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Segment-Level Risk Discovery in Online Handwriting for Alzheimer's Disease Detection

arXiv:2609.29384v1 Announce Type: cross Abstract: Online handwriting provides a non-invasive and low-cost behavioral biomarker for Alzheimer's disease (AD) detection, as it reflects both cognitive pla

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Selective Inference for Deep Clustering in Latent Spaces

DGX agent

arXiv:2609.28756v1 Announce Type: cross Abstract: Deep clustering is a powerful approach for discovering meaningful structures in high-dimensional data by learning a low-dimensional latent representat

safetyarxiv-cs-lg

25 Sept 2026

Self-Adaptive VLA for Robust Robot Deployment

arXiv:2609.30092v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models demonstrate impressive capabilities in robotic manipulation, their memoryless nature renders them brittle to

safetyarxiv-cs-cv

Tutorials

Self-Play Pretraining with Zero Data

arXiv:2609.30063v1 Announce Type: new Abstract: Advances in language modeling have been driven by scaling pretraining on ever more data. Yet, the training data is still largely curated on the model's

tutorialsarxiv-cs-ai

Self-Supervised Anchoring of Fingertip Sensing to Proprioception and Proactive Actions for Robot Imitation Learning

arXiv:2609.29822v1 Announce Type: new Abstract: Robotic imitation learning often relies on external cameras, yet local interaction cues such as object proximity, contact onset, and grasp state are dif

safetyarxiv-cs-ro

SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data

arXiv:2609.30238v1 Announce Type: new Abstract: Recent research on Multimodal Sentiment Analysis (MSA) has focused on learning from language, visual, and acoustic modalities with incomplete data to in

safetyarxiv-cs-cl

Sequential Confidence Sets for Coverage-Constrained Conformal Model Selection

arXiv:2609.28522v1 Announce Type: cross Abstract: Modern conformal forecasting systems often maintain several adaptive pipelines that differ in base forecasters, conformity scores, calibration windows

Sequential knowledge editing breaks a model's ability to tell good evidence from bad, without costing it accuracy

arXiv:2609.29587v1 Announce Type: new Abstract: Knowledge editing is evaluated on whether the edited fact changed, whether paraphrases follow, and whether unrelated answers stayed put. A model can pas

researcharxiv-cs-ai

SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models

arXiv:2609.28582v1 Announce Type: cross Abstract: The recent emergence of Time Series Foundation Models (TSFMs) has significantly advanced multi-step forecasting performance, enabling accurate predict

safetyarxiv-cs-ai

Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition

arXiv:2609.29373v1 Announce Type: new Abstract: Acoustic shadows from bone and other highly attenuating tissues obscure clinically important structures in ultrasound. In fetal brain imaging, skull-ind

researcharxiv-cs-cv

Sharp Limits for Honest Uncertainty in Hard-Budget Repeated Evaluation

arXiv:2609.29140v1 Announce Type: new Abstract: Repeated evaluation can estimate a benchmark score accurately while still requiring replication to certify narrow uncertainty. We characterize that requ

SHINE: Sequential Hierarchical Integration Network for EEG and MEG

arXiv:2602.23960v2 Announce Type: replace-cross Abstract: How natural speech is represented in the brain constitutes a major challenge for cognitive neuroscience. Reconstructing the speech envelope an

Sim-to-Real Aware End-to-End Learning Environment for Micromobility

arXiv:2609.28969v1 Announce Type: new Abstract: While end-to-end autonomous driving systems show promise, their application to micromobility vehicles is hindered by simulators failing to capture speci

safetyarxiv-cs-ro

Simple Torque-Observation Alignment for Zero-Shot Sim-to-Real Grasping with a Direct-Drive Gripper

arXiv:2609.29031v1 Announce Type: new Abstract: Torque observations in reinforcement learning remain challenging because simulated and measured torque differ in scale, offset, and noise. In this paper

safetyarxiv-cs-ro

Singularity Analysis for the Perspective-Four and Five-Line Problems

arXiv:2609.29417v1 Announce Type: new Abstract: This paper deals with image-based visual servoing and pose estimation by observing four and five lines. Our main interest is to determine the relative c

researcharxiv-cs-ro

SkinAgent AI: A Safety-Grounded Multimodal Agentic Framework for Non-Diagnostic Skincare Support

arXiv:2609.29341v1 Announce Type: new Abstract: Consumer-facing skincare AI must coordinate visual evidence, product information, tool use, and user-facing actions within explicit evidence and safety

SLCA-GRPO: Resolving Cross-Segment Credit Misattribution in Tool-Calling RL

arXiv:2609.29050v1 Announce Type: new Abstract: Tool-calling agents produce heterogeneous outputs, interleaving structured tool invocations with user-facing natural language summaries. This output het

safetyarxiv-cs-ai

Small yet Assistive: Spatially-Aware Post-Training for Low Vision

model-releasesarxiv-cs-cl

arXiv:2609.28757v1 Announce Type: cross Abstract: An estimated 1 billion people worldwide live with vision impairment, yet current vision-language models (VLMs) produce descriptions too vague for safe

Smartphone-Based Method for Automated Speed Enforcement

arXiv:2609.30107v1 Announce Type: new Abstract: Smartphone cameras and computer vision (CV) hold significant promise in assisting public agencies with enforcing traffic laws and enhancing road safety.

safetyarxiv-cs-cv

SMILESGNN: Interpretable Clinical Toxicity Prediction via SMILES-Graph Cross-Attention Fusion

arXiv:2609.28553v1 Announce Type: cross Abstract: Drug toxicity prediction is critical for reducing late-stage attrition in drug discovery, yet remains challenging due to severe class imbalance, scaff

researcharxiv-cs-ai

Spaceborne differential photogrammetry for control-free measurement of large-gradient deformation with structural immunity and a predictable accuracy envelope

arXiv:2609.29550v1 Announce Type: new Abstract: Optical satellite image correlation measures wide-area deformation in regimes where coherent interferometric synthetic aperture radar fails because disp

safetyarxiv-cs-cv

SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM

arXiv:2609.29446v1 Announce Type: new Abstract: Reducing communication in derivative-free decentralized learning requires controlling the disagreement accumulated over multiple local updates. This pap

SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference

arXiv:2609.28563v1 Announce Type: new Abstract: Spatial transcriptomics (ST) profiles gene expression within tissue architecture, but its cost and experimental complexity limit routine use. Predicting

SPARQL-LLM: Real-Time SPARQL Query Generation from Natural Language Questions

arXiv:2512.14277v2 Announce Type: replace-cross Abstract: The advent of large language models is contributing to the emergence of novel approaches that promise to better tackle the challenge of genera

Spatio-temporally complementary feature propagation on graphs for longitudinal AADT estimation

arXiv:2609.29906v1 Announce Type: new Abstract: The estimation of Annual Average Daily Traffic (AADT) is vital for transportation planning and infrastructure maintenance, yet obtaining accurate values

applicationsarxiv-cs-lg

Spectral Amplitude Purification in Distribution Matching for Diffusion Distillation

arXiv:2609.29116v1 Announce Type: new Abstract: Distribution Matching Distillation (DMD) enables high-quality diffusion sampling in only a few steps, but its optimization dynamics remain dominated by

researcharxiv-cs-cv

Spectral Graph Neural Networks with Hermite Polynomials: A Comprehensive Study

arXiv:2609.28979v1 Announce Type: new Abstract: We study spectral graph neural networks built from Hermite polynomials and propose HermNet, a simple model that combines a nodewise predictor with norma

safetyarxiv-cs-lg

Spectral-Guided Diffusion: Accelerating Inference via Static Spectral Layer Scheduling

arXiv:2609.29505v1 Announce Type: new Abstract: Diffusion inference repeatedly evaluates the same large network. We ask whether pretrained weights alone can identify residual branches that need not be

SpectralCTGaussians: Projection-Domain Reconstruction and Basis Material Decomposition for Spectral CT using 3D Gaussian Splatting

arXiv:2609.29638v1 Announce Type: new Abstract: Spectral computed tomography (CT) extends conventional CT by measuring attenuation across multiple energy channels, allowing improved modeling of physic

applicationsarxiv-cs-cv

Speculative Evaluation of Stochastic LLMs

arXiv:2609.28560v1 Announce Type: cross Abstract: Evaluating a stochastic large language model is costly: benchmark scores estimate expected performance from randomized rollouts, yet uniform repetitio

Tutorials

SplatLabel: Pseudo-Labelling through 4D Gaussian Splatting

arXiv:2609.29836v1 Announce Type: new Abstract: While 2D Vision Foundation Models offer a pathway to automate 3D semantic pseudo-labelling, translating these priors into robust 3D representations typi

tutorialsarxiv-cs-cv

Spooftral: Can Voxtral Audio-Language Model Detect Speech Spoofing?

arXiv:2609.28713v1 Announce Type: cross Abstract: Self-supervised learning (SSL) countermeasures (CMs) have shown strong performance in recent years. However, they often show degraded performance whil

researcharxiv-cs-cl

Spot, Separate, and Enhance: Fully Generative Approach for Audio Mixing

arXiv:2609.29169v1 Announce Type: cross Abstract: We introduce Spot, Separate, and Enhance (SSE), the first multimodal, user-guided generative model for audio remixing and enhancement. SSE enhances vi

Stable and Faithful Explanations for Knowledge Tracing

arXiv:2609.28502v1 Announce Type: new Abstract: Knowledge tracing (KT) models predict student performance opaquely, limiting pedagogical action. This study contributes a validation protocol testing pr

Stacked SVD or SVD stacked? A Random Matrix Theory perspective on data integration

arXiv:2507.22170v2 Announce Type: replace-cross Abstract: Modern data analysis increasingly requires identifying shared latent structure across multiple high-dimensional datasets. A commonly used mode

safetyarxiv-cs-lg

Stale Does Not Mean Unsafe: Guard Precision for Tool-Using LLM Agents under Infrastructure State Races

arXiv:2609.29522v1 Announce Type: new Abstract: Tool-using language-model agents increasingly mutate schedulers, data pipelines, object stores, and access-control systems. Between an agent's read and

StepCOPS: Closed-Testing Lower-Tail Certificates for Language-Model Policy Selection

model-releasesarxiv-cs-cl

arXiv:2609.29549v1 Announce Type: new Abstract: Post-training pipelines must select one language-model policy from many checkpoints, prompts, and decoding rules. Mean evaluator scores can conceal rare

Stochastic Inertial Krasnosel'skii-Mann Iteration Achieves Near-Optimal Sample Complexity

model-releasesarxiv-cs-lg

arXiv:2609.28543v1 Announce Type: cross Abstract: We analyze a simple stochastic inertial Krasnosel'skii--Mann (iKM) method for finding a fixed point of a nonexpansive operator in a real Hilbert space

Agents

Stochastic Semantic Evidence Graphs: Uncertainty Propagation and Governance for Agentic AI

arXiv:2609.29703v1 Announce Type: new Abstract: AI-agent evaluations usually inspect a final answer, yet error may enter through evidence, retrieval, prompting, generation or decision mapping. We intr

agentsarxiv-cs-cl

STRAND: Benchmarking and Improving Object-Centric Spatio-Temporal Monitoring in Video Large Language Models

model-releasesarxiv-cs-cl

arXiv:2609.29607v1 Announce Type: cross Abstract: While multimodal large language models (MLLMs) have advanced video understanding, they remain highly prone to hallucinations in dynamic scenes. We arg

Stream Recursion Model (SRM)

model-releasesarxiv-cs-lg

arXiv:2609.28809v1 Announce Type: new Abstract: Mechanistic interpretability seeks to make verifiable statements about the internal behavior of large language models (LLMs). Many interpretability tech

Tutorials

Streaming-WAM: Action-Conditioned World-Action Model for Asynchronous Robot Manipulation

arXiv:2609.28927v1 Announce Type: new Abstract: World action models (WAMs) that use future visual prediction at inference time incur substantial generation costs. Asynchronous execution reduces waitin

tutorialsarxiv-cs-ro

Structured Pose-Conditioned Flow Matching for Generative 5G CSI Augmentation

arXiv:2609.29912v1 Announce Type: cross Abstract: With the growing demand for privacy-preserving and occlusion-resilient human pose recognition (HPR), 5G channel state information (CSI) offers a promi

applicationsarxiv-cs-ai

Style, Not Self: Surface Cues Explain Zero-Shot Code Attribution by Large Language Models

arXiv:2609.30048v1 Announce Type: new Abstract: If a language model can recognize code it wrote, it may favor that code as a judge, and instances of one model monitoring each other could collude. We t

Sufficiently Reduced Distributional Regression

arXiv:2609.29291v1 Announce Type: cross Abstract: We propose Sufficiently Reduced Distributional Regression (SRDR), a generative method that combines conditional distribution estimation with nonlinear

SWE-Prometheus: Measuring Engineering Governance Improvements in Real-World Repositories

arXiv:2609.29465v1 Announce Type: new Abstract: Large language model based coding agents have made substantial progress on repository-level software engineering tasks. Existing repository benchmarks,

SwitchPFN: Shared Switching Dynamics for Frozen In-Context Time Series Classification

model-releasesarxiv-cs-lg

arXiv:2609.29814v1 Announce Type: new Abstract: Tabular foundation models (TFMs) provide a promising route to time-series classification, but their effectiveness depends on how sequential data are con

Synthetic Enclosed Echoes: A New Dataset to Mitigate the Gap Between Simulated and Real-World Sonar Data

arXiv:2505.15465v2 Announce Type: replace Abstract: This paper introduces Synthetic Enclosed Echoes (SEE), a novel dataset designed to enhance robot perception and 3D reconstruction capabilities in un

applicationsarxiv-cs-ro