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SlideDP: Scaling Host-Resident LLM Fine-Tuning Across Multiple GPUs

arXiv:2609.34162v1 Announce Type: cross Abstract: Host-resident layer streaming enables full-parameter LLM fine-tuning beyond GPU memory, but data-parallel ranks compete for shared host resources. Rep

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SliderEdit++: Improved Slider Trajectories in Instruction-Based Image Editing

DGX agent

arXiv:2610.14253v1 Announce Type: new Abstract: Slider-based control aims to give users continuous, fine-grained control over the intensity of individual edits in instruction-based image editing. Howe

model-releasesarxiv-cs-cv

14 Oct 2026

SlotAVS: Object-Centric Audio-Visual Segmentation via Bi-Modal Slot Attention

arXiv:2610.15764v1 Announce Type: new Abstract: Audio-Visual Segmentation (AVS) requires predicting pixel-wise masks of sounding objects by grounding acoustic signals in visual space. In multi-source

safetyarxiv-cs-cv

Small area learning

arXiv:2610.15953v1 Announce Type: cross Abstract: Small area estimation (SAE) traditionally borrows strength across domains through a prespecified working model. We introduce small area learning (SAL)

Smartphone-Based, Field-Deployed AI Inspection System for Power Distribution Infrastructure

arXiv:2610.17620v1 Announce Type: new Abstract: Routine inspection of power distribution infrastructure remains challenging for utilities operating geographically dispersed networks, where conventiona

Smoothness Ranks Skill, Not Success: An Audit of Published Trajectory-Smoothness Curation Metrics Under Episode-Length Controls

arXiv:2610.17427v1 Announce Type: new Abstract: Trajectory smoothness has been proposed as a cheap, training-free signal for curating imitation-learning demonstrations, most recently by rinse, which i

safetyarxiv-cs-ro

Social Priors: Descriptions of AI Users Shape How Language Models Trust and Cooperate

arXiv:2610.16946v1 Announce Type: new Abstract: Descriptions of how people use AI are becoming training data for the next generation of models. We ask whether what a model reads about its users change

Applications

SocraticRepair: Planning-Guided Socratic Tutoring for Program Debugging and Misconception Repair

arXiv:2610.15204v1 Announce Type: cross Abstract: Large language models are increasingly used in computer science education. However, the teaching abilities of even the most powerful LLMs are limited:

applicationsarxiv-cs-lg

Soft MPCritic: Value-Based Reinforcement Learning for Non-Differentiable Predictive Control

arXiv:2604.01477v2 Announce Type: replace Abstract: Reinforcement learning (RL) and model predictive control (MPC) offer complementary strengths, yet existing MPC-RL frameworks commonly rely on gradie

Soft Teacher Forcing

arXiv:2610.17189v1 Announce Type: cross Abstract: Soft tokens have so far been used to replace discrete chain-of-thought in latent reasoning methods. These approaches typically require multi-stage tra

Softmax-Free Attention in Spiking Transformers as the Zero-Temperature Limit of Two-Level Systems

arXiv:2610.15968v1 Announce Type: new Abstract: Spiking Transformers achieve energy efficiency by discarding softmax normalization in favor of thresholded, binary attention. However, this removal brea

SoK: Rethinking Membership Inference in Tabular Machine Learning

arXiv:2610.18486v1 Announce Type: new Abstract: Membership Inference Attacks (MIAs) are currently the dominant approach to evaluating privacy in machine learning applications. Despite their significan

Solvating machine-learning interatomic potentials for solution-phase chemistry

arXiv:2610.17226v1 Announce Type: cross Abstract: Most machine-learning interatomic potentials (MLIPs) are trained on gas-phase data, which limits their use for chemistry in solution. Here, we introdu

SOVABench: A Vehicle Surveillance Action Retrieval Benchmark for Multimodal Large Language Models

arXiv:2601.04824v3 Announce Type: replace Abstract: Automatic identification of events and recurrent behavior analysis are critical for video surveillance. However, most existing content-based video r

SP^2ec: Adaptive Self-Speculative Decoding for Vision-Language Models

arXiv:2610.18735v1 Announce Type: new Abstract: Speculative Decoding (SD) has demonstrated its efficacy in accelerating large language model inference, but for Vision-Language Models (VLMs), the diver

SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis

arXiv:2610.17808v1 Announce Type: new Abstract: Pixel-level annotation for chest X-ray (CXR) lesion segmentation is expensive, making active learning (AL) essential. However, existing AL strategies fa

safetyarxiv-cs-cv

SPARE: Shared Perception and Adaptive Retrieval of Evidence for Streaming Video Understanding

arXiv:2610.14671v1 Announce Type: new Abstract: Streaming video understanding requires answering questions from the observed video, with evidence drawn from recent observations or earlier events. Hist

Sparse All-Layer Connector for Domain Generalised Semantic Segmentation

arXiv:2610.15660v1 Announce Type: new Abstract: Domain generalised semantic segmentation (DGSS) has recently benefited from combining multiple foundation models, such as vision-language and vision fou

Sparse-TD-JEPA: Improving Zero-Shot Reinforcement Learning With Sparse Latent Task Representations

arXiv:2610.14400v1 Announce Type: new Abstract: Successor-feature-based zero-shot reinforcement learning methods pre-train a task encoder and successor-feature predictor on offline, reward-free data,

Sparsity Saves Compute, Not Samples: Oracle Inequalities for Dense and Sparse Mixture of Experts

arXiv:2610.15039v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models combine specialized experts through an input-dependent routing mechanism, and their sparse variants underpin many modern

Spatiotemporal Recognition of Abdominal Wall Layers During Optical Trocar Entry

arXiv:2610.14512v1 Announce Type: new Abstract: Objective. To evaluate whether spatial context and temporal modeling improve tissue recognition during optical trocar entry, where multiple abdominal-wa

Spectral Considerations on Unsupervised Time-domain EEG Representation Learning with Deep Networks

arXiv:2610.16264v1 Announce Type: new Abstract: Electroencephalography (EEG) is a non-invasive, scalable source of quantitative biomarkers for the early diagnosis of brain disorders, and deep represen

Spectral Costs of Multinomial and Residual Resampling in High-Dimensional Particle Ensembles: A Free-Probability Analysis

arXiv:2610.16680v1 Announce Type: cross Abstract: Resampling is the standard remedy against weight degeneracy in particle filters, yet its effect on the spectrum of the particle second-moment matrix i

Spectral-Driven Adaptive-Rank Optimizer for Large Language Model Reinforcement Learning with Verifiable Rewards

arXiv:2610.16533v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is reshaping how large language models (LLMs) acquire reasoning capabilities, yet the optimizers

Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary

arXiv:2302.00985v3 Announce Type: replace-cross Abstract: We consider online scheduling on unrelated (heterogeneous) machines in a speed-oblivious setting, where an algorithm is unaware of the exact j

Agents

SpikeOcc: Towards Resilient Multimodal 3D Occupancy Perception via Spiking Experts

arXiv:2610.15745v1 Announce Type: new Abstract: Multimodal 3D occupancy prediction underpins safe autonomous driving through comprehensive scene understanding. However, existing methods have largely p

agentsarxiv-cs-cv

Hardware

SplatBus: A Gaussian Splatting Viewer Framework via GPU Interprocess Communication

arXiv:2601.15431v2 Announce Type: replace-cross Abstract: Radiance field-based rendering methods have attracted significant interest from the computer vision and computer graphics communities. They en

hardwarearxiv-cs-cv

SPLICE: Sparse Physics Library Identification and Compositional Expansion

arXiv:2610.17029v1 Announce Type: new Abstract: Neural operator splitting composes a library of neural operators, one per physical mechanism, into zero-shot simulators of new equations. Existing appro

Splicing Hints into Reasoning Traces: Learning from Demonstrator Failures

arXiv:2610.13996v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves mathematical reasoning when a final answer can be checked, but a correct answer does not certify

Applications

Split Conformal Interval Length under Huber Contamination

arXiv:2610.15458v1 Announce Type: new Abstract: Existing analyses of interval length in split conformal prediction largely focus on clean data. However, how contamination at the training and calibrati

applicationsarxiv-cs-lg

SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering

arXiv:2610.14405v1 Announce Type: cross Abstract: Reinforcement learning (RL) has driven substantial progress in coding agents by relying on verified successful trajectories as learning signals. Howev

SQ-CBF: Signed Distance Functions for Reliable Superquadric-Based Safety Filtering

arXiv:2602.11049v2 Announce Type: replace Abstract: Ensuring safe robot operation in cluttered and dynamic environments remains a fundamental challenge. While control barrier functions (CBFs) provide

safetyarxiv-cs-ro

Squeeze-Aware-YOLO: Early Channel Recalibration and Multi-Scale Fusion for Real-Time Traffic Sign Detection

arXiv:2610.15604v1 Announce Type: new Abstract: Traffic sign detection plays a vital role in intelligent transportation systems, supporting the safe operation of advanced driver assistance systems and

SSM-Attack: Learning Where to Perturb for Black-Box Face Privacy

arXiv:2610.17323v1 Announce Type: new Abstract: Face recognition systems are deployed widely enough that a photograph shared online can be matched without its subject's consent. Generator-based protec

ST-GRU PredRNN-V2: A GRU-Based Reformulation of PredRNN-V2 for Video Prediction

arXiv:2610.18645v1 Announce Type: new Abstract: Video prediction requires models to capture complex spatiotemporal dependencies and to retain relevant information over long sequences. PredRNN and its

Stable-Plastic Subspace Construction for Differentially Private Continual LoRA

arXiv:2610.17904v1 Announce Type: new Abstract: Continual learning (CL) adapts pretrained models to a sequence of tasks while retaining knowledge acquired from earlier tasks. When task data are sensit

Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection

arXiv:2606.19774v2 Announce Type: replace Abstract: Action chunking enables robot policies to produce temporally coherent behavior, but generating multi-step action sequences with flow-based policies

safetyarxiv-cs-ro

Agents

StateSkill-Agent: An Effective and Efficient Embodied Agent via State-conditioned Skill

arXiv:2610.14601v1 Announce Type: new Abstract: In this work, we aim to develop an embodied MLLM agent that understands and solves embodied tasks by learning, abstracting, and reusing transferrable an

agentsarxiv-cs-ro

Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa

arXiv:2610.16816v1 Announce Type: cross Abstract: Global reanalyses are too coarse to resolve the convective systems that produce most West African monsoon rainfall. In this work, we use U-Net based a

Statistical Vignettes on the Grokking Phenomenon

arXiv:2610.18811v1 Announce Type: new Abstract: In this work, we study grokking in a simple, analytically tractable setting whose rich phenomenology allows us to disentangle the effects of initializat

Steering the Fidelity in State Preparation on Noisy Bosonic Systems with Risk-Seeking Optimization

arXiv:2610.18495v1 Announce Type: cross Abstract: In order to execute quantum algorithms on bosonic quantum computers, it is necessary to encode their inputs into a desired target quantum state, with

Steertuning Policies from a Few Human Videos

model-releasesarxiv-cs-ro

arXiv:2610.18711v1 Announce Type: new Abstract: Current recipes for robot learning require massive amounts of robot demonstrations to obtain robust and adaptive policies. In this work, we introduce St

Stein Variational Distributional Reinforcement Learning: Score-Guided Particles for Multivariate Return Distributions

arXiv:2610.17925v1 Announce Type: new Abstract: Decisions that weigh several reward signals against each other require the joint distribution of the return vector. Categorical critics scale exponentia

Stereo-Planner: Learning Stereo-to-Trajectory Planning for Zero-Shot Sim-to-Real Quadrotor Navigation

model-releasesarxiv-cs-ro

arXiv:2610.15560v1 Announce Type: new Abstract: Existing navigation systems commonly rely on dedicated depth sensors or explicit depth reconstruction, while learning to generate trajectories directly

StimFlow: Synthesizing Brain Activity from Images via Stimulus-conditioned Flow Matching

arXiv:2610.15341v1 Announce Type: new Abstract: The inherent variability of fMRI responses across trials encourages a generative view of the stimulus-to-response mapping. Prior generative encoders oft

Stochastic Difference-of-Convex Optimization with Momentum

arXiv:2510.17503v2 Announce Type: replace Abstract: We study the online stochastic difference-of-convex (DC) program min_{x in R^n} F(x) = G(x) - H(x) + r_1(x) - r_2(x), which covers sparse learning w

Applications

StreamingWAM: Streaming World Action Modeling via Chunk-Wise Staircase Flow Matching

arXiv:2610.15585v1 Announce Type: new Abstract: World Action Models (WAMs), often built upon pretrained video-generation world models, have emerged as a promising framework for general-purpose and ope

applicationsarxiv-cs-ro

Streamline pathology foundation model by cross-magnification distillation

arXiv:2509.23097v4 Announce Type: replace Abstract: Foundation models (FM) have transformed computational pathology but remain computationally prohibitive for clinical deployment due to their massive