Search: “safety”

Agentic Artificial Intelligence for Multistage Physics Experiments at a Large-Scale User Facility Particle Accelerator

arXiv:2509.17255v2 Announce Type: replace-cross Abstract: We present the first language-model-driven agentic artificial intelligence (AI) system to autonomously execute multi-stage physics experiments

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Safety

Best-of-Better-N: Generating Pre-Aligned Responses with In-Context Learning

DGX agent

arXiv:2607.03453v1 Announce Type: cross Abstract: Inference-time alignment methods, such as Best-of-N, offer a flexible alternative to training-based alignment by using reward models to select high-qu

safetyarxiv-cs-ai

7 Jul 2026

BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations

arXiv:2603.06576v2 Announce Type: replace-cross Abstract: The integration of Large Language Models (LLMs) into autonomous driving has attracted growing interest for their strong reasoning and semantic

CAGE-1: Control, Assurance, and Governance Evaluation for Enterprise Agentic AI

arXiv:2607.03510v1 Announce Type: cross Abstract: Enterprise artificial intelligence is moving from experimentation into operational workflows. Early programs focused on model access and retrieval-aug

CN-CBF: Composite Neural Control Barrier Function for Robot Navigation in Dynamic Environments

arXiv:2603.06921v2 Announce Type: replace-cross Abstract: Safe navigation of autonomous robots remains one of the core challenges in the field, especially in dynamic and uncertain environments. One pr

Model Releases

Continuous-Time Gaussian Belief Trees for Motion Planning

model-releasesarxiv-cs-ro

arXiv:2607.02884v1 Announce Type: new Abstract: We address sampling-based motion planning for continuous-time stochastic systems under process and measurement uncertainty, with probabilistic guarantee

CONTRA: Red-Teaming Configurations of Personalizable Agents

arXiv:2607.03220v1 Announce Type: cross Abstract: Recent tools such as OpenClaw have extended the capabilities of LLM-based agents from simple dialog-based systems to fully autonomous agents. These sy

CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recovery

arXiv:2607.03177v1 Announce Type: cross Abstract: Traditional reinforcement learning (RL) for recovery in autonomous systems lacks causal understanding and generalizes poorly to novel failure scenario

Defending from GeoLocalization through Adversarial Road Trips

arXiv:2607.03277v1 Announce Type: new Abstract: Retrieval-based image geolocalization has emerged as a powerful technique for determining the location of a query image by matching it against a large,

Designing Touch for Trauma-Informed Social Robots: A Design Space for Direct and Indirect Actuation

arXiv:2607.04981v1 Announce Type: new Abstract: Touch is a fundamental communication modality in human-robot interaction and may support grounding, emotional regulation, and stress reduction in therap

Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG

arXiv:2607.02966v1 Announce Type: new Abstract: Cross-lingual retrieval-augmented generation (RAG) is often deployed in an English-evidence regime, where users query in diverse languages but retrieved

Faithfulness to Refusal: A Causal Audit of Neuron Selectors

arXiv:2607.05355v1 Announce Type: new Abstract: Attribution scores increasingly identify which neuron rows of a language model matter for applications such as pruning, interpretability, and editing fo

FLOAT Drone for Physical Interaction: Lateral Airflow Reduction, Wrench Modeling, and Adaptive Control

arXiv:2607.04260v1 Announce Type: new Abstract: Aerial physical interaction represents a promising direction for next-generation unmanned aerial vehicles (UAVs), but it requires an aerial platform tha

How Utilitarian Are OpenAI's Models Really? Replicating and Reinterpreting Pfeffer, Krugel, and Uhl (2025)

arXiv:2603.22730v2 Announce Type: replace Abstract: Pfeffer, Krugel, and Uhl (2025) report that OpenAI's reasoning model o1-mini produces more utilitarian responses to the trolley problem and footbrid

Integrated Graph Search and Model Predictive Control for Smooth and Efficient Path Planning in Autonomous Vehicles

arXiv:2607.04259v1 Announce Type: new Abstract: Path planning is a fundamental component of autonomous vehicles, where achieving safe, comfortable, and dynamically feasible paths while ensuring comput

NeSy-CSA: A Neuro-Symbolic Framework for Open-Ended Critical Scenario Attribution

arXiv:2607.03847v1 Announce Type: new Abstract: Understanding why discovered scenarios become critical in scenario-based testing is essential for effectively leveraging them in decision-making systems

Open-Attribute Person Retrieval: Finding People Through Distinctive and Novel Attributes

arXiv:2508.01389v3 Announce Type: replace Abstract: Person retrieval in surveillance videos often depends on attributes described by witnesses or operators. However, the most useful cues in practice a

Position: Use Sparse Autoencoders to Discover Unknowns

arXiv:2506.23845v2 Announce Type: replace-cross Abstract: While sparse autoencoders (SAEs) have generated significant excitement, a series of negative results have added to skepticism about their usef

Road-Aware Anomaly Segmentation with Query-Guided Polygons and CLIP in Autonomous Driving

arXiv:2607.04304v1 Announce Type: new Abstract: Traditional semantic segmentation models operate under a closed-set assumption and struggle to recognize unknown or unexpected objects-an essential capa

Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions

arXiv:2602.07341v2 Announce Type: replace Abstract: This paper focuses on the scalable robot learning for manipulation in the dexterous robot arm-hand systems, where the remote human-robot interaction

Self-Reference in Large Language Models: The Introspection Threshold for Recursive Self-Improvement

arXiv:2607.04277v1 Announce Type: cross Abstract: The pursuit of self-evolving AI raises a critical question: when is autonomous self-improvement sustainable rather than degenerative? Drawing an analo

Short-Horizon Position Accuracy of Single-Track Models: Implications for Motion Planning of Autonomous Vehicles

arXiv:2606.14216v2 Announce Type: replace Abstract: Accurate and computationally efficient vehicle models are essential for motion planning of autonomous vehicles, where positional accuracy directly a

Silicon Sampling via Cross-Survey Transfer

arXiv:2607.03091v1 Announce Type: new Abstract: Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promising approach for augmenting traditional

Training Verifiably Robust Agents Using Set-Based Reinforcement Learning

arXiv:2408.09112v2 Announce Type: replace Abstract: Reinforcement learning policies parametrized by deep neural networks have achieved strong performance for continuous control, yet even small input p

Adaptive Companionship for Group-Following Robots: Handling Dynamically Changing Group Formations

arXiv:2607.01287v1 Announce Type: cross Abstract: Accompanying a group of humans is an essential aspect of developing human-like social cognition in robots. However, human groups typically do not foll

Copewell: A Multi-Agent Swarm Architecture for Equitable Mental Wellness Support

arXiv:2607.02245v1 Announce Type: new Abstract: Mental health disorders affect nearly one billion people globally, yet 75% of individuals in low- and middle-income countries receive no treatment due t

DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving

arXiv:2603.18315v2 Announce Type: replace-cross Abstract: Traditional reinforcement learning (RL) methods rely on manually engineered rewards or sparse collision signals, which fail to capture the ric

Epistemic Goggles: A Pretrained Module that Induces an Epistemic Frame via Gradient Editing

arXiv:2607.01690v1 Announce Type: new Abstract: Finetuning a language model on documents that are explicitly annotated as fictional results in a model that still actually believes the documents' core

ESC: Emotional Self-Correction for Reliable Vision-Language Models

arXiv:2607.02089v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved strong performance across diverse multimodal tasks, yet they remain vulnerable to unreliable reasoning. Ex

Fast and Accurate Anomaly Detection in Time Series

arXiv:2607.02046v1 Announce Type: new Abstract: Anomaly detection is a critical and evolving field in Machine Learning, with applications targeting different domains such as cybersecurity, finance, he

kNNGuard: Turning LLM Hidden Activations into a Training-Free Configurable Guardrail

arXiv:2607.02072v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in domains requiring guardrails to detect unsafe, off-topic, or adversarial prompts. Existing g

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning

arXiv:2607.01651v1 Announce Type: new Abstract: Learning effective robot control policies on physical hardware is challenging due to costly data collection and the difficulty of reward specification.

Overthink-Triggered Slowdown Attacks on LVLM-Based Robotic Systems

arXiv:2607.01518v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have been increasingly integrated into robotic systems. However, these models may exhibit overthinking behaviors,

AI Native Games: A Survey and Roadmap

arXiv:2607.00527v1 Announce Type: new Abstract: Generative AI now enables games to produce dialogue, quests, characters, images, and worlds at runtime. Yet generation alone does not make a game AI-nat

ECoSim: Data Efficient Fine-Tuning for Controllable Traffic Simulation

arXiv:2607.00545v1 Announce Type: new Abstract: Controllable traffic simulation is critical for testing autonomous driving systems, yet existing approaches often require retraining large generative mo

Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows

arXiv:2607.00828v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows. Although recent advances hav

From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape

arXiv:2606.08625v2 Announce Type: replace Abstract: As Large Language Models (LLMs) advance toward open-ended autonomous agents, the mechanisms used to evaluate and guide their behavior must evolve ac

Learning from Demonstration via Spatiotemporal Tubes for Unknown Euler-Lagrange Systems

arXiv:2607.00534v1 Announce Type: new Abstract: We present STT-LfD, a unified Learning from Demonstration (LfD) framework that integrates motion learning with control for unknown Euler-Lagrange system

Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications

arXiv:2607.00442v1 Announce Type: cross Abstract: Reinforcement learning (RL) for quadruped locomotion commonly depends on fixed, hand-crafted, and Markovian reward functions that limit both interpret

Manifold-constrained Hamilton-Jacobi Reachability Learning for Decentralized Multi-Agent Motion Planning

arXiv:2511.03591v2 Announce Type: replace Abstract: Safe multi-agent motion planning (MAMP) under task-induced constraints is a critical challenge in robotics. Many real-world scenarios require robots

Mnemosyne: Agentic Transaction Processing for Validating and Repairing AI-generated Workflows

arXiv:2607.00269v1 Announce Type: new Abstract: LLMs, solvers, and agent teams increasingly generate workflow actions, repairs, and plans, but a generated action may be syntactically valid yet stale,

Reasoning Up the Instruction Ladder for Controllable Language Models

arXiv:2511.04694v5 Announce Type: replace-cross Abstract: As large language model (LLM) based systems take on high-stakes roles in real-world decision-making, they must reconcile competing instruction

Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions

arXiv:2507.15692v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) provide new opportunities for blind and low vision (BLV) people to access visual information in their daily l

Zero-Shot Distracted Driver Detection via Vision Language Models with Double Decoupling

arXiv:2601.08467v2 Announce Type: replace Abstract: Distracted driving is a major cause of traffic collisions, calling for robust and scalable detection methods. Vision-language models (VLMs) enable s

A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems

arXiv:2606.31639v1 Announce Type: cross Abstract: Large language models are no longer only text generators. They are increasingly embedded in retrieval pipelines, enterprise assistants, coding environ

A Scalable Whole-body Motion Transfer via Implicit Kinodynamic Motion Retargeting

arXiv:2509.15443v2 Announce Type: replace-cross Abstract: Human-to-humanoid imitation learning presents a promising pathway to address the severe data scarcity bottleneck in robotics by utilizing abun

A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents

arXiv:2606.31635v1 Announce Type: cross Abstract: Fault recovery in process plants still relies heavily on plant operators, especially when faults fall outside predefined supervisory logic. Operators

AI for Quality Assurance in the Operating Room

arXiv:2606.30657v1 Announce Type: cross Abstract: Surgical outcomes depend not only on patient factors and postoperative care but are also strongly influenced by the quality of the operation itself. Y