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arXiv:2604.22805v1 Announce Type: cross Abstract: Augmented reality (AR) systems pose unique privacy risks due to their continuous capture of visual data. Existing AR privacy frameworks lack semantic

applicationsarxiv-cs-ai

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Human DGX agent

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Seeing Is No Longer Believing: Frontier Image Generation Models, Synthetic Visual Evidence, and Real-World Risk

DGX agent

arXiv:2604.24197v1 Announce Type: cross Abstract: Frontier image generation has moved from artistic synthesis toward synthetic visual evidence. Systems such as GPT Image 2, Nano Banana Pro, Nano Banan

model-releasesarxiv-cs-ai

28 Apr 2026

Seer: Language Instructed Video Prediction with Latent Diffusion Models

arXiv:2303.14897v4 Announce Type: replace Abstract: Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video p

safetyarxiv-cs-cv

Selective Conformal Risk Control

arXiv:2512.12844v2 Announce Type: replace-cross Abstract: Reliable uncertainty quantification is essential for deploying machine learning systems in high-stakes domains. Conformal prediction provides

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

Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review

arXiv:2312.15020v4 Announce Type: replace-cross Abstract: Technical debt (TD) refers to the long-term costs associated with suboptimal design or code decisions in software development, often made to m

Local Ai

Self Knowledge Re-expression: A Fully Local Method for Adapting LLMs to Tasks Using Intrinsic Knowledge

arXiv:2604.22939v1 Announce Type: cross Abstract: While the next-token prediction (NTP) paradigm enables large language models (LLMs) to express their intrinsic knowledge, its sequential nature constr

local-aiarxiv-cs-ai

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

Hardware

Self-Rewarding Vision-Language Model via Reasoning Decomposition

arXiv:2508.19652v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) often suffer from visual hallucinations: generating things that are not consistent with visual inputs and language sho

hardwarearxiv-cs-cv

Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset

arXiv:2604.23025v1 Announce Type: cross Abstract: Android malware detectors built with machine learning often suffer from temporal bias: models are trained and evaluated without respecting apps' actua

safetyarxiv-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

safetyarxiv-cs-cv

Semantic Segmentation for Histopathology using Learned Regularization based on Global Proportions

arXiv:2604.24347v1 Announce Type: cross Abstract: In pathology, the spatial distribution and proportions of tissue types are key indicators of disease progression, and are more readily available than

SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation

arXiv:2604.23274v1 Announce Type: new Abstract: Semi-supervised learning addresses label scarcity and high annotation costs in medical image segmentation by exploiting the latent information in unlabe

SemiSAM-O1: How far can we push the boundary of annotation-efficient medical image segmentation?

arXiv:2604.24109v1 Announce Type: new Abstract: Semi-supervised learning (SSL) has become a promising solution to alleviate the annotation burden of deep learning-based medical image segmentation mode

SemML 2.0: Synthesizing Controllers for LTL

arXiv:2604.24102v1 Announce Type: new Abstract: Synthesizing a reactive system from specifications given in linear temporal logic (LTL) is a classical problem, finding its applications in safety-criti

safetyarxiv-cs-ai

Sentiment and Emotion Classification of Indonesian E-Commerce Reviews via Multi-Task BiLSTM and AutoML Benchmarking

arXiv:2604.24720v1 Announce Type: new Abstract: Indonesian marketplace reviews mix standard vocabulary with slang, regional loanwords, numeric shorthands, and emoji, making lexicon-based sentiment too

researcharxiv-cs-cl

SeqShield: A Behavioral Analysis Approach to Uncover Rootkits

arXiv:2604.23812v1 Announce Type: cross Abstract: Rootkits are among the most elusive types of malware, capable of bypassing traditional static analysis methods due to their metamorphic behavior. Sign

ServImage: An Image Generation and Editing Benchmark from Real-world Commercial Imaging Services

arXiv:2604.24023v1 Announce Type: new Abstract: Recent image generation and editing models demonstrate robust adherence to instructions and high visual quality on academic benchmarks. However, their p

SEVerA: Verified Synthesis of Self-Evolving Agents

model-releasesarxiv-cs-lg

arXiv:2603.25111v2 Announce Type: replace Abstract: Recent advances have shown the effectiveness of self-evolving LLM agents on tasks such as program repair and scientific discovery. In this paradigm,

SFT-then-RL Outperforms Mixed-Policy Methods for LLM Reasoning

arXiv:2604.23747v1 Announce Type: cross Abstract: Recent mixed-policy optimization methods for LLM reasoning that interleave or blend supervised and reinforcement learning signals report improvements

SGP-SAM: Self-Gated Prompting for Transferring 3D Segment Anything Models to Lesion Segmentation

arXiv:2604.22825v1 Announce Type: cross Abstract: Large segmentation foundation models such as the Segment Anything Model (SAM) have reshaped promptable segmentation in natural images, and recent effo

Tutorials

Shallow Neural Networks Learn Low-Degree Spherical Polynomials with Feature Learning by Learnable Channel Attention

arXiv:2512.20562v2 Announce Type: replace-cross Abstract: We study the problem of learning a low-degree spherical polynomial of degree ell_0 = Theta(1) ge 1 defined on the unit sphere in RR^d by train

tutorialsarxiv-cs-lg

Shape: A Self-Supervised 3D Geometry Foundation Model for Industrial CAD Analysis

arXiv:2604.22826v1 Announce Type: new Abstract: Industrial CAD workflows require robust, generalizable 3D geometric representations supporting accuracy and explainability. We introduce Shape, a self-s

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

ShapeUP: Scalable Image-Conditioned 3D Editing

arXiv:2602.05676v2 Announce Type: replace Abstract: Recent advancements in 3D foundation models have enabled the generation of high-fidelity assets, yet precise 3D manipulation remains a significant c

Shared-kernel Wavelet Neural Networks for Poisson Image Reconstruction

arXiv:2604.24000v1 Announce Type: cross Abstract: The Laplacian operator transforms the image into its Laplacian field, which usually is sparse and satisfies a stable distribution. On the other hand,

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

safetyarxiv-cs-cv

ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction

model-releasesarxiv-cs-cl

arXiv:2604.23813v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable performance in Visually Rich Document Understanding (VRDU) tasks, but their capabili

Shuffle and Joint Differential Privacy for Generalized Linear Contextual Bandits

arXiv:2602.00417v2 Announce Type: replace-cross Abstract: We present the first algorithms for generalized linear contextual bandits under shuffle differential privacy and joint differential privacy. W

SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoning

arXiv:2506.05425v2 Announce Type: replace-cross Abstract: Understanding social interaction, which encompasses perceiving numerous and subtle multimodal cues, inferring unobservable mental states and r

SketchVLM: Vision language models can annotate images to explain thoughts and guide users

arXiv:2604.22875v1 Announce Type: cross Abstract: When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-language models

Skill Retrieval Augmentation for Agentic AI

arXiv:2604.24594v1 Announce Type: cross Abstract: As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their

Sliced-Regularized Optimal Transport

arXiv:2604.23944v1 Announce Type: cross Abstract: We propose a new regularized optimal transport (OT) formulation, termed sliced-regularized optimal transport (SROT). Unlike entropic OT (EOT), which r

Sliced Wasserstein Steering between Gaussian Measures

arXiv:2604.22807v1 Announce Type: cross Abstract: Optimal transport with quadratic cost provides a geometric framework for steering an ensemble, modeled by a probability law, with minimal effort. Yet

Sliding Mode Control for Safe Trajectory Tracking with Moving Obstacles Avoidance: Experimental Validation on Planar Robots

arXiv:2604.24518v1 Announce Type: cross Abstract: This paper presents a unified control framework for robust trajectory tracking and moving obstacle avoidance applicable to a broad class of mobile rob

safetyarxiv-cs-ro

Small Language Model Helps Resolve Semantic Ambiguity of LLM Prompt

arXiv:2604.23263v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly utilized in various complex reasoning tasks due to their excellent instruction following capability. How

Tutorials

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs

arXiv:2604.23996v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) has become a prevalent backbone for large vision-language models (VLMs), yet how modality-specific signals should guide expert

tutorialsarxiv-cs-cv

Smoothing Slot Attention Iterations and Recurrences

arXiv:2508.05417v3 Announce Type: replace Abstract: Slot Attention (SA) lies at the heart of mainstream Object-Centric Learning (OCL). Image features can be aggregated into object-level representation

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

SMSI: System Model Security Inference: Automated Threat Modeling for Cyber-Physical Systems

arXiv:2604.23905v1 Announce Type: cross Abstract: Threat modeling for cyber-physical systems (CPS) remains a largely manual exercise. This project presents SMSI (System Model Security Inference), a hy

SoccerRef-Agents: Multi-Agent System for Automated Soccer Refereeing

arXiv:2604.23392v1 Announce Type: new Abstract: Refereeing is vital in sports, where fair, accurate, and explainable decisions are fundamental. While intelligent assistant technologies are being widel

SolarFCD: A Large-Scale Dataset and Benchmark for Solar Fault Classification in Photovoltaic Systems

arXiv:2604.23662v1 Announce Type: new Abstract: The increasing global deployment of solar photovoltaic (PV) systems needs robust, scalable, and automated inspection technologies capable of detecting a

SolarTformer: A Transformer Based Deep Learning Approach for Short Term Solar Power Forecasting

arXiv:2604.24306v1 Announce Type: cross Abstract: Accurate forecasting of solar power output is essential for efficient integration of renewable energy into the grid. In this study, an attention-based

Solving Nonlinear PDEs with Sparse Radial Basis Function Networks

arXiv:2505.07765v3 Announce Type: replace-cross Abstract: We propose a novel framework for solving nonlinear PDEs using sparse radial basis function (RBF) networks. Sparsity-promoting regularization i

SPAGS: Sparse-View Articulated Object Reconstruction from Single State via Planar Gaussian Splatting

arXiv:2511.17092v4 Announce Type: replace Abstract: Articulated objects are ubiquitous in daily environments, and their 3D reconstruction holds great significance across various fields. However, exist

Sparse Concept Anchoring for Interpretable and Controllable Neural Representations

arXiv:2512.12469v3 Announce Type: replace Abstract: We introduce Sparse Concept Anchoring, a method that biases latent space to position a targeted subset of concepts while allowing others to self-org

Spatiotemporal Degradation-Aware 3D Gaussian Splatting for Realistic Underwater Scene Reconstruction

arXiv:2604.23551v1 Announce Type: new Abstract: Reconstructing realistic underwater scenes from underwater video remains a meaningful yet challenging task in the multimedia domain. The inherent spatio

SPEAR-1: Scaling Beyond Robot Demonstrations via 3D Understanding

arXiv:2511.17411v2 Announce Type: replace-cross Abstract: Robotic Foundation Models (RFMs) hold great promise as generalist, end-to-end systems for robot control. Yet their ability to generalize acros