Search: “safety”

SAGE: A Strategy-Aware Graph-Enhanced Generation Framework For Online Counseling

arXiv:2604.26630v1 Announce Type: new Abstract: Effective mental health counseling is a complex, theory-driven process requiring the simultaneous integration of psychological frameworks, real-time dis

safetyarxiv-cs-cl

30 Apr 2026

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Sparsity as a Key: Unlocking New Insights from Latent Structures for Out-of-Distribution Detection

DGX agent

arXiv:2604.26409v1 Announce Type: new Abstract: Sparse Autoencoders (SAEs) have demonstrated significant success in interpreting Large Language Models (LLMs) by decomposing dense representations into

safetyarxiv-cs-cv

30 Apr 2026

Tatemae: Detecting Alignment Faking via Tool Selection in LLMs

arXiv:2604.26511v1 Announce Type: cross Abstract: Alignment faking (AF) occurs when an LLM strategically complies with training objectives to avoid value modification, reverting to prior preferences o

30 Apr 2026

Value-Guided Iterative Refinement and the DIQ-H Benchmark for Evaluating VLM Robustness

arXiv:2512.03992v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are essential for embodied AI and safety-critical applications, such as robotics and autonomous systems. However

30 Apr 2026

Knowledge Distillation Must Account for What It Loses

arXiv:2604.25110v1 Announce Type: new Abstract: This position paper argues that knowledge distillation must account for what it loses: student models should be judged not only by retained task scores,

29 Apr 2026

Reinforcement Learning for Testing Interdependent Requirements in Autonomous Vehicles: An Empirical Study

arXiv:2502.15792v2 Announce Type: replace-cross Abstract: Autonomous vehicles (AVs) make driving decisions without humans, making dependability assurance critical. Scenario-based testing is widely use

29 Apr 2026

VISION-SLS: Safe Perception-Based Control from Learned Visual Representations via System Level Synthesis

arXiv:2604.24894v1 Announce Type: cross Abstract: We propose VISION-SLS, a method for nonlinear output-feedback control from high-resolution RGB images which provides robust constraint satisfaction gu

29 Apr 2026

Betting for Sim-to-Real Performance Evaluation

arXiv:2604.24018v1 Announce Type: new Abstract: This paper studies the problem of robot performance evaluation, focusing on how to obtain accurate and efficient estimates of real-world behavior under

Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models

arXiv:2502.14888v4 Announce Type: replace-cross Abstract: The success of vision-language models is primarily attributed to effective alignment across modalities such as vision and language. However, m

BMD-45: A Large-Scale CCTV Vehicle Detection Dataset for Urban Traffic in Developing Cities

arXiv:2604.24419v1 Announce Type: new Abstract: Robust vehicle detection from fixed CCTV cameras is critical for Intelligent Transportation Systems. Yet existing benchmarks predominantly feature relat

CAP-CoT: Cycle Adversarial Prompt for Improving Chain of Thoughts in LLM Reasoning

arXiv:2604.23270v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has emerged as a simple and effective way to elicit step-by-step solutions from large language models (LLMs). However,

CombiMOTS: Combinatorial Multi-Objective Tree Search for Dual-Target Molecule Generation

arXiv:2604.23307v1 Announce Type: cross Abstract: Dual-target molecule generation, which focuses on discovering compounds capable of interacting with two target proteins, has garnered significant atte

Discovering Failure Modes in Vision-Language Models using RL

arXiv:2604.04733v2 Announce Type: replace-cross Abstract: Vision-language Models (VLMs), despite achieving strong performance on multimodal benchmarks, often misinterpret straightforward visual concep

Exploring the Secondary Risks of Large Language Models

arXiv:2506.12382v5 Announce Type: replace-cross Abstract: Ensuring the safety and alignment of Large Language Models is a significant challenge with their growing integration into critical application

Fix Initial Codes and Iteratively Refine Textual Directions Toward Safe Multi-Turn Code Correction

arXiv:2604.23989v1 Announce Type: cross Abstract: Recent work on large language models (LLMs) has emphasized the importance of scaling inference compute. From this perspective, the state-of-the-art me

From Optimization to Prediction: Transformer-Based Path-Flow Estimation to the Traffic Assignment Problem

arXiv:2510.19889v2 Announce Type: replace-cross Abstract: The traffic assignment problem is essential for traffic flow analysis, traditionally solved using mathematical programs under the Equilibrium

Generative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control

arXiv:2603.17834v2 Announce Type: replace-cross Abstract: Diffusion models and flow matching have become a cornerstone of robotic imitation learning, yet they suffer from a structural inefficiency whe

Guided Speculative Inference for Efficient Test-Time Alignment of LLMs

arXiv:2506.04118v3 Announce Type: replace Abstract: We propose Guided Speculative Inference (GSI), a novel algorithm for efficient reward-guided decoding in large language models. GSI combines soft be

Hierarchical RL-MPC Control for Dynamic Wake Steering in Wind Farms

arXiv:2604.22797v1 Announce Type: cross Abstract: Wind farm wake steering optimization is challenging due to complex flow physics and changing conditions. This paper presents a hierarchical framework

Knowledge Lever Risk Management for Software Engineering: A Stochastic Framework for Mitigating Knowledge Loss

arXiv:2604.23257v1 Announce Type: cross Abstract: Software engineering (SE) organizations operate in a knowledge-intensive domain where critical assets -- architectural expertise, design rationale, an

Learning from Demonstration with Failure Awareness for Safe Robot Navigation

arXiv:2604.23360v1 Announce Type: new Abstract: Learning from demonstration is widely used for robot navigation, yet it suffers from a fundamental limitation: demonstrations consist predominantly of s

OpenPodcar2: a robust, ROS2 vehicle for self-driving research

arXiv:2604.24242v1 Announce Type: new Abstract: OpenPodcar2 is a robust, ROS2-interfaced, low-cost, open source hardware and software, autonomous vehicle platform based on an off-the-shelf, hard-canop

Quantifying Divergence in Inter-LLM Communication Through API Retrieval and Ranking

arXiv:2604.22760v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly operate as autonomous agents that reason over external APIs to perform complex tasks. However, their reliabi

Reinforcement Learning with Backtracking Feedback

arXiv:2602.08377v2 Announce Type: replace-cross Abstract: Addressing the critical need for robust safety in Large Language Models (LLMs), particularly against adversarial attacks and in-distribution e

Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey

arXiv:2505.15957v4 Announce Type: replace-cross Abstract: With advancements in large audio-language models (LALMs), which enhance large language models (LLMs) with auditory capabilities, these models

Transferable Physical-World Adversarial Patches Against Object Detection in Autonomous Driving

arXiv:2604.23105v1 Announce Type: new Abstract: Deep learning drives major advances in autonomous driving (AD), where object detectors are central to perception. However, adversarial attacks pose sign

UGAF-ITS: A Standards Harmonization Framework and Validation Tool for Multi-Framework AI Governance in Distributed Intelligent Transportation Systems

arXiv:2604.22789v1 Announce Type: cross Abstract: Organizations deploying AI-enabled Intelligent Transportation Systems face fragmented governance: ISO/IEC 42001 demands a certifiable management syste

UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems

arXiv:2604.23362v1 Announce Type: cross Abstract: Adversarial attacks play a pivotal role in testing and improving the reliability of deep learning (DL) systems. Existing literature has demonstrated t

Verifying Quantized GNNs With Readout Is Decidable But Highly Intractable

arXiv:2510.08045v2 Announce Type: replace-cross Abstract: We introduce a logical language for reasoning about quantized aggregate-combine graph neural networks with global readout (ACR-GNNs). We provi

When Policies Cannot Be Retrained: A Unified Closed-Form View of Post-Training Steering in Offline Reinforcement Learning

arXiv:2604.22873v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) can learn effective policies from fixed datasets, but deployment objectives may change after training, and in many

A Co-Evolutionary Theory of Human-AI Coexistence: Mutualism, Governance, and Dynamics in Complex Societies

arXiv:2604.22227v1 Announce Type: cross Abstract: Classical robot ethics is often framed around obedience, most famously through Asimov's laws. This framing is too narrow for contemporary AI systems,

How Large Language Models Balance Internal Knowledge with User and Document Assertions

arXiv:2604.22193v1 Announce Type: new Abstract: Large language models (LLMs) often need to balance their internal parametric knowledge with external information, such as user beliefs and content from

safetyarxiv-cs-cl

Learning-augmented robotic automation for real-world manufacturing

arXiv:2604.22235v1 Announce Type: cross Abstract: Industrial robots are widely used in manufacturing, yet most manipulation still depends on fixed waypoint scripts that are brittle to environmental ch

On the Properties of Feature Attribution for Supervised Contrastive Learning

arXiv:2604.22540v1 Announce Type: cross Abstract: Most Neural Networks (NNs) for classification are trained using Cross-Entropy as a loss function. This approach requires the model to have an explicit

Reliable Self-Harm Risk Screening via Adaptive Multi-Agent LLM Systems

arXiv:2604.22154v1 Announce Type: cross Abstract: Emerging AI systems in behavioral health and psychiatry use multi-step or multi-agent LLM pipelines for tasks like assessing self-harm risk and screen

Rethinking XAI Evaluation: A Human-Centered Audit of Shapley Benchmarks in High-Stakes Settings

arXiv:2604.22662v1 Announce Type: cross Abstract: Shapley values are a cornerstone of explainable AI, yet their proliferation into competing formulations has created a fragmented landscape with little

A Survey of Legged Robotics in Non-Inertial Environments: Past, Present, and Future

arXiv:2604.20990v1 Announce Type: new Abstract: Legged robots have demonstrated remarkable agility on rigid, stationary ground, but their locomotion reliability remains limited in non-inertial environ

Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection

arXiv:2512.06171v2 Announce Type: replace Abstract: Shearography is an interferometric technique sensitive to surface displacement gradients, providing high sensitivity for detecting subsurface defect

CARE: Counselor-Aligned Response Engine for Online Mental-Health Support

arXiv:2604.21352v1 Announce Type: new Abstract: Mental health challenges are increasing worldwide, straining emotional support services and leading to counselor overload. This can result in delayed re

safetyarxiv-cs-cl

Dialect vs Demographics: Quantifying LLM Bias from Implicit Linguistic Signals vs. Explicit User Profiles

arXiv:2604.21152v1 Announce Type: cross Abstract: As state-of-the-art Large Language Models (LLMs) have become ubiquitous, ensuring equitable performance across diverse demographics is critical. Howev

Escaping the Agreement Trap: Defensibility Signals for Evaluating Rule-Governed AI

arXiv:2604.20972v1 Announce Type: new Abstract: Content moderation systems are typically evaluated by measuring agreement with human labels. In rule-governed environments this assumption fails: multip

Fairness Evaluation and Inference Level Mitigation in LLMs

arXiv:2510.18914v4 Announce Type: replace-cross Abstract: Large language models often display undesirable behaviors embedded in their internal representations, undermining fairness, inconsistency drif

Inferring High-Level Events from Timestamped Data: Complexity and Medical Applications

arXiv:2604.21793v1 Announce Type: new Abstract: In this paper, we develop a novel logic-based approach to detecting high-level temporally extended events from timestamped data and background knowledge

Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous

arXiv:2512.09111v3 Announce Type: replace-cross Abstract: Reliable real-time trajectory generation is essential for future autonomous spacecraft. While recent progress in nonconvex guidance and contro

Probabilistic Verification of Neural Networks via Efficient Probabilistic Hull Generation

arXiv:2604.21556v1 Announce Type: new Abstract: The problem of probabilistic verification of a neural network investigates the probability of satisfying the safe constraints in the output space when t

Task-specific Subnetwork Discovery in Reinforcement Learning for Autonomous Underwater Navigation

arXiv:2604.21640v1 Announce Type: cross Abstract: Autonomous underwater vehicles are required to perform multiple tasks adaptively and in an explainable manner under dynamic, uncertain conditions and

TraceScope: Interactive URL Triage via Decoupled Checklist Adjudication

arXiv:2604.21840v1 Announce Type: cross Abstract: Modern phishing campaigns increasingly evade snapshot-based URL classifiers using interaction gates (e.g., checkbox/slider challenges), delayed conten

Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation

arXiv:2512.08216v3 Announce Type: replace-cross Abstract: Accurate segmentation of lung tumors from 3D computed tomography (CT) scans is essential for automated treatment planning and response assessm