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arXiv:2606.00011v1 Announce Type: cross Abstract: Despite the promise of AI to assist complex decisions, practitioners still lack ways to detect likely failures and inspect the consequences of model e

local-aiarxiv-cs-ai

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RynnVLA-002: A Unified Vision-Language-Action and World Model

DGX agent

arXiv:2511.17502v3 Announce Type: replace Abstract: We introduce RynnVLA-002, a unified Vision-Language-Action (VLA) and world model. The world model leverages action and visual inputs to predict futu

model-releasesarxiv-cs-ro

2 Jun 2026

Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers

arXiv:2606.00902v1 Announce Type: new Abstract: General-purpose VLMs remain unreliable for biomedical research because valid answers in scientific papers depend on evidence split across figures, table

S-SPPO: Semantic-Calibrated Self-Play Preference Optimization

arXiv:2606.01561v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human preferences is often formulated via Direct Preference Optimization (DPO). However, the standard Bradley

S2M-Trek: From Single to Multi-Sphere Transport via Per-Frame Deep Sets on a Wheel-Legged Robot

arXiv:2606.01332v1 Announce Type: new Abstract: We study the problem of scaling dynamic loco-manipulation from a single free-rolling sphere to multiple spheres transported simultaneously on the back o

researcharxiv-cs-ro

S3TS: Stochastic Scenario-Structured Tree Search for Advanced Planning Under Uncertainty

arXiv:2606.02151v1 Announce Type: new Abstract: Effective scheduling in the energy sector is essential to ensure the reliable operation of electrical grids and their connected assets by, for instance,

researcharxiv-cs-ai

Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation

arXiv:2606.00808v1 Announce Type: new Abstract: Source-free graph domain adaptation (SF-GDA) aims to adapt source-trained graph models to unlabeled target graphs when source graphs are no longer acces

applicationsarxiv-cs-lg

Safe2Drive: Evaluating Safe Driving Behaviors of E2E Autonomous Driving Models

arXiv:2606.00191v1 Announce Type: cross Abstract: Recent end-to-end (E2E) autonomous driving policies achieve high driving scores in closed-loop simulations. Yet it remains unclear whether these polic

safetyarxiv-cs-cv

SafeGen-Bench: Benchmarking Safety in Image-Conditioned Text-to-Video Generation

arXiv:2606.01481v1 Announce Type: new Abstract: With the rapid advancements in text-to-image diffusion models, generative video models (T2V models) like Sora can now produce short synthetic videos fro

Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without Small (Sub)Gradients

arXiv:2512.02342v3 Announce Type: replace-cross Abstract: The stochastic Polyak step size (SPS) has proven to be a promising choice for stochastic gradient descent (SGD), delivering competitive perfor

SafeMCP: Proactive Power Regulation for LLM Agent Defense via Environment-Grounded Look-Ahead Reasoning

arXiv:2606.01991v1 Announce Type: new Abstract: As Large Language Model (LLM) agents increasingly leverage the Model Context Protocol (MCP) to operate in complex environments, the expansion of their a

SafeSteer: Localized On-Policy Distillation for Efficient Safety Alignment

arXiv:2606.02530v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human values often degrades their general capabilities, termed the alignment tax. Existing methods mitigate t

Safety Alignment of LMs via Non-cooperative Games

arXiv:2512.20806v3 Announce Type: replace Abstract: Ensuring the safety of language models (LMs) while maintaining their usefulness remains a critical challenge in AI alignment. Current approaches rel

Safety Game: Inference-Time Alignment of Black-Box LLMs via Constrained Optimization

arXiv:2510.09330v3 Announce Type: replace Abstract: Ensuring that large language models (LLMs) comply with safety requirements is a central challenge in AI deployment. Existing alignment approaches pr

safetyarxiv-cs-lg

Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning and Can Be Mitigated by Machine Unlearning

arXiv:2503.11832v5 Announce Type: replace Abstract: Recent vision language models (VLMs) have made remarkable strides in generative modeling with multimodal inputs, particularly text and images. Howev

SafeVLA-Bench: A Benchmark for the Success-Safety Gap in Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2606.00773v1 Announce Type: new Abstract: Vision-language-action (VLA) benchmarks measure whether a policy completes a requested manipulation task, but binary success can hide safety-relevant tr

Saliency-Aware Model Merging

arXiv:2606.00511v1 Announce Type: cross Abstract: Model merging aims to consolidate multiple task-specific models fine-tuned on different datasets into a unified architecture that performs cross-domai

SALSA: Speech Aware LLM Adaptation via Learned Steering Activation Vectors

arXiv:2606.00460v1 Announce Type: new Abstract: Speech-aware large language models often generalize poorly to out-of-domain settings. We propose SALSA (Speech-Aware LLM Adaptation via Learned Steering

researcharxiv-cs-cl

Same Payload, Different Channel: Measuring Trust Asymmetry in Tool-Using Language Models

model-releasesarxiv-cs-cl

arXiv:2606.00566v1 Announce Type: cross Abstract: As language models take on agentic roles that span calling external APIs, reading tool outputs, and acting on instructions embedded in third-party con

Sample Complexity and Decision-Theoretic Guarantees for Bayesian Model Averaging over Decision Trees with Catalan-Exponential Priors

arXiv:2606.01340v1 Announce Type: new Abstract: We ask: when do Bayesian model averaging (BMA) weights over decision trees carry sufficient epistemic information to justify committed exploitation of t

Sandboxed Coding Agents are Competitive Omni-modal Task Solvers

model-releasesarxiv-cs-cl

arXiv:2606.00579v1 Announce Type: new Abstract: As multimodal LLMs increasingly target video and audio, it is often assumed that such tasks require native omnimodal models. We show that this is not al

SARA: Stress Test Reasoning in Audio Deepfake Detection

arXiv:2601.03615v2 Announce Type: replace Abstract: Audio Language Models (ALMs) offer a promising shift towards explainable audio deepfake detections (ADD), moving beyond extit{black-box} classifiers

researcharxiv-cs-cl

SAVMap: Structure-Aided Visual Mapping of Large-Scale 2.5D Manhattan Wireframes from Panoramic Video

arXiv:2606.01939v1 Announce Type: new Abstract: Precise 3D representations of industrial environments enable tasks such as robot localization and digital twin generation. We propose SAVMap, a method f

applicationsarxiv-cs-cv

Scalable Counterfactual Risk Estimation for Rare Events in Longitudinal Data

arXiv:2606.01539v1 Announce Type: cross Abstract: Estimating the causal effect of time-varying treatments on survival outcomes in large observational studies is computationally demanding, particularly

Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach

arXiv:2503.24183v3 Announce Type: replace Abstract: The expansion of ride-sourcing services such as Uber and Lyft has reshaped urban transportation by offering flexible, on-demand mobility via mobile

safetyarxiv-cs-lg

Scalar-Measurement Attitude Estimation on mathbf{SO}(3) with Bias Compensation

arXiv:2603.02478v2 Announce Type: replace-cross Abstract: Attitude estimation methods typically rely on full vector measurements from inertial sensors such as accelerometers and magnetometers. This pa

safetyarxiv-cs-ro

Agents

Scaling Agentic Capabilities via Grounded Interaction Synthesis

arXiv:2606.02001v1 Announce Type: new Abstract: General agentic intelligence hinges on the ability to interact with diverse real-world tools to complete complex tasks, a capability fundamentally tied

agentsarxiv-cs-cl

Agents

Scaling Behavior of Single LLM-Driven Multi-Agent Systems

arXiv:2606.00655v1 Announce Type: cross Abstract: The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental quest

agentsarxiv-cs-ai

Scaling depth capacity via zero/one-layer model expansion

arXiv:2511.04981v2 Announce Type: replace Abstract: Model depth is a double-edged sword in deep learning: deeper models achieve higher accuracy but require higher computational cost. To efficiently tr

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders

arXiv:2606.00746v1 Announce Type: new Abstract: Vision foundation models are bottlenecked by the quadratic cost of self-attention, which limits usable resolution and increases the cost of large-scale

Scaling Pre-training to One Hundred Billion Data for Vision Language Models

arXiv:2502.07617v2 Announce Type: replace Abstract: We provide an empirical investigation of the potential of pre-training vision-language models on an unprecedented scale: 100 billion examples. We fi

researcharxiv-cs-cv

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments

arXiv:2602.23234v4 Announce Type: replace-cross Abstract: Large-scale commercial search systems optimize for relevance to drive successful sessions that help users find what they are looking for. To m

applicationsarxiv-cs-ai

SCAPO: Self-Supervised Category-Level Articulated Pose Estimation from a Single 3D Observation

arXiv:2606.01940v1 Announce Type: new Abstract: Existing methods for category-level object articulation from a single 3D observation often rely on dense supervision, multi-frame inputs, or CAD templat

safetyarxiv-cs-cv

ScaRF-SLAM: Scale-Consistent Reconstruction with Feed-Forward Models and Classical Visual SLAM

arXiv:2606.00307v1 Announce Type: new Abstract: Recent works have explored unifying SLAM with geometric foundation models (GFMs). However, directly using GFM predictions for tracking is highly sensiti

researcharxiv-cs-ro

SceneSmith: Agentic Generation of Simulation-Ready Indoor Scenes

arXiv:2602.09153v2 Announce Type: replace-cross Abstract: Simulation has become a key tool for training and evaluating home robots at scale, yet existing environments fail to capture the diversity and

Agents

SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents

arXiv:2602.12984v2 Announce Type: replace Abstract: Scientific reasoning inherently demands integrating sophisticated toolkits to navigate domain-specific knowledge. Yet, current benchmarks largely ov

agentsarxiv-cs-cl

Local Ai

scicode-lint: Detecting Methodology Bugs in Scientific Python Code with LLM-Generated Patterns

arXiv:2603.17893v2 Announce Type: replace-cross Abstract: Methodology bugs in scientific Python code produce plausible but incorrect results that traditional linters and static analysis tools cannot d

local-aiarxiv-cs-ai

Science Earth: Towards A Planet-Scale Operating System for AI-Native Scientific Discovery

arXiv:2606.01316v1 Announce Type: new Abstract: Scientific discovery demands intelligence, perseverance, and serendipity across vast search spaces. Today, top scientific capabilities remain siloed--on

Tutorials

SCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection

arXiv:2404.11326v5 Announce Type: replace Abstract: In recent years, change detection and anomaly detection models based on CNN and transformer have achieved remarkable success across various datasets

tutorialsarxiv-cs-cv

Score-Control for Hallucination Reduction in Diffusion Models

arXiv:2606.00377v1 Announce Type: new Abstract: Diffusion models have emerged as the backbone of modern generative AI, powering advances in vision, language, audio and other modalities. Despite their

Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning

arXiv:2307.05213v3 Announce Type: replace-cross Abstract: Many real-world optimization problems contain parameters that are unknown before deployment time, either due to stochasticity or to lack of in

applicationsarxiv-cs-ai

Score imes Decoder: A Unified View of Unsupervised Inference-Time Scaling for Hallucination Mitigation

arXiv:2606.00739v1 Announce Type: new Abstract: Large language models hallucinate even when the answer lies within their parameters. While inference-time scaling can surface this latent knowledge, the

SDR: Set-Distance Rewards for Radiology Report Generation

arXiv:2606.00440v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has rapidly advanced reasoning in vision--language models. However, for chest X-ray report generation, th

Search-on-Graph: Iterative Informed Navigation for Large Language Model Reasoning on Knowledge Graphs

arXiv:2510.08825v2 Announce Type: replace Abstract: Large language models (LLMs) augmented with knowledge graphs (KGs) offer a promising approach for knowledge-intensive reasoning. Central to this app

researcharxiv-cs-cl

SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search

arXiv:2606.01325v1 Announce Type: cross Abstract: Unmanned Aerial Vehicles (UAVs) equipped with radar sensors are deployed for target search missions in diverse environments, where targets exhibit cha

safetyarxiv-cs-lg

SeClaw: Spec-Driven Security Task Synthesis for Evaluating Autonomous Agents

arXiv:2606.02302v1 Announce Type: cross Abstract: Autonomous LLM agents increasingly operate in stateful environments where they access tools, files, memory, and external services. While such capabili

SECUREVENT: Hybrid AI/ML Security Monitoring for Distributed Event-Based Systems

arXiv:2606.01741v1 Announce Type: cross Abstract: Distributed event-based systems have become a common substrate for Internet-scale publish/subscribe services, IoT telemetry, cloud-native microservice

See, Plan, Rewind: Progress-Aware Vision-Language-Action Models for Robust Robotic Manipulation

arXiv:2603.09292v2 Announce Type: replace-cross Abstract: Measurement of task progress through explicit, actionable milestones is critical for robust robotic manipulation. This progress awareness enab