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SelectTTRL: Representation-Guided Data Selection for Efficient Test-Time Reinforcement Learning

arXiv:2610.16104v1 Announce Type: new Abstract: Test-Time Reinforcement Learning (TTRL) enables large language models to adapt directly on unlabeled test problems by constructing pseudo-labels from mu

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Self-Creating Random Walks for Decentralized Learning under Pac-Man Attacks

DGX agent

arXiv:2601.07674v3 Announce Type: replace-cross Abstract: Random walk (RW)-based algorithms have long been popular in distributed systems due to low overheads and scalability, with recent growing appl

model-releasesarxiv-cs-lg

14 Oct 2026

Self-improving data synthesis can scale embedding models across domains and tasks

arXiv:2610.14730v1 Announce Type: cross Abstract: Adapting embedding models to new tasks requires training data that reflect task-specific information needs and relevance criteria. However, manual dat

Self-Supervised Learning of Semantically Consistent Visual Features via a Simple 3D Prior

arXiv:2610.16988v1 Announce Type: new Abstract: Visual features extracted from vision models like DINOv2 or Stable Diffusion enable high quality semantic correspondence between images. However, such f

tutorialsarxiv-cs-cv

Self-Verifier Capture in Unified Visual Generators: Diagnosing and Repairing Closed-Loop Self-Reward

arXiv:2610.16500v1 Announce Type: new Abstract: Image-space reward requires decoding and a separate judge; unified visual generators can instead reuse their understanding pathway for decode-free, self

Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

arXiv:1307.2965v3 Announce Type: replace Abstract: The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilag

Semantic Edge-Cloud Communication for Real-Time Urban Traffic Digital Twins

arXiv:2610.14187v1 Announce Type: cross Abstract: Urban traffic systems are becoming increasingly complex, demanding real-time traffic perception, proactive collision prediction, and intelligent manag

safetyarxiv-cs-cv

Semantic Saliency from Multi-Modal Large Language Model Scene Understanding Maps

arXiv:2610.15467v1 Announce Type: new Abstract: Image-computable low-level image saliency has shaped perceptual psychology and computer science, but is limited by its inability to capture human high-l

Semantically Steerable Image Tokenization

arXiv:2610.15515v1 Announce Type: new Abstract: Adaptive image tokenizers compress an image into a variable number of tokens matched to its complexity, and recent work makes this allocation controllab

SemTree: Judge-Aware Semantic Decision Trees for Inspectable and Repairable Rule Decisions

arXiv:2610.13999v1 Announce Type: new Abstract: Rule decisions such as content moderation, advertising compliance, and eligibility checks combine several conditions, exemptions, and missing-informatio

SenseWeather: Multimodal Sensing for Robust Robotic Perception under Extreme Conditions

arXiv:2610.14082v1 Announce Type: cross Abstract: Robotic perception must remain reliable when extreme environmental conditions severely degrade the primary RGB observation. Although robotic platforms

Sensing via whole-body interactions with complex terrains: physical insights from robotic models

arXiv:2610.15334v1 Announce Type: cross Abstract: In nature, animals interact with complex terrains and often generate forces, deformations, and vibrations across the body and environment. Such intera

tutorialsarxiv-cs-ro

Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models

arXiv:2610.15540v1 Announce Type: new Abstract: Deep-learning models increasingly rival numerical simulation in Earth-system forecasting, yet forecast error alone cannot determine whether their learne

Sensitivity or Criterion? Measuring and Moving When Language Models Act

arXiv:2610.15415v1 Announce Type: cross Abstract: Before an agent acts, it must decide whether action is warranted. A single accuracy cannot distinguish failure to recognize a warranted action from re

Sensor-Aware Joint Data Fusion and State Estimation under Sparse Multi-rate Distribution Grid Sensing

arXiv:2610.15156v1 Announce Type: new Abstract: Distribution-system state estimation is a growing necessity for the modern power grid as the addition of local storage and generation add system complex

Separating Prompt Harmfulness from Interaction Safety via Partial Generation

arXiv:2610.16761v1 Announce Type: cross Abstract: LLM-based information systems need safety guardrails. Most guardrails classify the prompt alone. If the prompt looks harmful, the system rejects it be

Sequence Composition in EHR Foundation Model Pretraining

arXiv:2610.16878v1 Announce Type: new Abstract: Autoregressive electronic health record (EHR) foundation models predict the next event token in patient trajectories: sequences of structured medical ev

Sequence-Structured Decision Learning for Branch-and-Bound in Mixed-Integer Linear Programming

arXiv:2610.14835v1 Announce Type: new Abstract: Branch-and-bound (BnB) solves mixed-integer linear programs by constructing and exploring a search tree. Node selection and branching are two recurrent

Sequent-Prover: Training Agents for Formal, Checkable SMT-based Reasoning

arXiv:2610.15258v1 Announce Type: cross Abstract: We introduce Sequent-Prover, a family of verifier-using language-model agents, and the post-training framework that produces them. Agents reason in an

safetyarxiv-cs-lg

SERF: Spatiotemporal Environment and Robot Feature Map for Long-Horizon Mobile Manipulation

model-releasesarxiv-cs-ro

arXiv:2606.12956v2 Announce Type: replace Abstract: Long-horizon robot mobile manipulation requires continual reasoning about localization, environment changes, and task progress, all of which are cha

SERQ: Saliency-Aware Low-Rank Error Reconstruction for LLM Quantization

arXiv:2603.08185v2 Announce Type: replace Abstract: Post-training quantization (PTQ) has emerged as a prevailing technique for deploying large language models (LLMs) efficiently in terms of both memor

Shaping Latent Mixture-of-Experts: From Expert Geometry to Layerwise Capacity Allocation

arXiv:2610.14644v1 Announce Type: cross Abstract: Fine-grained mixture-of-experts (MoE) models commonly increase expert count by narrowing each expert, treating expert shape as a by-product of routing

Shared-Control Teleoperation of Omnidirectional Aerial Robots for Continuous Valve Rotation

arXiv:2610.14478v1 Announce Type: new Abstract: Omnidirectional aerial robots offer full 6-DoF independent control over position and orientation, making them popular for aerial manipulation. Although

researcharxiv-cs-ro

SharedSAE: a unified sparse dictionary across u-net blocks and denoising time for diffusion models

arXiv:2610.13997v1 Announce Type: new Abstract: Concept representations in diffusion models drift across network depth and denoising time, yet existing sparse autoencoders offer only per-block or per-

Sharper Frames, Worse Rooms: Viewpoint Coverage Dominates Image Quality in Feed-Forward 3D Reconstruction

arXiv:2610.15854v1 Announce Type: new Abstract: Grounded spatial reasoning about indoor scenes rests on a geometric reconstruction that the multimodal system does not itself produce. Feed-forward mult

SHELDON: Reasoning through Dual Search over Hypothesis and Observation Spaces

arXiv:2610.18025v1 Announce Type: cross Abstract: Test-time scaling has enabled LLMs to solve increasingly complex reasoning, discovery, and optimization tasks. However, existing approaches often gath

tutorialsarxiv-cs-lg

ShellElastic: Scalable Elastic Pretraining with Simultaneous Maximal Updates and Hyperparameter Transfer

arXiv:2610.15124v1 Announce Type: new Abstract: Foundation models need to support diverse inference budgets, yet training a separate model for each budget duplicates pretraining effort. Elastic models

ShoeFit: A New Dataset and Dual-image-stream DiT Framework for Virtual Footwear Try-On

arXiv:2610.18325v1 Announce Type: new Abstract: Virtual footwear try-on (VFTON), a critical yet underexplored area in virtual try-on (VTON), aims to synthesize faithful try-on results given diverse fo

SHOES: Sim-to-real Hardware-adaptation via On-policy Expansion of Skills for Legged Robots

arXiv:2610.16457v1 Announce Type: new Abstract: Humans expand their locomotion capability by wearing appropriate footwear, such as hiking boots for mountains or skates for ice. Footwear changes the gr

safetyarxiv-cs-ro

Showing versus Doing: Teaching by Demonstration

arXiv:2610.14004v1 Announce Type: new Abstract: People often learn from others' demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contrast

tutorialsarxiv-cs-lg

Agents

SHRUMS: Sensor Hallucination for Real-time Underwater Motion Planning with a Compact 3D Sonar

arXiv:2510.18996v2 Announce Type: replace Abstract: Autonomous navigation in 3D is a fundamental problem for autonomy. Despite major advancements in terrestrial and aerial settings due to improved ran

agentsarxiv-cs-ro

Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals

arXiv:2610.15351v1 Announce Type: new Abstract: Amyotrophic lateral sclerosis (ALS) diagnosis remains challenging due to heterogeneous symptoms and reliance on clinical assessment, underscoring the ne

tutorialsarxiv-cs-lg

SidewalkBench: Benchmarking Visual Navigation on Urban Sidewalks

model-releasesarxiv-cs-ro

arXiv:2606.16953v2 Announce Type: replace Abstract: Urban sidewalk navigation presents significant challenges due to complex structural layouts, dynamic pedestrian behaviors, and long distances. While

SIGN-MAPPO: State-Intent-Aware Graph Multi-Agent Reinforcement Learning for Cooperative Driving under Imperfect V2X

arXiv:2610.15577v1 Announce Type: new Abstract: Cooperative driving at mixed-traffic unsignalized intersections is challenged by imperfect vehicle-to-everything (V2X) communication, human-driven vehic

safetyarxiv-cs-ro

Signature-Kernel One-Class Learning with Manifold Regularisation for Multivariate Time Series Anomaly Detection

arXiv:2507.23449v3 Announce Type: replace Abstract: One-class learning is an effective methodology for time series anomaly detection, yet kernel one-class approaches often compare sequences in a point

Silence and Noise: Resolving Suppression and Overvaluation in Multimodal Entity Alignment

arXiv:2610.16106v1 Announce Type: new Abstract: Multimodal entity alignment (MMEA) is fundamental to integrating heterogeneous knowledge graphs (KGs), yet existing paradigms remain critically vulnerab

safetyarxiv-cs-lg

Sim4RAD: A MATLAB Toolkit for Design-Phase Sizing, Actuator Selection and URDF-Model Verification of Serial Robotic Arms

arXiv:2610.17534v1 Announce Type: new Abstract: Actuator sizing is an early and consequential decision: joint torque and velocity specifications fix the motors, gearboxes and brakes before any CAD mod

researcharxiv-cs-ro

Agents

Simple Baselines are Competitive with Code Evolution

arXiv:2602.16805v2 Announce Type: replace-cross Abstract: Code evolution is a family of techniques that rely on large language models to search through possible computer programs by evolving existing

agentsarxiv-cs-lg

Simple Linear Corrections for Compression in Late Interaction

arXiv:2610.14196v1 Announce Type: cross Abstract: Late-interaction (multi-vector) models represent documents using a set of token-level vectors, providing a middle ground between cross-encoders and de

Simpler and Stronger Baselines for All-in-One Image Restoration

arXiv:2512.10581v2 Announce Type: replace Abstract: Current methods for all-in-one image restoration increasingly rely on complex mechanisms, such as Mixture-of-Experts or prompt learning, to handle d

safetyarxiv-cs-cv

Local Ai

Simplifying and Improving On-Policy Distillation for Few-Step Diffusion

arXiv:2610.18472v1 Announce Type: new Abstract: Few-step distillation reduces the sampling cost of diffusion and flow models, but existing approaches often require auxiliary score models or specialize

local-aiarxiv-cs-cv

SIRNET: Causal Adaptation of Pretrained Embeddings for Semiparametric Treatment-Effect Estimation Under Surrogacy

arXiv:2610.14011v1 Announce Type: new Abstract: Modern experiments increasingly produce rich post-treatment objects---such as text, images, and video---that may provide early surrogate signals for out

Applications

Skill-Aware Diffusion for Generalizable Robotic Manipulation

arXiv:2601.11266v2 Announce Type: replace Abstract: Robust generalization in robotic manipulation is crucial for robots to adapt flexibly to diverse environments. Existing methods usually improve gene

applicationsarxiv-cs-ro

SkillScaffold: Scaffolding Agentic Reinforcement Learning with Policy-Adaptive Skill Credit Assignment and Selective Injection

arXiv:2610.18807v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) enables large language models (LLMs) to learn from multi-turn interactions with verifiable outcomes. Textual skills

safetyarxiv-cs-lg

Skin Tone in the Wild: A Dataset and Benchmark for 10-tone MST Classification

arXiv:2603.02475v2 Announce Type: replace Abstract: Skin tone classification is a fundamental task in computer vision, with applications in quantifying medical features, assessing demographic represen

SkipWorld: Latent Skip Planning with Distilled World Models for Efficient Visual Navigation

arXiv:2610.18135v1 Announce Type: new Abstract: World-model-based visual navigation enables action planning by predicting future states under candidate trajectories. However, long-horizon rollout rema

tutorialsarxiv-cs-ro

Agents

SLDBench v2: Active Scaling Law Discovery

arXiv:2610.16297v1 Announce Type: new Abstract: Scaling laws predict large-model performance from small experiments, but finding a reliable law itself requires costly experiments. SLDBench tests wheth

agentsarxiv-cs-lg

SLEM: Spectrum-Aware Latent Expert Merging

arXiv:2610.16484v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models scale model capacity efficiently by activating only a small subset of experts for each input, while still requiring all