Search: “safety”

Property-Driven Synthetic Data Engineering for Data-Scarce Software Systems: Reflections from the Breast Cancer Domain

arXiv:2607.06133v1 Announce Type: cross Abstract: Modern software systems increasingly depend on data for analysis, prediction, testing, and decision-making. Yet many important domains, including medi

8 Jul 2026

Safety

The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology

DGX agent

arXiv:2607.06531v1 Announce Type: new Abstract: - Objective: Multimodal deep learning models in oncology are currently limited by monolithic designs that rigidly couple data ingestion, clinical routin

safetyarxiv-cs-ai

8 Jul 2026

TILDE: TILt-based Distributional Erasure for Concept Unlearning

arXiv:2607.06432v1 Announce Type: cross Abstract: Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes,

8 Jul 2026

Truthful or Fabricated? Using Causal Attribution to Mitigate Reward Hacking in Explanations

arXiv:2504.05294v3 Announce Type: replace Abstract: Chain-of-thought explanations are widely used to inspect the decision process of large language models (LLMs) and to evaluate the trustworthiness of

8 Jul 2026

A Precedent-Guided Co-Scientist for Side-Effect-Aware Drug Redesign

arXiv:2607.02944v1 Announce Type: cross Abstract: We propose PRECEDE, a precedent-guided co-scientist for side-effect-aware drug redesign that revises a parent compound to mitigate a specified side ef

A User-driven Design Framework for Robotaxi

arXiv:2602.19107v3 Announce Type: replace Abstract: Robotaxis are emerging as a promising form of urban mobility, but removing human drivers fundamentally reshapes passenger-vehicle interaction and ra

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

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

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

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

NRC is (sort of) getting rid of 'as low as reasonably achievable' standard

The NRC proposed replacing the longstanding 'as low as reasonably achievable' (ALARA) principle with clearer, more objective requirements focused on compliance with regulatory precautions and establis

safetyars-technica

6 Jul 2026

We need a bit more shame. People used to avoid certain self-interested behaviors to avoid shame, private and public. Law and customs assumed…

We need a bit more shame. People used to avoid certain self-interested behaviors to avoid shame, private and public. Law and customs assumed this. Now, 38% of Stanford students claim to be disabled. 4

safetyelon-musk--x

6 Jul 2026

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

Emails disclosed in a court filing detail the uneasy back-and-forth between Dario Amodei and DOD's Emil Michael and how Anthropic's relationship with DOD soured (Wall Street Journal)

Wall Street Journal: Emails disclosed in a court filing detail the uneasy back-and-forth between Dario Amodei and DOD's Emil Michael and how Anthropic's relationship with DOD soured — Undersecretary E

safetytechmeme

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