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Rotation Equivariant Convolutions in Deformable Registration of Brain MRI

arXiv:2604.08034v1 Announce Type: new Abstract: Image registration is a fundamental task that aligns anatomical structures between images. While CNNs perform well, they lack rotation equivariance - a

Tutorials

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RPM-Net Reciprocal Point MLP Network for Unknown Network Security Threat Detection

DGX agent

arXiv:2604.06638v1 Announce Type: cross Abstract: Effective detection of unknown network security threats in multi-class imbalanced environments is critical for maintaining cyberspace security. Curren

tutorialsarxiv-cs-ai

10 Apr 2026

RQR3D: Reparametrizing the regression targets for BEV-based 3D object detection

arXiv:2505.17732v2 Announce Type: replace Abstract: Accurate, fast, and reliable 3D perception is essential for autonomous driving. Recently, bird's-eye view (BEV)-based perception approaches have eme

agentsarxiv-cs-cv

$S^3$: Stratified Scaling Search for Test-Time in Diffusion Language Models

arXiv:2604.06260v1 Announce Type: cross Abstract: Test-time scaling investigates whether a fixed diffusion language model (DLM) can generate better outputs when given more inference compute, without a

Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU

arXiv:2604.07644v1 Announce Type: new Abstract: We present GPU-SLS, a GPU-parallelized framework for safe, robust nonlinear model predictive control (MPC) that scales to high-dimensional uncertain rob

safetyarxiv-cs-ro

SALLIE: Safeguarding Against Latent Language & Image Exploits

arXiv:2604.06247v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Vision-Language Models (VLMs) remain highly vulnerable to textual and visual jailbreaks, as well as prompt injections

Sampling-Aware 3D Spatial Analysis in Multiplexed Imaging

arXiv:2604.07890v1 Announce Type: new Abstract: Highly multiplexed microscopy enables rich spatial characterization of tissues at single-cell resolution, yet most analyses rely on two-dimensional sect

researcharxiv-cs-cv

SANDO: Safe Autonomous Trajectory Planning for Dynamic Unknown Environments

model-releasesarxiv-cs-ro

arXiv:2604.07599v1 Announce Type: new Abstract: SANDO is a safe trajectory planner for 3D dynamic unknown environments, where obstacle locations and motions are unknown a priori and a collision-free p

SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking

arXiv:2604.07922v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have revolutionized complex problem-solving, yet they exhibit a pervasive 'overthinking', generating unnecessarily long

researcharxiv-cs-cl

SAT: Selective Aggregation Transformer for Image Super-Resolution

arXiv:2604.07994v1 Announce Type: new Abstract: Transformer-based approaches have revolutionized image super-resolution by modeling long-range dependencies. However, the quadratic computational comple

local-aiarxiv-cs-cv

Say Something Else: Rethinking Contextual Privacy as Information Sufficiency

arXiv:2604.06409v1 Announce Type: cross Abstract: LLM agents increasingly draft messages on behalf of users, yet users routinely overshare sensitive information and disagree on what counts as private.

SBBTS: A Unified Schrodinger-Bass Framework for Synthetic Financial Time Series

arXiv:2604.07159v1 Announce Type: new Abstract: We study the problem of generating synthetic time series that reproduce both marginal distributions and temporal dynamics, a central challenge in financ

researcharxiv-cs-lg

Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction

arXiv:2604.08542v1 Announce Type: new Abstract: This paper addresses the task of large-scale 3D scene reconstruction from long video sequences. Recent feed-forward reconstruction models have shown pro

local-aiarxiv-cs-cv

Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems

arXiv:2604.08366v1 Announce Type: cross Abstract: Large-scale deep learning models for physical AI applications depend on diverse training data collection efforts. These models and correspondingly, th

agentsarxiv-cs-cv

SceneScribe-1M: A Large-Scale Video Dataset with Comprehensive Geometric and Semantic Annotations

model-releasesarxiv-cs-cv

arXiv:2604.07990v1 Announce Type: new Abstract: The convergence of 3D geometric perception and video synthesis has created an unprecedented demand for large-scale video data that is rich in both seman

Scientific Knowledge-driven Decoding Constraints Improving the Reliability of LLMs

arXiv:2604.06603v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong knowledge reserves and task-solving capabilities, but still face the challenge of severe hallucination,

SciFigDetect: A Benchmark for AI-Generated Scientific Figure Detection

model-releasesarxiv-cs-cv

arXiv:2604.08211v1 Announce Type: new Abstract: Modern multimodal generators can now produce scientific figures at near-publishable quality, creating a new challenge for visual forensics and research

sciwrite-lint: Verification Infrastructure for the Age of Science Vibe-Writing

arXiv:2604.08501v1 Announce Type: cross Abstract: Science currently offers two options for quality assurance, both inadequate. Journal gatekeeping claims to verify both integrity and contribution, but

SD-FSMIS: Adapting Stable Diffusion for Few-Shot Medical Image Segmentation

arXiv:2604.03134v2 Announce Type: replace Abstract: Few-Shot Medical Image Segmentation (FSMIS) aims to segment novel object classes in medical images using only minimal annotated examples, addressing

researcharxiv-cs-cv

SE-Enhanced ViT and BiLSTM-Based Intrusion Detection for Secure IIoT and IoMT Environments

arXiv:2604.06254v1 Announce Type: cross Abstract: With the rapid growth of interconnected devices in Industrial and Medical Internet of Things (IIoT and MIoT) ecosystems, ensuring timely and accurate

SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models

arXiv:2506.01062v4 Announce Type: replace Abstract: We introduce SealQA, a new challenge benchmark for evaluating SEarch-Augmented Language models on fact-seeking questions where web search yields con

Tutorials

Search-R3: Unifying Reasoning and Embedding in Large Language Models

arXiv:2510.07048v2 Announce Type: replace Abstract: Despite their remarkable natural language understanding capabilities, Large Language Models (LLMs) have been underutilized for retrieval tasks. We p

tutorialsarxiv-cs-cl

SearchAD: Large-Scale Rare Image Retrieval Dataset for Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2604.08008v1 Announce Type: new Abstract: Retrieving rare and safety-critical driving scenarios from large-scale datasets is essential for building robust autonomous driving (AD) systems. As dat

See the Forest for the Trees: Loosely Speculative Decoding via Visual-Semantic Guidance for Efficient Inference of Video LLMs

arXiv:2604.05650v2 Announce Type: replace Abstract: Video Large Language Models (Video-LLMs) excel in video understanding but suffer from high inference latency during autoregressive generation. Specu

researcharxiv-cs-cl

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts

arXiv:2604.08541v1 Announce Type: cross Abstract: Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks. However, we identify a puzzling phenomenon t

safetyarxiv-cs-cl

Seeing Like an AI: How LLMs Apply (and Misapply) Wikipedia Neutrality Norms

arXiv:2407.04183v4 Announce Type: replace Abstract: Large language models (LLMs) are trained on broad corpora and then used in communities with specialized norms. Is providing LLMs with community rule

safetyarxiv-cs-cl

SeLaR: Selective Latent Reasoning in Large Language Models

arXiv:2604.08299v1 Announce Type: new Abstract: Chain-of-Thought (CoT) has become a cornerstone of reasoning in large language models, yet its effectiveness is constrained by the limited expressivenes

researcharxiv-cs-cl

Selective Neuron Amplification for Training-Free Task Enhancement

arXiv:2604.07098v1 Announce Type: new Abstract: Large language models often fail on tasks they seem to already understand. In our experiments, this appears to be less about missing knowledge and more

researcharxiv-cs-lg

Self-Debias: Self-correcting for Debiasing Large Language Models

arXiv:2604.08243v1 Announce Type: new Abstract: Although Large Language Models (LLMs) demonstrate remarkable reasoning capabilities, inherent social biases often cascade throughout the Chain-of-Though

safetyarxiv-cs-cl

Self-Discovered Intention-aware Transformer for Multi-modal Vehicle Trajectory Prediction

arXiv:2604.07126v1 Announce Type: cross Abstract: Predicting vehicle trajectories plays an important role in autonomous driving and ITS applications. Although multiple deep learning algorithms are dev

agentsarxiv-cs-ai

Self-Distilled RLVR

arXiv:2604.03128v2 Announce Type: replace Abstract: On-policy distillation (OPD) has become a popular training paradigm in the LLM community. This paradigm selects a larger model as the teacher to pro

safetyarxiv-cs-lg

Self-Improving 4D Perception via Self-Distillation

arXiv:2604.08532v1 Announce Type: new Abstract: Large-scale multi-view reconstruction models have made remarkable progress, but most existing approaches still rely on fully supervised training with gr

researcharxiv-cs-cv

Self-Preference Bias in Rubric-Based Evaluation of Large Language Models

arXiv:2604.06996v1 Announce Type: cross Abstract: LLM-as-a-judge has become the de facto approach for evaluating LLM outputs. However, judges are known to exhibit self-preference bias (SPB): they tend

Self-Supervised Foundation Model for Calcium-imaging Population Dynamics

arXiv:2604.04958v2 Announce Type: replace-cross Abstract: Recent work suggests that large-scale, multi-animal modeling can significantly improve neural recording analysis. However, for functional calc

Applications

SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratio

arXiv:2604.06389v1 Announce Type: new Abstract: Uncertainty estimation for reasoning language models remains difficult to deploy in practice: sampling-based methods are computationally expensive, whil

applicationsarxiv-cs-ai

Sell More, Play Less: Benchmarking LLM Realistic Selling Skill

arXiv:2604.07054v2 Announce Type: replace Abstract: Sales dialogues require multi-turn, goal-directed persuasion under asymmetric incentives, which makes them a challenging setting for large language

Semantic-Aware UAV Command and Control for Efficient IoT Data Collection

arXiv:2604.08153v1 Announce Type: new Abstract: Unmanned Aerial Vehicles (UAVs) have emerged as a key enabler technology for data collection from Internet of Things (IoT) devices. However, effective d

safetyarxiv-cs-ro

Semantic Noise Reduction via Teacher-Guided Dual-Path Audio-Visual Representation Learning

arXiv:2604.08147v1 Announce Type: cross Abstract: Recent advances in audio-visual representation learning have shown the value of combining contrastive alignment with masked reconstruction. However, j

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP

arXiv:2509.26036v3 Announce Type: replace Abstract: While Contrastive Language-Image Pretraining (CLIP) excels at zero-shot tasks by aligning image and text embeddings, its performance in few-shot cla

Sensitivity-Positional Co-Localization in GQA Transformers

arXiv:2604.07766v1 Announce Type: new Abstract: We investigate a fundamental structural question in Grouped Query Attention (GQA) transformers: do the layers most sensitive to task correctness coincid

SensorPersona: An LLM-Empowered System for Continual Persona Extraction from Longitudinal Mobile Sensor Streams

arXiv:2604.06204v1 Announce Type: cross Abstract: Personalization is essential for Large Language Model (LLM)-based agents to adapt to users' preferences and improve response quality and task performa

agentsarxiv-cs-ai

SentinelSphere: Integrating AI-Powered Real-Time Threat Detection with Cybersecurity Awareness Training

arXiv:2604.06900v1 Announce Type: cross Abstract: The field of cybersecurity is confronted with two interrelated challenges: a worldwide deficit of qualified practitioners and ongoing human-factor wea

SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs

arXiv:2604.07737v1 Announce Type: new Abstract: While transformer-based Large Language Models (LLMs) theoretically support massive context windows, they suffer from severe performance degradation when

local-aiarxiv-cs-cl

Severity-Aware Weighted Loss for Arabic Medical Text Generation

arXiv:2604.06346v1 Announce Type: cross Abstract: Large language models have shown strong potential for Arabic medical text generation; however, traditional fine-tuning objectives treat all medical ca

ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference

arXiv:2508.16703v4 Announce Type: replace-cross Abstract: On-device running Large Language Models (LLMs) is nowadays a critical enabler towards preserving user privacy. We observe that the attention o

local-aiarxiv-cs-ai

SHAPE: Stage-aware Hierarchical Advantage via Potential Estimation for LLM Reasoning

arXiv:2604.06636v1 Announce Type: cross Abstract: Process supervision has emerged as a promising approach for enhancing LLM reasoning, yet existing methods fail to distinguish meaningful progress from

Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications

arXiv:2603.13970v2 Announce Type: replace Abstract: In High Energy Physics, as in many other fields of science, the application of machine learning techniques has been crucial in advancing our underst

researcharxiv-cs-lg

Shortcut Learning in Glomerular AI: Adversarial Penalties Hurt, Entropy Helps

arXiv:2604.07936v1 Announce Type: new Abstract: Stain variability is a pervasive source of distribution shift and potential shortcut learning in renal pathology AI. We ask whether lupus nephritis glom