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Sampling Allocation of LinUCB: Optimal Design Limits in the Small-Gap Regime

arXiv:2610.06213v1 Announce Type: new Abstract: We study the sampling allocation of LinUCB in the small-gap regime, where the reward gaps are of order at most n^{-1/2} over the decision horizon n. Thi

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Saying, Not Knowing: Aggressively GGUF-Quantized Small Language Models Still Write Rare Words They Can No Longer Define

DGX agent

arXiv:2610.04403v1 Announce Type: cross Abstract: Post-training quantization to the GGUF format's mixed-precision K-quants is commonly how open-weight language models reach consumer hardware, yet its

model-releasesarxiv-cs-lg

6 Oct 2026

Scalable Decision Making for Games of Imperfect Information

arXiv:2511.07312v2 Announce Type: replace Abstract: Real-world decision-making generally involves hidden information, that is, information that is unknown to one agent but possessed by another. Unfort

agentsarxiv-cs-lg

Scalable Minimal-Change Learning for Controllable Image Editing

arXiv:2610.06021v1 Announce Type: new Abstract: Image editing should change only the attributes specified by an instruction while preserving everything else, yet current methods often make unintended

local-aiarxiv-cs-cv

Scaling 3D Visual Grounding in Abdominal CT

arXiv:2610.04095v1 Announce Type: new Abstract: Visual grounding models can enhance radiology workflows by linking report findings to image regions. This is particularly valuable for 3D CT, where find

Scaling Down the Scaling Laws: Parameter Efficiency and Compute-Optimal Training in Resource-Constrained Large Language Models

arXiv:2610.06387v1 Announce Type: new Abstract: Large language models (LLMs) have achieved substantial performance gains through increases in model size, training data, and computational resources. Ho

Scenario-Based Compositional Statistical Model Checking for Safety Specifications

arXiv:2610.05571v1 Announce Type: cross Abstract: In safety-critical domains such as autonomous driving, systems must be evaluated across a large number of environment conditions, often represented as

safetyarxiv-cs-ro

ScopeSAE: Model-Scope Feature Discovery with Interpretable Layer Selection

arXiv:2610.04905v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are a central tool in mechanistic interpretability. However, existing SAEs are primarily trained per layer. The modeling subs

Applications

Score Approximation for Diffusion Models on Arbitrary Low-Dimensional Structures

arXiv:2606.19894v2 Announce Type: replace Abstract: Score-based diffusion models have achieved remarkable empirical success, motivating extensive theoretical work to establish their foundations. Howev

applicationsarxiv-cs-lg

Score-Calibrated Flow for Sampling from Unnormalized Densities with Applications to Generative Online Reinforcement Learning

arXiv:2610.04696v1 Announce Type: new Abstract: Diffusion and flow models provide expressive policy classes for online reinforcement learning (RL), enabling multimodal behaviors and improved performan

Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans

arXiv:2410.18275v2 Announce Type: replace Abstract: In this paper, we study the problem of methodically obtaining a sufficient set of kinesthetic demonstrations, one at a time, such that a robot can b

researcharxiv-cs-ro

SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting

arXiv:2610.04109v1 Announce Type: new Abstract: Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical nu

Selecting Long-Horizon Trajectories for Reliable and Efficient Terminal-Agent Training

arXiv:2610.05831v1 Announce Type: new Abstract: Terminal agents are commonly trained by imitating long teacher trajectories, yet how much of each trajectory to supervise remains unexplored. We study t

Selective Backpropagation for Efficient Few-Shot Class-Incremental Learning

model-releasesarxiv-cs-cv

arXiv:2610.04003v1 Announce Type: cross Abstract: Few-Shot Class-Incremental Learning (FSCIL) requires models to continuously learn new classes from limited samples while retaining prior knowledge, un

SelectOccFlow: Selective Spatiotemporal Aggregation for 3D Occupancy and Scene Flow Prediction

arXiv:2610.04356v1 Announce Type: new Abstract: Comprehensive 3D scene understanding for autonomous driving requires modeling geometry, semantics, and motion. However, camera-based occupancy and scene

agentsarxiv-cs-cv

Self-Reflection Fine-Tuning: Enhancing Agent Security against Prompt Injection Attacks from Failure Experience

arXiv:2610.04269v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in tool-augmented environments, but their reliance on external inputs makes them highly vuln

SemCam: Semantic Camera Motion Control for Video Generation

model-releasesarxiv-cs-cv

arXiv:2610.05141v1 Announce Type: new Abstract: Controlling the camera relative to a moving subject in an existing video is challenging: behaviors such as maintaining a frontal view require the camera

Separating Decision Time from Decision Quality in the Real-Time Gap of Distilled Deciders: Evidence from a Game and a Conveyor Simulator

arXiv:2610.04810v1 Announce Type: new Abstract: Real-time agents are often evaluated with the world paused, or with decision time rounded to whole ticks (tick conversion). Tick conversion predicts alm

Separators Make Carry Propagation Learnable:The Geometry of Latent Carry in a Multiplication Transformer

arXiv:2610.06605v1 Announce Type: new Abstract: Transformers asked to multiply multi-digit numbers in a single forward pass often fail, and interpretability studies of pretrained language models find

SepRQ : Self-Supervised Speech Mixture Representation Learning via Mask-Free, Multi-Scale Source Separation

arXiv:2610.04690v1 Announce Type: cross Abstract: Self-supervised learning (SSL) is standard for speech representation learning, but mainstream models are designed around single-speaker audio, limitin

SFlexRCA: Lightweight, Scalable, and Flexible Root Cause Analysis for IIoT Edge Systems

arXiv:2610.04893v1 Announce Type: new Abstract: Industrial Internet of Things (IIoT) systems generate high-dimensional sensor telemetry from interconnected components, where faults can propagate acros

Shadow Feature Refinement Network: Progressive Feature Refinement based on Knowledge Distillation for Effective Shadow Removal

arXiv:2610.05325v1 Announce Type: new Abstract: In the field of deep learning, has seen significant advancements; however, shadow removal remains a persistent challenge owing to the variable sizes and

Shapley-based Structural Analysis of Neural Calibration for Stochastic Volatility Models

arXiv:2610.03076v1 Announce Type: cross Abstract: Neural network-based approaches have emerged as efficient alternatives to traditional optimization-based procedures for the calibration of stochastic

Shared Geometry Is Not Shared Physics: A Layerwise Test of the Platonic Representation Hypothesis in Astronomy

arXiv:2610.04130v1 Announce Type: cross Abstract: In this paper we investigate whether geometrically aligned astronomical representations are also scientifically interchangeable. We analyze every laye

local-aiarxiv-cs-lg

Sharp Convergence and Sample Complexity of Policy Mirror Descent for Average-Reward MDPs

arXiv:2610.04117v1 Announce Type: new Abstract: Policy mirror descent (PMD) has a mature finite-time theory in discounted Markov decision processes (MDPs), but less is known in the average-reward sett

Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms

arXiv:2304.03056v1 Announce Type: cross Abstract: In this work, we derive sharp non-asymptotic deviation bounds for weighted sums of Dirichlet random variables. These bounds are based on a novel integ

Sharp dimensional analysis of midpoint methods for Langevin sampling

arXiv:2610.06308v1 Announce Type: cross Abstract: We study deterministic and randomized midpoint discretizations of Langevin dynamics for a target pi propto e^{-V}, where 0 prec alpha Ipreceqnabla^2Vp

Sharp Integrality Gaps in Calibration Distance

arXiv:2610.05679v1 Announce Type: new Abstract: We study the offline gap between deterministic calibration distance C and its fractional relaxation L for binary unit-weight sequences under total absol

Sharpen Without Search: On-Policy Distillation of Sequence-Level Power Distribution

arXiv:2610.06804v1 Announce Type: new Abstract: A language model can give a correct answer more probability than any single incorrect answer and still usually sample an incorrect one, because the inco

SIFT: Robust Meta-Faithfulness Verification of Chain-of-Thought Reasoning Under Distribution Shift

arXiv:2610.04594v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) faithfulness detectors are widely used to audit reasoning models, yet a detector is itself a predictor whose verdicts are treat

Tutorials

SimAuthor: Harnessing Foundation Models for Persistent Scientific Simulator Authoring

arXiv:2610.06257v1 Announce Type: new Abstract: Foundation models can generate scientific code, but authoring a scientific simulator (an executable program encoding hypotheses about how mechanisms gen

tutorialsarxiv-cs-lg

SimForcing: Distilling Simulation Motion Priors into Real-Domain Robot World Models

arXiv:2610.06598v1 Announce Type: cross Abstract: Action-conditioned robot world models must respond precisely to robot trajectories while preserving realistic visual dynamics, yet learning both from

safetyarxiv-cs-cv

Singular parameters and missing limits in neural PDE solvers

arXiv:2610.06770v1 Announce Type: cross Abstract: Neural solvers for partial differential equations (PDEs) can approach an accurate solution while their parameters grow without bound. In such cases, t

SkillScriptBench: Benchmarking Self-Evolution of Executable Agent Skill Packages Beyond Markdown

arXiv:2610.04008v1 Announce Type: cross Abstract: Executable Agent Skills combine natural-language instructions and scripts into reusable packages for LLM agents, and revising them requires fixing err

Slaying the Hydra: Interaction-Aware Circuit Discovery in Language Models

arXiv:2610.04017v1 Announce Type: new Abstract: Localizing behavior to individual components of a language model is a central goal of mechanistic interpretability. However, scoring components one at a

local-aiarxiv-cs-lg

Smoothed Gradient Method for Nonconvex Federated Stochastic Bilevel Optimization

arXiv:2610.05290v1 Announce Type: new Abstract: In recent years, federated stochastic bilevel optimization has attracted increasing attention due to its wide range of applications in machine learning.

SO(3)-RoPE for Spherical Transformers

arXiv:2610.06229v1 Announce Type: new Abstract: Spherical data arise in many scientific applications. Often spherical transformers disregard the geometry of the underlying spherical domain, causing di

SoccerTrack v2: A Full-Pitch Panoramic Video Dataset for Game State Reconstruction and Ball Action Spotting

model-releasesarxiv-cs-cv

arXiv:2508.01802v2 Announce Type: replace Abstract: Soccer analytics draws on two kinds of information: spatio-temporal data describing where players and the ball are, and event data describing what t

Social Navigation for Tour-guide Robot

arXiv:2610.05455v1 Announce Type: new Abstract: We propose a force-based model for social navigation of a tour-guide robot. Social forces due to various factors like obstacles, user position and headi

safetyarxiv-cs-ro

Soft Strategy Selection for Batch-Mode Active Learning

arXiv:2610.05544v1 Announce Type: new Abstract: Real-world deployment of active learning typically forces practitioners to choose an acquisition strategy before any data is labeled. This is a daunting

Software World Models: From Consequence Prediction to Decision Value

arXiv:2610.04940v1 Announce Type: new Abstract: A coding agent may safely modify one repository while silently breaking downstream services, libraries, or datastores that depend on it. Exhaustively ru

agentsarxiv-cs-lg

SOL: Measuring Gaps between Text Distributions by Double Sliced Wasserstein Metrics

arXiv:2610.06513v1 Announce Type: cross Abstract: Evaluating text generation requires measuring how well the generated distribution matches the data distribution. For autoregressive models, this is do

SoundWeaver: Compositional Warm-Starting for Text-to-Audio Diffusion Serving

arXiv:2603.07865v2 Announce Type: replace-cross Abstract: Text-to-audio (T2A) diffusion models generate high-quality audio but require tens of neural function evaluations (NFEs), resulting in substant

SPACE-CLIP: Spatial Perception via Adaptive CLIP Embeddings for Monocular Depth Estimation

arXiv:2601.17657v4 Announce Type: replace Abstract: Robotic and autonomous systems need dense spatial cues, yet adding a dedicated depth estimator can duplicate visual processing already performed by

agentsarxiv-cs-cv

SPACE-CLIPv2: Decoding Local Geometry from Frozen CLIP for Monocular Depth Estimation

arXiv:2610.05029v1 Announce Type: new Abstract: Vision-language foundation models such as CLIP provide strong semantic representations, but their patch tokens are not directly optimized for dense metr

local-aiarxiv-cs-cv

Spacecraft Rendezvous Trajectory Generation with Modular Constraints via Diffusion Model Composition

arXiv:2610.05642v1 Announce Type: cross Abstract: Emerging mission classes such as on-orbit servicing, satellite inspection, and active debris removal require trajectory design methods that are adapta

Sparse Calibration-Based Personalization of Kernel-Based Gait Phase and Speed Estimation Using Wearable IMUs

arXiv:2610.03931v1 Announce Type: new Abstract: This paper presents sparse calibration-based personalization for kernel-based gait phase and walking speed co-estimation from wearable inertial measurem

researcharxiv-cs-ro

Sparse-GS2Mesh: 3D Gaussian Splatting Guided by Novel Stereo Views and 2DGS for Sparse View Surface Reconstruction}

arXiv:2610.04203v1 Announce Type: new Abstract: Surface reconstruction under sparse-view settings remains challenging due to limited geometric cues. Volume rendering methods based on signed distance f