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SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data

model-releasesarxiv-cs-lg

arXiv:2605.05863v2 Announce Type: replace Abstract: Incorporating prior data into online reinforcement learning accelerates training but typically forces a difficult trade-off between high computation

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Spacetime Optimal-Transport Attention for Visuo-Haptic Imitation Learning of Contact-Rich Manipulation

DGX agent

arXiv:2605.20433v1 Announce Type: new Abstract: Contact-rich manipulation tasks such as tight-clearance insertion, connector mating, polishing, and surface-conforming wiping remain difficult for data-

safetyarxiv-cs-ro

21 May 2026

SPARC: Spatial-Aware Path Planning via Attentive Robot Communication

arXiv:2603.02845v3 Announce Type: replace Abstract: Efficient communication is critical for decentralized Multi-Robot Path Planning (MRPP), yet existing learned communication methods treat all neighbo

researcharxiv-cs-ro

Spatial Gram Alignment for Ultra-High-Resolution Image Synthesis

arXiv:2605.20808v1 Announce Type: new Abstract: Modern ultra-high-resolution image synthesis relies heavily on the robust generative capacity of large-scale pre-trained Latent Diffusion Models (LDMs).

SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents

model-releasesarxiv-cs-cl

arXiv:2605.21384v1 Announce Type: cross Abstract: As long-horizon coding agents produce more code than any developer can review, oversight collapses onto a single surface: the automated test suite. Re

Spectral bandits for smooth graph functions with applications in recommender systems

arXiv:2605.20552v1 Announce Type: cross Abstract: Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs

applicationsarxiv-cs-lg

Spectral Souping: A Unified Framework for Online Preference Alignment

arXiv:2605.20408v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) effectively aligns Large Language Models (LLMs) with aggregate human preferences but often fails to ad

Spectral Unforgetting: Post-Hoc Recovery of Damaged Capabilities Without Retraining

model-releasesarxiv-cs-lg

arXiv:2605.20296v1 Announce Type: new Abstract: Fine-tuning a language model for a target task routinely degrades capabilities the training data never explicitly threatened. We study this phenomenon,

SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining

arXiv:2605.21075v1 Announce Type: new Abstract: Earth observation (EO) foundation models (FMs) are increasingly trained on multisensor data, spanning multispectral imagery (MSI), synthetic aperture ra

researcharxiv-cs-cv

Local Ai

SpikeDet: Better Firing Patterns for Accurate and Energy-Efficient Object Detection with Spiking Neural Networks

arXiv:2501.15151v5 Announce Type: replace Abstract: Spiking Neural Networks (SNNs) are the third generation of neural networks. They have gained widespread attention in object detection due to their l

local-aiarxiv-cs-cv

Local Ai

SpineContextResUNet: A Computationally Efficient Residual UNet for Spine CT Segmentation

arXiv:2605.20760v1 Announce Type: new Abstract: Automated segmentation of the vertebral column in Computed Tomography (CT) scans is a prerequisite for pathological assessment and surgical planning. Ho

local-aiarxiv-cs-cv

SR-Ground: Image Quality Grounding for Super-Resolved Content

arXiv:2605.21244v1 Announce Type: new Abstract: Super-Resolution (SR) has advanced rapidly in recent years, with diffusion-based models achieving unprecedented fidelity at the cost of introducing new

researcharxiv-cs-cv

Stage-Audit: Auditable Source-Frontier Discovery for Cross-Wiki Tables

arXiv:2605.20478v1 Announce Type: new Abstract: LLM-curated tables can appear source-grounded while containing unsupported rows: the curator may recall entries from parametric memory and retroactively

safetyarxiv-cs-cl

STAR-IOD: Scale-decoupled Topology Alignment with Pseudo-label Refinement for Remote Sensing Incremental Object Detection

model-releasesarxiv-cs-cv

arXiv:2605.20738v1 Announce Type: new Abstract: Remote sensing imagery typically arrives in the form of continuous data streams. Traditional detectors often forget previously learned categories when l

Statistical Guarantees in the Search for Less Discriminatory Algorithms

arXiv:2512.23943v2 Announce Type: replace-cross Abstract: U.S. discrimination law can impose liability on firms that fail to adopt a less discriminatory alternative (LDA): a decision policy that achie

STEAM: A Training-Free Congestion-Aware Enhancement Framework for Decentralized Multi-Agent Path Finding

arXiv:2605.20929v1 Announce Type: new Abstract: We propose STEAM (Spatial, Temporal, and Emergent congestion Awareness for MAPF), a training-free test-time enhancement framework for learning-based dec

safetyarxiv-cs-ro

Agents

STELLAR: Scaling 3D Perception Large Models for Autonomous Driving

arXiv:2605.20390v1 Announce Type: new Abstract: Model scaling has demonstrated remarkable success through large-scale training on diverse datasets. It remains an open question whether the same paradig

agentsarxiv-cs-cv

Stimulus symmetries can confound representational similarity analyses

arXiv:2605.21324v1 Announce Type: cross Abstract: What can representational similarity matrices (RSMs) tell us about a neural code? As the popularity of these summary statistics grows, so too does the

STiTch: Semantic Transition and Transportation in Collaboration for Training-Free Zero-Shot Composed Image Retrieval

arXiv:2605.21261v1 Announce Type: new Abstract: Training-free zero-shot composed image retrieval models are recently gaining increasing research interest due to their generalizability and flexibility

STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction

arXiv:2508.12247v2 Announce Type: replace Abstract: Recently, spatio-temporal time-series prediction has developed rapidly, yet existing deep learning methods struggle with learning complex long-term

applicationsarxiv-cs-lg

Strategy-Induct: Task-Level Strategy Induction for Instruction Generation

arXiv:2605.20924v1 Announce Type: new Abstract: Designing effective task-level prompts is crucial for improving the performance of Large Language Models (LLMs). While prior work on instruction inducti

researcharxiv-cs-cl

Stream3D: Sequential Multi-View 3D Generation via Evidential Memory

arXiv:2605.21472v1 Announce Type: new Abstract: View-conditioned 3D generators such as SAM 3D, TRELLIS and Hunyuan3D produce high-quality object reconstructions from a single view, but real-world visu

applicationsarxiv-cs-cv

StreamGVE: Training-Free Video Editing via Few-Step Streaming Video Generation

arXiv:2605.21466v1 Announce Type: new Abstract: Although existing video editing methods are generally feasible, they often require many costly iterations and still struggle to deliver high-quality yet

researcharxiv-cs-cv

Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning

arXiv:2506.21039v3 Announce Type: replace Abstract: Long-horizon goal-conditioned tasks pose fundamental challenges for reinforcement learning (RL), particularly when goals are distant and rewards are

Agents

SubTGraph: Large-Scale Subterranean Environment Synthesis with Controllable Topological Variability for Robotic Autonomy Validation

arXiv:2605.20917v1 Announce Type: new Abstract: Subterranean (SubT) environments have been a frontier for autonomous robotics, driven by the push for automation of mining operations and the interest i

agentsarxiv-cs-ro

SUGAR: A Scalable Human-Video-Driven Generalizable Humanoid Loco-Manipulation Learning Framework

arXiv:2605.20373v1 Announce Type: cross Abstract: Building humanoid robots capable of generalizable whole-body loco-manipulation in the real world remains a fundamental challenge. Existing methods eit

Supervised Latent Restructuring for Small-Data Quantum Learning in Plant Phenomics

arXiv:2605.20413v1 Announce Type: new Abstract: High-dimensional biological data often exhibit a severe mismatch between feature dimensionality and sample size, making reliable classification difficul

SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front

arXiv:2605.20619v1 Announce Type: new Abstract: Scalarization is widely used in multi-objective optimization owing to its simplicity and scalability. In many applications, the goal is to generate solu

SurgOnAir: Hierarchy-Aware Real-Time Surgical Video Commentary

arXiv:2605.21132v1 Announce Type: new Abstract: Understanding surgical workflow in real time is fundamental for intelligent surgical embodiment, where AI systems continuously perceive and respond as s

Local Ai

Sustainability Is Not Linear: Quantifying Performance, Energy, and Privacy Trade-offs in On-Device Intelligence

arXiv:2603.26603v2 Announce Type: replace-cross Abstract: The migration of Large Language Models (LLMs) from cloud clusters to edge devices promises enhanced privacy and offline accessibility, but thi

local-aiarxiv-cs-lg

Sutra: Tensor-Op RNNs as a Compilation Target for Vector Symbolic Architectures

arXiv:2605.20919v1 Announce Type: new Abstract: Sutra is a typed, purely functional programming language whose compiled forward pass is a PyTorch neural network. The compiler beta-reduces the whole pr

SymbolicLight V1: Spike-Gated Dual-Path Language Modeling with High Activation Sparsity and Sub-Billion-Scale Pre-Training Evidence

model-releasesarxiv-cs-cl

arXiv:2605.21333v1 Announce Type: new Abstract: Natively trained spiking language models struggle to combine Transformer-like language quality, stable multi-domain pre-training, and high activation sp

Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labels

arXiv:2605.20347v1 Announce Type: new Abstract: Labeling a training set is often expensive and susceptible to errors, making the design of robust loss functions for label noise an important problem. T

SynCB: A Synergy Concept-Based Model with Dynamic Routing Between Concepts and Complementary Neural Branches

arXiv:2605.20908v1 Announce Type: new Abstract: Concept-based (CB) models provide interpretability and support test-time human intervention, while standard neural networks (NN) offer strong task perfo

Synchronization and Turn-Taking in Full-Duplex Speech Dialogue Models

arXiv:2605.20356v1 Announce Type: new Abstract: Full-duplex spoken dialogue models (SDMs) can listen and speak simultaneously, enabling interaction dynamics closer to human conversation than turn-base

safetyarxiv-cs-cl

TabPFN Extensions for Interpretable Geotechnical Modelling

model-releasesarxiv-cs-lg

arXiv:2603.21033v2 Announce Type: replace-cross Abstract: Geotechnical site characterisation relies on sparse, heterogeneous borehole data, where uncertainty quantification and interpretability matter

TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data

arXiv:2605.20234v1 Announce Type: new Abstract: Prior-Data Fitted networks (PFNs) have been very successful in tabular contexts, handling prediction tasks in context. However, they are designed for si

Task-conditioned probing of instruction-tuned multimodal LLMs: Region-specific brain alignment patterns under naturalistic stimuli

arXiv:2506.08277v3 Announce Type: replace-cross Abstract: Recent voxel-wise multimodal brain encoding studies have shown that multimodal large language models (MLLMs) exhibit a higher degree of brain

safetyarxiv-cs-cl

Tutorials

Task-Routed Mixture-of-Experts with Cognitive Appraisal for Implicit Sentiment Analysis

arXiv:2605.20916v1 Announce Type: new Abstract: Implicit sentiment analysis is challenging because sentiment toward an aspect is often inferred from events rather than expressed through explicit opini

tutorialsarxiv-cs-cl

TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design

model-releasesarxiv-cs-cv

arXiv:2605.20731v1 Announce Type: new Abstract: Text-to-image models produce graphic design at production scale, but their supervision comes from photo-style preference data with a single overall verd

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis

arXiv:2510.06063v2 Announce Type: replace-cross Abstract: Modern enterprises generate vast streams of time series metrics when monitoring complex systems, known as observability data. Unlike conventio

applicationsarxiv-cs-lg

TelePhysics: Physics-Grounded Multi-Object Scene Generation from a Single Image with Real-Time Interaction

arXiv:2605.20290v1 Announce Type: cross Abstract: Recent generative video models achieve impressive visual quality but remain constrained by limited physical consistency and controllability. Existing

TempGlitch: Evaluating Vision-Language Models for Temporal Glitch Detection in Gameplay Videos

model-releasesarxiv-cs-cv

arXiv:2605.21443v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly being explored for video game quality assurance, especially gameplay glitch detection. Most existing eval

Temporal Counterfactual Explanations of Behaviour Tree Decisions

arXiv:2509.07674v2 Announce Type: replace Abstract: Explainability, in particular, the ability for robots to explain why they have made a decision or behaved in a certain way, is a critical tool in he

researcharxiv-cs-ro

TERDNet: Transformer Encoder-Recurrent Decoder Network for Scene Change Detection

arXiv:2605.20822v1 Announce Type: new Abstract: In this work, we address the challenge of Scene Change Detection (SCD), where the goal is to identify variations between two images of the same location

applicationsarxiv-cs-cv

Terminal-World: Scaling Terminal-Agent Environments via Agent Skills

model-releasesarxiv-cs-cl

arXiv:2605.20876v1 Announce Type: new Abstract: Terminal agents extend Large Language Models with the ability to execute tasks directly in command-line environments, but their progress is bottlenecked

Terrestrial Soft Mobile Robots: A Review

arXiv:2605.20304v1 Announce Type: new Abstract: Soft mobile robots have emerged as a promising area of research with potential applications in various disciplines including but not limited to search-a

applicationsarxiv-cs-ro

Testing Support Size More Efficiently Than Learning Histograms

arXiv:2410.18915v4 Announce Type: replace-cross Abstract: Consider two problems about an unknown probability distribution p: 1. How many samples from p are required to test if p is supported on n elem