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RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis

arXiv:2606.28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning. However, evaluating these videos is chal

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Safety

RoboPIN: Grounded Embodied Reasoning via Pinned Chain-of-Thought

DGX agent

arXiv:2606.15753v2 Announce Type: replace Abstract: Embodied reasoning requires models to perceive task-relevant objects and spaces in physical environments and maintain consistent visual grounding th

safetyarxiv-cs-ai

30 Jun 2026

Robotic Arm-Based Spectral Sensing for Strawberry Positioning and Non-Destructive Sweetness Measurement

arXiv:2606.28555v1 Announce Type: new Abstract: Accurate assessment of sweetness is essential for quality control in agriculture, yet conventional methods rely on destructive sampling and are difficul

safetyarxiv-cs-ro

Hardware

Robust and Efficient Monocular 3D Gaussian SLAM for Kilometer-Scale Outdoor Scenes

arXiv:2606.30436v1 Announce Type: new Abstract: Scaling monocular 3D Gaussian Splatting (3DGS) SLAM to kilometer-level outdoor environments poses two tightly coupled challenges: fragile long-term pose

hardwarearxiv-cs-cv

Robust Extended Kalman Filter for Land Navigation Using Massive Array of MEMS IMUs

arXiv:2606.29271v1 Announce Type: cross Abstract: We propose a robust Extended Kalman Filter (EKF) architecture for land navigation using an array of hundreds of low-cost micro-electromechanical syste

safetyarxiv-cs-ro

Robust Strategic Classification under Decision-Dependent Cost Uncertainty

arXiv:2606.30136v1 Announce Type: new Abstract: Humans facing algorithmic decision systems have been found to ``game'' them by altering their input data (at a cost to them) in order to favorably chang

safetyarxiv-cs-lg

Tutorials

Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization

arXiv:2510.02308v2 Announce Type: replace Abstract: Estimating the tangent spaces of a data manifold is a fundamental problem in geometric data analysis. The standard approach, Local Principal Compone

tutorialsarxiv-cs-lg

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets

arXiv:2606.29837v1 Announce Type: new Abstract: Dataset distillation (DD) condenses large corpora into compact, information-rich subsets for efficient training and reuse. However, under noisy supervis

safetyarxiv-cs-cv

Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors

arXiv:2606.29428v1 Announce Type: new Abstract: Zero-shot anomaly detection aims to identify defects in arbitrary novel domains; however, existing models assume that the auxiliary data contains a rich

safetyarxiv-cs-cv

Robustness and Structure Preservation in Flow-Based Generative Models via Wasserstein Path-Space Divergences

arXiv:2410.01244v2 Announce Type: replace-cross Abstract: We introduce a novel Wasserstein-1 (W_1) path-space divergence for stochastic and deterministic dynamics and establish a Wasserstein Uncertain

safetyarxiv-cs-lg

Room Scene Discovery and Grouping in Unstructured Vacation Rental Image Collections

arXiv:2507.00263v2 Announce Type: replace Abstract: The rapid growth of vacation rental (VR) platforms has led to an increasing volume of property images, often uploaded without structured categorizat

RSGPNet: Geometric Prompting for Remote Sensing Open-Vocabulary Semantic Segmentation

arXiv:2606.28410v1 Announce Type: cross Abstract: Open-vocabulary semantic segmentation (OVSS) enables text-guided segmentation of unseen objects, breaking fixed-class limitations to achieve open-worl

S-Agent: Spatial Tool-Use Elicits Reasoning for Spatial Intelligence

arXiv:2606.20515v2 Announce Type: replace Abstract: Real-world spatial intelligence requires reasoning over a continuous and evolving 3D world, yet existing VLMs and tool-augmented agents largely rema

S-GAI: Spectral Geometry-Aware Initialization for Sigmoidal MLPs -- From Dataset Geometry to Network Weights

arXiv:2606.28444v1 Announce Type: cross Abstract: Classical universal approximation theorems establish the expressive power of sigmoidal multilayer perceptrons, but they do not prescribe how initial w

SA-Homo: Scale Adaptive Homography Estimation for Scale Variation Scenarios

arXiv:2606.30408v1 Announce Type: new Abstract: Homography estimation, as one of the fundamental problems in computer vision, remains challenged by scale variation scenarios where image pairs potentia

SA-VIS: Sparse frame Annotations for training Video Instance Segmentation

arXiv:2606.20140v2 Announce Type: replace Abstract: Recent online video instance segmentation (VIS) methods have achieved impressive results, thus becoming the preferred approach to segment instances

SA-VLA: State-aware tokenizer for improving Vision-Language-Action Models' performance

arXiv:2606.30113v1 Announce Type: cross Abstract: Discrete action tokenization provides a compact interface for autoregressive VLA policies, but accurately recovering continuous robot actions from dis

safetyarxiv-cs-ai

SABER-Math: Automated Benchmark for Information Retrieval Evaluation in Mathematics

arXiv:2606.29894v1 Announce Type: cross Abstract: As agentic AI systems tackle more complex mathematical tasks, they increasingly rely on information retrieval (IR) to search problem databases, theore

SAD-GS: Learning Reliable 3D Semantic Gaussian Fields via Dynamic Geo-Semantic Anchoring

arXiv:2606.29376v1 Announce Type: new Abstract: Open-vocabulary 3D semantic Gaussian field learning relies on multi-view 2D supervision, whose semantic targets and spatial assignments are often unreli

SADL: What to Ignore? A Benchmark for Subject-Aware Distractor Localization

arXiv:2606.30393v1 Announce Type: new Abstract: Photographs frequently contain visual distractors besides foregrounds and backgrounds of the intended subject, competing for attention and weakening com

SAFE-DiT: Semantics-Aware Fast-path Execution for High-Resolution Diffusion Transformers

arXiv:2606.29360v1 Announce Type: new Abstract: High-resolution Diffusion Transformer (DiT) inference contains substantial spatial redundancy, but many spatially adaptive implementations encode region

safetyarxiv-cs-cv

Agents

SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents

arXiv:2606.28356v1 Announce Type: cross Abstract: Generative Engine Optimization (GEO) lets content owners rewrite web content to increase their visibility in generative systems. In recommendation age

agentsarxiv-cs-ai

SafePyramid: A Hierarchical Benchmark for In-context Policy Guardrailing

arXiv:2606.29887v1 Announce Type: new Abstract: In real-world applications, guardrails are often expected to identify unsafe user-model interactions according to application-specific safety policies,

Safety from Honesty in a Disinterested AI Predictor

arXiv:2606.29657v1 Announce Type: new Abstract: As AI systems become more capable, training procedures that optimize for downstream outcomes risk introducing implicit agency: goal-directed behavior th

safetyarxiv-cs-ai

Agents

SAGA: Scene-Aware, Goal-Evolving Agents for Long-Horizon CivRealm Strategy Planning

arXiv:2606.29932v1 Announce Type: new Abstract: Long-horizon strategic planning in complex strategy games demands concurrent reasoning across multiple decision domains under imperfect information and

agentsarxiv-cs-ai

SAKE: Software Architectural Knowledge Evaluation Benchmark for Large Language Models

arXiv:2606.29520v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used as assistants across the software development lifecycle, yet their ability to reason about software

Same Concept, Different Directions: Cross-Modal Feature Heterogeneity in Sparse Autoencoders

arXiv:2606.29888v1 Announce Type: cross Abstract: Vision-language models map images and text into a joint embedding space. However, these embeddings often entangle multiple semantic features, which li

Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Trees

arXiv:2606.29331v1 Announce Type: new Abstract: Scientific discovery via symbolic regression is often viewed as statistically and computationally intractable because the hypothesis space of expression

researcharxiv-cs-lg

Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees

arXiv:2606.29681v1 Announce Type: new Abstract: Probabilistic model checking for Markov decision processes (MDPs) provides quantitative guarantees, but often offers limited insight into why undesired

SARLO-80: Worldwide Slant SAR Language Optic Dataset 80cm

arXiv:2606.20523v2 Announce Type: replace-cross Abstract: Multimodal foundation models have advanced rapidly thanks to large optical benchmarks, but comparable resources for synthetic aperture radar (

safetyarxiv-cs-ai

SAT-RTS: A systematic framework for tactical knowledge extraction and visualization-based analysis in real-time strategy games

arXiv:2606.30090v1 Announce Type: new Abstract: Efficient tactical knowledge extraction and analysis in real-time strategy (RTS) games micromanagement are constrained by the high-dimensional coupled s

Applications

SATB-VR: Training Few-Step Video Restoration Diffusion Model using SNR-Aware Trajectory Blending

arXiv:2606.28677v1 Announce Type: new Abstract: While diffusion models excel in video restoration, their reliance on extensive iterative steps limits efficiency. Conversely, aggressive single-step dis

applicationsarxiv-cs-cv

SatSplat: Geometrically-Accurate Gaussian Splatting for Satellite Imagery

arXiv:2606.28581v1 Announce Type: new Abstract: High-resolution satellite imagery demands 3D reconstruction methods that deliver both speed and geometric accuracy. Recent adaptations of 3D Gaussian Sp

Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs

arXiv:2511.10480v3 Announce Type: replace-cross Abstract: Optimizing the performance of large language models (LLMs) on large-scale AI training and inference systems requires a scalable and expressive

Scalar Representations of Neural Network Training Dynamics

model-releasesarxiv-cs-lg

arXiv:2606.30384v1 Announce Type: new Abstract: Training in artificial neural networks can be viewed as a trajectory evolving through a high-dimensional loss landscape. However, the large number of tr

ScAle: Attention Head Scaling as a Minimal Adapter for Spatial Reasoning in Vision Language Models

arXiv:2606.29579v1 Announce Type: cross Abstract: Spatial reasoning remains a persistent challenge for many vision language models (VLMs), and improving it typically requires fine-tuning with substant

ScaleAware-JEPA: Latent Representation for Discovery in Multiscale Physical Fields

arXiv:2606.29723v1 Announce Type: cross Abstract: Continuous physical fields represent a large fraction of data under scientific investigation. Their multiscale structures are central to discovery, ye

Tutorials

ScaleErasure: Inference-Time Minimal Intervention for Precise Concept Erasure in Next-Scale Autoregressive Image Generation

arXiv:2606.29282v1 Announce Type: new Abstract: Concept erasure aims to prevent image generative models from producing unsafe content while preserving their general generative capability. Meanwhile, n

tutorialsarxiv-cs-cv

Scaling Textual Gradients via Sampling-Based Momentum

arXiv:2506.00400v4 Announce Type: replace-cross Abstract: LLM-based prompt optimization, which uses LLM-provided ``textual gradients'' (feedback) to refine prompts, has emerged as an effective method

Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent

model-releasesarxiv-cs-cl

arXiv:2606.30616v1 Announce Type: new Abstract: We introduce Agents-A1, a 35B Mixture-of-Experts Agentic Model that reaches trillion-parameter-level performance by scaling the agent horizon. We invest

SCARCE: Scalable Cascade Analysis for Rare-event Characterisation via Embeddings

arXiv:2606.29623v1 Announce Type: new Abstract: Rare events govern the safety profile of modern AI systems, yet their probabilities are extremely difficult to estimate: direct Monte Carlo requires pro

Agents

Scene-aware Prediction of Diverse Human Movement Goals

arXiv:2606.29942v1 Announce Type: new Abstract: Anticipation of human behaviours facilitates autonomous systems in proactive planning. Human behaviour could be stochastic due to varying goals. Human g

agentsarxiv-cs-cv

Local Ai

Scenes as Objects, Not Primitives: Instance-Structured 3D Tokenization from Unposed Views

arXiv:2606.29513v1 Announce Type: new Abstract: A 3D scene is understood through its objects, not the primitives that compose them. Yet feed-forward reconstruction methods output dense, unstructured s

local-aiarxiv-cs-cv

Applications

Schema-First Retrieval: Embedding Catalogs for Natural Language Analytics

arXiv:2606.28387v1 Announce Type: cross Abstract: Enterprise text-to-SQL systems often fail before SQL is generated: the model receives the wrong schema context. Modern warehouses contain thousands of

applicationsarxiv-cs-ai

Applications

SciFlow: Semantic Cross Interference for Self-Supervised Optical Flow Domain Generalization

arXiv:2606.29004v1 Announce Type: new Abstract: Motions of objects and scenes carry essential intelligence in video understanding, offering rich cues for interpreting dynamic settings and interactions

applicationsarxiv-cs-cv

SciIR: A Large-scale Training Dataset and Benchmark for Scientific Image Reasoning Generation

arXiv:2606.30124v1 Announce Type: new Abstract: While Text-to-Image (T2I) models have shown remarkable success in generating photorealistic visual content, they still struggle with the rigorous semant

SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents

arXiv:2603.29139v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have enabled agentic systems to translate natural-language intent into executable scientific visuali

Tutorials

scKDGM: KAN-guided Dynamic Graph Masked Learning for Single-Cell RNA-seq Clustering

arXiv:2606.28459v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) clustering is essential for identifying cell types, but high dimensionality, sparsity, dropout, and technical noi

tutorialsarxiv-cs-lg