ai

Omni-o3: Deep Nested Omnimodal Deduction for Deliberative Audio-Visual Reasoning

arXiv:2604.24191v1 Announce Type: new Abstract: Omnimodal understanding entails a massive, highly redundant search space of cross-modal interactions, demanding focused and deliberative reasoning. Curr

Agents

OpenVO: Open-World Visual Odometry with Temporal Dynamics Awareness

arXiv:2602.19035v2 Announce Type: replace Abstract: We introduce OpenVO, a novel framework for Open-world Visual Odometry (VO) with temporal awareness under limited input conditions. OpenVO effectivel

agentsarxiv-cs-cv

PeeriScope: A Multi-Faceted Framework for Evaluating Peer Review Quality

arXiv:2604.24071v1 Announce Type: new Abstract: The increasing scale and variability of peer review in scholarly venues has created an urgent need for systematic, interpretable, and extensible tools t

researcharxiv-cs-cl

PhysLayer: Language-Guided Layered Animation with Depth-Aware Physics

arXiv:2604.23574v1 Announce Type: new Abstract: Existing image-to-video generation methods often produce physically implausible motions and lack precise control over object dynamics. While prior appro

Tutorials

Point-MF: One-step Point Cloud Generation from a Single Image via Mean Flows

arXiv:2604.24586v1 Announce Type: new Abstract: Single-image point cloud reconstruction must infer complete 3D geometry, including occluded parts, from a single RGB image. While diffusion-based recons

tutorialsarxiv-cs-cv

Quasi-Quadratic Gradient: A New Direction for Accelerating the BFGS Method in Quasi-Newton Optimization

arXiv:2604.23922v1 Announce Type: cross Abstract: In this paper, we introduce the Quasi-Quadratic Gradient (QQG), a novel search direction designed to accelerate the BFGS method within the quasi-Newto

RecoverFormer: End-to-End Contact-Aware Recovery for Humanoid Robots

arXiv:2604.22911v1 Announce Type: new Abstract: Humanoid robots operating in unstructured environments must recover from unexpected disturbances-a capability that remains challenging for end-to-end co

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering

arXiv:2604.24334v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation (RAG) chunking methods often create excessive redundancy, increasing storage costs and slowing retrieval. This s

researcharxiv-cs-cl

Local Ai

Rethinking Trust Region Bayesian Optimization in High Dimensions

arXiv:2604.22967v1 Announce Type: cross Abstract: Trust Region Bayesian Optimization (TuRBO) is an effective strategy for alleviating the curse of dimensionality in high-dimensional black-box optimiza

local-aiarxiv-cs-lg

Risk-Aware Robust Learning: Reducing Clinical Risk under Label Noise in Medical Image Classification

arXiv:2604.23875v1 Announce Type: cross Abstract: Noisy labels are a pervasive challenge in medical image classification, where annotation errors arise from inter-observer variability and diagnostic a

safetyarxiv-cs-ai

Agents

Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation under Uncertainty

arXiv:2603.04603v2 Announce Type: replace-cross Abstract: We present a risk-aware formalism for evaluating system trajectories in the presence of uncertain interactions between the system and its envi

agentsarxiv-cs-ro

Applications

Robust Deepfake Detection, NTIRE 2026 Challenge: Report

arXiv:2604.24163v1 Announce Type: new Abstract: Robustness is a long-overlooked problem in deepfake detection. However, detection performance is nearly worthless in the real world if it suffers under

applicationsarxiv-cs-cv

Safe Navigation in Unknown and Cluttered Environments via Direction-Aware Convex Free-Region Generation

arXiv:2604.23648v1 Announce Type: new Abstract: Convex free regions provide a structured and optimization-friendly representation of collision-free space for robot navigation in unknown and cluttered

Safety-aware Goal-oriented Semantic Sensing, Communication, and Control for Robotics

arXiv:2603.13502v2 Announce Type: replace Abstract: Wirelessly-connected robotic systems empower robots with real-time intelligence by leveraging remote computing resources for decision-making. Howeve

Local Ai

SATTC: Structure-Aware Label-Free Test-Time Calibration for Cross-Subject EEG-to-Image Retrieval

arXiv:2603.20738v2 Announce Type: replace Abstract: Cross-subject EEG-to-image retrieval for visual decoding is challenged by subject shift and hubness in the embedding space, which distort similarity

local-aiarxiv-cs-cv

Self-Abstraction Learning for Effective and Stable Training of Deep Neural Networks

arXiv:2604.24313v1 Announce Type: cross Abstract: Training large-scale deep neural networks effectively and stably is essential for applying deep learning across various fields. However, conventional

Agents

Self-Organising Memristive Networks as Physical Learning Systems

arXiv:2509.00747v2 Announce Type: replace-cross Abstract: Learning with physical systems is an emerging paradigm that seeks to harness the intrinsic nonlinear dynamics of physical substrates for learn

agentsarxiv-cs-lg

Self-Supervised Representation Learning via Hyperspherical Density Shaping

arXiv:2604.24498v1 Announce Type: new Abstract: Modern self-supervised representation learning methods often relies on empirical heuristics that are not theoretically grounded. In this study we propos

Shape of Memory: a Geometric Analysis of Machine Unlearning in Second-Order Optimizers

arXiv:2604.23046v1 Announce Type: new Abstract: We argue that current definitions of machine unlearning are underspecified for second-order optimizers. We compare first-order and second-order learners

ShowFlow: From Robust Single Concept to Condition-Free Multi-Concept Generation

arXiv:2506.18493v2 Announce Type: replace Abstract: Customizing image generation remains a core challenge in controllable image synthesis. For single-concept generation, maintaining both identity pres

SMP: Reusable Score-Matching Motion Priors for Physics-Based Character Control

arXiv:2512.03028v3 Announce Type: replace-cross Abstract: Data-driven motion priors that can guide agents toward producing naturalistic behaviors play a pivotal role in creating life-like virtual char

safetyarxiv-cs-ai

StackFeat RL: Reinforcement Learning over Iterative Dual Criterion Feature Selection for Stable Biomarker Discovery

arXiv:2604.22892v1 Announce Type: new Abstract: Feature selection in high-dimensional genomic data (d gg n) demands methods that are simultaneously accurate, sparse, and stable. Existing approaches ei

safetyarxiv-cs-lg

STAND: Semantic Anchoring Constraint with Dual-Granularity Disambiguation for Remote Sensing Image Change Captioning

arXiv:2604.23309v1 Announce Type: new Abstract: Remote sensing image change captioning (RSICC) aims to describe the difference between two remote sensing images. While recent methods have explored vid

Text Tells the Cost: Predicting and Analyzing Repayment Effort of Self-Admitted Technical Debt

arXiv:2309.06020v3 Announce Type: replace-cross Abstract: Technical debt refers to the consequences of sub-optimal decisions made during software development that prioritize short-term benefits over l

Translate or Simplify First: An Analysis of Cross-lingual Text Simplification in English and French

arXiv:2604.23844v1 Announce Type: new Abstract: Cross-Lingual Text Simplification (CLTS) aims to make content more accessible across languages by simultaneously addressing both linguistic complexity a

researcharxiv-cs-cl

Local Ai

U-ViLAR: Uncertainty-Aware Visual Localization for Autonomous Driving via Differentiable Association and Registration

arXiv:2507.04503v2 Announce Type: replace Abstract: Accurate localization using visual information is a critical yet challenging task, especially in urban environments where nearby buildings and const

local-aiarxiv-cs-cv

UNSEEN: A Cross-Stack LLM Unlearning Defense against AR-LLM Social Engineering Attacks

arXiv:2604.23141v1 Announce Type: cross Abstract: Emerging AR-LLM-based Social Engineering attack (e.g., SEAR) is at the edge of posing great threats to real-world social life. In such AR-LLM-SE attac

safetyarxiv-cs-ai

Unstable Rankings in Bayesian Deep Learning Evaluation

arXiv:2604.23102v1 Announce Type: new Abstract: Standard evaluations of Bayesian deep learning methods assume that metric estimates are reliable, but we show this assumption fails under data scarcity.

Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Unified and Generalized Approach

arXiv:2604.23953v1 Announce Type: cross Abstract: Blind omnidirectional image quality assessment (BOIQA) presents a great challenge to the visual quality assessment community, due to different storage

Well-Conditioned Oblivious Perturbations in Linear Space

arXiv:2604.23193v1 Announce Type: cross Abstract: Perturbing a deterministic n-dimensional matrix with small Gaussian noise is a cornerstone of smoothed analysis of algorithms [Spielman and Teng, JACM

Tutorials

When AI reviews science: Can we trust the referee?

arXiv:2604.23593v1 Announce Type: new Abstract: The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At the s

tutorialsarxiv-cs-ai

When Annotators Agree but Labels Disagree: The Projection Problem in Stance Detection

arXiv:2603.24231v2 Announce Type: replace Abstract: Stance detection is nearly always formulated as classifying text into Favor, Against, or Neutral. This convention was inherited from debate analysis

safetyarxiv-cs-cl

When Chain-of-Thought Fails, the Solution Hides in the Hidden States

arXiv:2604.23351v1 Announce Type: cross Abstract: Whether intermediate reasoning is computationally useful or merely explanatory depends on whether chain-of-thought (CoT) tokens contain task-relevant

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions

arXiv:2604.23528v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a promising machine learning framework for solving partial differential equations, but their training o