Search: “safety”

CSA-Graphs: A Privacy-Preserving Structural Dataset for Child Sexual Abuse Research

arXiv:2604.07132v1 Announce Type: cross Abstract: Child Sexual Abuse Imagery (CSAI) classification is an important yet challenging problem for computer vision research due to the strict legal and ethi

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Data Leakage in Automotive Perception: Practitioners' Insights

DGX agent

arXiv:2604.06899v1 Announce Type: cross Abstract: Data leakage is the inadvertent transfer of information between training and evaluation datasets that poses a subtle, yet critical, risk to the reliab

safetyarxiv-cs-lg

10 Apr 2026

Deep Learning-Powered Visual SLAM Aimed at Assisting Visually Impaired Navigation

arXiv:2510.20549v2 Announce Type: replace Abstract: Despite advancements in SLAM technologies, robust operation under challenging conditions such as low-texture, motion-blur, or challenging lighting r

Local Ai

FedDetox: Robust Federated SLM Alignment via On-Device Data Sanitization

arXiv:2604.06833v1 Announce Type: cross Abstract: As high quality public data becomes scarce, Federated Learning (FL) provides a vital pathway to leverage valuable private user data while preserving p

local-aiarxiv-cs-lg

From experimentation to engagement: on the paradox of participatory AI and power in contexts of forced displacement and humanitarian crises

arXiv:2604.06219v1 Announce Type: cross Abstract: Across the Global North, calls for participatory artificial intelligence (AI) to improve the responsible, safe, and ethical use of AI have increased,

Front-End Ethics for Sensor-Fused Health Conversational Agents: An Ethical Design Space for Biometrics

arXiv:2604.06203v1 Announce Type: cross Abstract: The integration of continuous data from built-in sensors and Large Language Models (LLMs) has fueled a surge of 'Sensor-Fused LLM agents' for personal

Governing frontier general-purpose AI in the public sector: adaptive risk management and policy capacity under uncertainty through 2030

arXiv:2604.06215v1 Announce Type: cross Abstract: The governance of frontier general-purpose artificial intelligence has become a public-sector problem of institutional design, not merely a technical

Harnessing Embodied Agents: Runtime Governance for Policy-Constrained Execution

arXiv:2604.07833v1 Announce Type: new Abstract: Embodied agents are evolving from passive reasoning systems into active executors that interact with tools, robots, and physical environments. Once gran

Harnessing Hyperbolic Geometry for Harmful Prompt Detection and Sanitization

arXiv:2604.06285v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have become essential for tasks such as image synthesis, captioning, and retrieval by aligning textual and visual inform

Incorporating Social Awareness into Control of Unknown Multi-Agent Systems: A Real-Time Spatiotemporal Tubes Approach

arXiv:2510.25597v2 Announce Type: replace-cross Abstract: This paper presents a decentralized control framework that incorporates social awareness into multi-agent systems with unknown dynamics to ach

Invisible to Humans, Triggered by Agents: Stealthy Jailbreak Attacks on Mobile Vision-Language Agents

arXiv:2510.07809v4 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) empower autonomous mobile agents, yet their security under realistic mobile deployment constraints remain

Learning Who Disagrees: Demographic Importance Weighting for Modeling Annotator Distributions with DiADEM

arXiv:2604.08425v1 Announce Type: cross Abstract: When humans label subjective content, they disagree, and that disagreement is not noise. It reflects genuine differences in perspective shaped by anno

LINE: LLM-based Iterative Neuron Explanations for Vision Models

arXiv:2604.08039v1 Announce Type: new Abstract: Interpreting the concepts encoded by individual neurons in deep neural networks is a crucial step towards understanding their complex decision-making pr

LUMINA: Foundation Models for Topology Transferable ACOPF

arXiv:2603.04300v2 Announce Type: replace Abstract: Foundation models in general promise to accelerate scientific computation by learning reusable representations across problem instances, yet constra

MedVR: Annotation-Free Medical Visual Reasoning via Agentic Reinforcement Learning

arXiv:2604.08203v1 Announce Type: new Abstract: Medical Vision-Language Models (VLMs) hold immense promise for complex clinical tasks, but their reasoning capabilities are often constrained by text-on

Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability

arXiv:2604.06628v1 Announce Type: new Abstract: A prevailing narrative in LLM post-training holds that supervised finetuning (SFT) memorizes while reinforcement learning (RL) generalizes. We revisit t

Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU

arXiv:2604.07644v1 Announce Type: new Abstract: We present GPU-SLS, a GPU-parallelized framework for safe, robust nonlinear model predictive control (MPC) that scales to high-dimensional uncertain rob

Towards the Development of an LLM-Based Methodology for Automated Security Profiling in Compliance with Ukrainian Cybersecurity Regulations

arXiv:2604.06274v1 Announce Type: cross Abstract: In recent years, the pace of development of information technology in various areas has increased drastically, forcing cybersecurity specialists to co

Model Releases

TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories

model-releasesarxiv-cs-ai

arXiv:2604.07223v1 Announce Type: cross Abstract: As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to int

WebSP-Eval: Evaluating Web Agents on Website Security and Privacy Tasks

arXiv:2604.06367v1 Announce Type: cross Abstract: Web agents automate browser tasks, ranging from simple form completion to complex workflows like ordering groceries. While current benchmarks evaluate

A-3PO: Accelerating Asynchronous LLM Training with Staleness-aware Proximal Policy Approximation

arXiv:2512.06547v4 Announce Type: replace-cross Abstract: Decoupled PPO has been a successful reinforcement learning (RL) algorithm to deal with the high data staleness under the asynchronous RL setti

A method for tissue-mask supported whole-body image registration in the UK Biobank

arXiv:2512.02702v3 Announce Type: replace Abstract: The UK Biobank is a large-scale study collecting imaging and non-imaging health data. Robust and accurate inter-subject image registration of these

A New First-Order Meta-Learning Algorithm with Convergence Guarantees

arXiv:2409.03682v2 Announce Type: replace Abstract: Learning new tasks by leveraging prior experience is a fundamental trait of intelligent systems. While Model-Agnostic Meta-Learning (MAML) is a lead

Accuracy and Order Sensitivity Diverge Under Label-Free Strategies

arXiv:2608.11947v1 Announce Type: cross Abstract: Multiple-choice benchmarks are widely used to evaluate large language models, but MCQ scores conflate knowledge with sensitivity to option order, whic

Adaptation of Generalist Robot Policies with Minimal Data

arXiv:2608.11363v1 Announce Type: cross Abstract: A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction. Yet

Alignment of Similarity-Transformed Images Based on Fourier--Mellin Transform Using Auxiliary Function Method

arXiv:2608.11565v1 Announce Type: cross Abstract: This paper proposes an algorithm for estimating the similarity transformation, namely translation, scale, and rotation, between two images with subpix

Anti-Shortcut Distillation via Temporal Negative Knowledge Transfer

arXiv:2608.11789v1 Announce Type: new Abstract: Knowledge distillation (KD) trains a compact student by attracting it towards a converged teacher. It is silent about which directions the teacher itsel

AutoGrable: What Is a Good Graph for a Table?

arXiv:2608.11431v1 Announce Type: new Abstract: Graph learning presupposes a graph, and tables and relational databases do not come with one. Applying a GNN to them requires deciding which entities be

Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed

arXiv:2608.11981v1 Announce Type: new Abstract: Small Language Models (SLMs) have emerged as a more efficient alternative to traditional Large Language Models (LLMs), offering promising potential in r

Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models

arXiv:2608.12078v1 Announce Type: cross Abstract: Learning world models from offline trajectories enables agents to accomplish different tasks through planning. Object-centric (OC) representations, wh

CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement

arXiv:2608.11631v1 Announce Type: new Abstract: In open-domain human-computer interaction scenarios, large language models (LLMs) frequently encounter user queries that are ambiguous or incomplete. In

CoAdapt-GUI: Joint Workflow Context and Policy Adaptation for Unseen GUI Applications

arXiv:2608.11588v1 Announce Type: new Abstract: Mobile GUI agents remain brittle when deployed to applications absent from source training. We study novel-app generalization under a limited target int

Continuous-Latent Predictive Modeling with Semantic Alignment for EEG-Language Foundation Models

arXiv:2608.11656v1 Announce Type: new Abstract: Recent advances in EEG foundation models have demonstrated the potential of large-scale pretraining to enable generalizable neural decoding across subje

CookVoice: Unified Framework for Style Controllable Multi-Modal Human Voice Generation

arXiv:2608.11590v1 Announce Type: cross Abstract: Human voice generation has made rapid progress in speech generation, singing voice generation, voice cloning, and voice editing. However, most existin

Defending against Model Extraction for GNNs with Model Reprogramming

arXiv:2608.11495v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) serve as the backbone for high-stakes applications in Machine-Learning-as-a-Service (MLaaS). Still, their black-box deploym

Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction

arXiv:2608.11772v1 Announce Type: new Abstract: Self-correction is particularly useful when a failure constrains the next repair. Coding agents benefit from this property because compilers, tests, and

Diffusion-Guided Cooperative Policy Learning for Target Tracking Based on Underwater Mobile Agent Networks

arXiv:2603.29426v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning (MARL) provides a promising solution for cooperative target tracking in networks of autonomous underwater v

Disentangling the Expressivity of RoPE

arXiv:2608.11909v1 Announce Type: new Abstract: Two accounts recur in explanations of the success of rotary position embeddings (RoPE). Expressivity studies associate periodic position information wit

Dual Anchors, Do It Better: Hierarchical Group Merging for Zero-Shot Anomaly Detection

arXiv:2608.11933v1 Announce Type: new Abstract: Zero-shot anomaly detection (ZSAD) aims to identify anomalies in unseen domains, a setting that is particularly critical for industrial and medical appl

Dual-Domain Cross-Modal Decoding for Clinical Text-Guided Medical Image Segmentation

arXiv:2608.11335v1 Announce Type: cross Abstract: Clinical text can narrow down what to segment, but recent text-guided designs emphasize spatial alignment while overlooking frequency content that gov

Dynamic Governance of Multi-LLM Agent Systems for Collaborative Conversational Outcomes

arXiv:2608.11207v1 Announce Type: new Abstract: When two LLM agents with structurally opposed objectives interact across multiple turns, the absence of a shared goal function produces not competition

Enhancing Visual Domain Robustness in Behaviour Cloning via Saliency-Guided Augmentation

arXiv:2608.11870v1 Announce Type: new Abstract: In vision-based behavior cloning (BC), conventional image augmentations such as Random Crop and Color Jitter often fall short under substantial visual d

Epiplexity Guided Data Selection and Generation for Out-of-Distribution Generalization

arXiv:2608.11746v1 Announce Type: cross Abstract: Modern systems are increasingly expected to transfer across tasks not specified during training. What data facilitates generalization in these new, un

FM-LLM: A frequency-enhanced mixture-of-experts framework for adapting LLMs to time series forecasting

arXiv:2608.11623v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting. However, existing methods rely heavily

Foresight Without Seeing: Latent Futures for World Action Models

arXiv:2608.11605v1 Announce Type: new Abstract: World Action Models (WAMs) couple future visual prediction with robot action generation, enabling policies to model how the physical world evolves durin

FQTree: Fine-grained Quantization and Hardware Generation of Boosted Decision Trees

arXiv:2608.12140v1 Announce Type: cross Abstract: Boosted decision trees (BDTs) are widely used in latency-critical applications, but efficient hardware deployment remains challenging. Existing design

From Prompting to Behavioral Alignment: Personalized LLM Judges for Recommendation Evaluation

arXiv:2608.11493v1 Announce Type: new Abstract: Traditional offline recommendation evaluation relies heavily on complex, manually maintained feature pipelines that are difficult to scale. While Large

GCPO: Diagnosing and Constraining Subspace Geometry in Rollout RL for LLMs

arXiv:2608.11674v1 Announce Type: cross Abstract: On-policy rollout methods such as GRPO are central to post-training of large language models, yet they frequently suffer from training instabilities,