safety

SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation

arXiv:2602.16863v3 Announce Type: replace-cross Abstract: The ability to manipulate tools significantly expands the set of tasks a robot can perform. Yet, tool manipulation represents a challenging cl

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Sparse2comm: Towards Robust Cooperative 3D Object Detection

DGX agent

arXiv:2610.08573v1 Announce Type: new Abstract: Cooperative perception improves autonomous driving by sharing complementary observations among vehicles and roadside infrastructure for 3D object detect

safetyarxiv-cs-cv

7 Oct 2026

SPEAR: Five Principles for Interactive Human-Agent Alignment

arXiv:2610.07204v1 Announce Type: cross Abstract: Recent AI alignment work often frames alignment as a pre-deployment optimization problem: collect human feedback, learn preferences or principles, fin

Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization

arXiv:2511.20718v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) algorithms such as PPO and GRPO are widely used to train large language models (LLMs) for multi-turn agentic tasks

Statistical Turbulence and High-Fidelity Disturbance Fields for Quadrotor Flight Control

arXiv:2610.06874v1 Announce Type: cross Abstract: Reinforcement-learning quadrotor controllers are usually trained under simplified wind models, yet the impact of wind-field fidelity, as opposed to ma

Structure-Aware Graph Abstention for Reliable Selective Forecasting

arXiv:2610.08322v1 Announce Type: new Abstract: Selective forecasting abstains on high-risk test windows under a retained-coverage budget. Existing gates such as TEM (Brusokas et al., 2025) score each

Structure-aware Keypoint Localization for Videofluoroscopic Swallowing Study

arXiv:2610.07726v1 Announce Type: new Abstract: Videofluoroscopic Swallowing Study (VFSS) is one of the gold standard for diagnosing swallowing disorders, providing dynamic X-ray imaging of the swallo

SWAP: Stepwise Action Policy Routing for Vision-Language-Action Models

arXiv:2610.06926v1 Announce Type: new Abstract: Robot manipulation systems using Vision-Language-Action (VLA) model backbones typically use just one VLA for task execution. However, individual VLAs do

Systemization of Knowledge (SoK): Human-Centered AI Safety for Youth

arXiv:2610.08554v1 Announce Type: cross Abstract: While HCI increasingly examines AI-safety for youth, the literature lacks a comprehensive view of what risks have been identified, how they are addres

Targeted search shows that random-device testing underestimates worst-case error in a simulated wave-based neural operator

arXiv:2610.07529v1 Announce Type: new Abstract: Wave-based processors promise fast, energy-efficient Fourier layers for neural operators. They are usually validated on randomly sampled devices, but us

Task-Space Imitation Guidance for Efficient Reinforcement Learning

arXiv:2610.07527v1 Announce Type: new Abstract: We introduce Task-Space Imitation Guidance for Efficient Reinforcement Learning (TIGER), a reward-construction and pretraining framework for sparse-rewa

TeleHairing: A Teleoperation Baseline for Robotic Haircutting

arXiv:2610.07096v1 Announce Type: new Abstract: Robotic haircutting requires controlled tool motion near the head while simultaneously accounting for communication, visual feedback, tool actuation, an

TF-PRVR: Training-Free Partially Relevant Video Retrieval

arXiv:2610.07925v1 Announce Type: new Abstract: Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos containing moments relevant to a given text query. Despite recent progress,

The Implicit Bias of Hyperbolic Representation Learning for Multiclass Data: A Busemann Risk Perspective

arXiv:2610.07131v1 Announce Type: new Abstract: We study the implicit bias of Riemannian gradient flow for hyperbolic multiclass classification with fixed class prototypes in hyperbolic space H^n. Our

The Missing Minimal Pair: Stereotype Evaluation in LLMs

arXiv:2610.08747v1 Announce Type: new Abstract: A common approach to measuring bias in Large Language Models is to compare the log-likelihoods of two contrastive stereotype sentences. We argue that su

The Model Plants the Trigger: Answer-Side Backdoor Attacks in Multi-Turn Large Language Models

arXiv:2610.07723v1 Announce Type: cross Abstract: Safety alignment in Large Language Models (LLMs) remains vulnerable to backdoor attacks. Existing LLM backdoors are almost all input-centric: activati

The Robot Is Not Its Description: GaugeBench for Representation Robustness in Morphology-Aware Policies

arXiv:2610.07597v1 Announce Type: cross Abstract: A robot description does more than specify a physical mechanism: it also encodes arbitrary conventions, such as joint-axis direction, joint-angle zero

this is typical of the cluelessness of people who literarily don’t understand the difference between a. a neural network solving a problem b…

this is typical of the cluelessness of people who literarily don’t understand the difference between a. a neural network solving a problem by itself and b. a neural network proposing an answer and hav

safetygary-marcus--x

Time-o1: Time-Series Forecasting Needs Transformed Label Alignment

arXiv:2505.17847v3 Announce Type: replace-cross Abstract: Training time-series forecasting models poses unique challenges in loss function design. Most existing approaches adopt temporal mean squared

Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift

arXiv:2607.17524v2 Announce Type: replace Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness predicti

Toward Alignment Scaling Laws: A Framework and First Preregistered Measurements

arXiv:2610.08540v1 Announce Type: new Abstract: Whether alignment gets easier or harder as models grow is often argued from isolated findings, as if alignment were one property. We treat it as a famil

Toward Trustworthy Physical AI for Human Interaction

arXiv:2610.07382v1 Announce Type: new Abstract: Robots are entering human spaces faster than we can establish when they deserve trust. We propose a framework for trustworthy physical AI that integrate

Towards the Automatic Synthesis of Interpretable Chess Tactics

arXiv:2610.07640v1 Announce Type: new Abstract: State-of-the-art reinforcement learning agents are capable of outperforming human experts at games like chess, Go and StarCraft II. These agents do not

TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models

arXiv:2610.07767v1 Announce Type: cross Abstract: Reinforcement learning (RL) for post-training large language models (LLMs) incurs substantial computation and memory overhead during rollout generatio

Transferable Spatial Temporal Coherence Adversarial Attack on Black-Box Vision Language Models for Autonomous Driving

arXiv:2610.08331v1 Announce Type: cross Abstract: The rapid integration of Vision Language Models (VLMs) into sensitive systems introduces critical safety vulnerabilities that remain unexplored in exi

TransMASK: Masked State Representation through Learned Transformation

arXiv:2603.05670v2 Announce Type: replace Abstract: When humans learn new manipulation skills, they are able to generalize these skills to new contexts and environments. In particular, when learning,

TRIAGE: Direction-Aware Mismatch Stabilization of Native NVFP4 Reinforcement Learning

arXiv:2610.07043v1 Announce Type: cross Abstract: Low-precision execution can substantially accelerate reinforcement learning (RL) for large language models, but discrepancies between learner and samp

Variance-Averse n-Step Offline Reinforcement Learning for Sparse Long-Horizon Environments

arXiv:2610.07899v1 Announce Type: cross Abstract: Generative actors are transforming offline reinforcement learning (RL) by enabling expressive policy classes that model complex action distributions.

Vector Map Quality Metrics for Contextual Autonomous Driving Systems

arXiv:2610.08043v1 Announce Type: new Abstract: Ensuring safety in autonomous driving requires continuous map maintenance supported by reliable quality indicators. In this context, it is crucial to id

VeriFine: Scaling Verification for Self-Improvement in Embodied Reasoning

arXiv:2610.08761v1 Announce Type: new Abstract: Self-improving policies continually expose new failure patterns, changing what their judges must be able to verify. However, current fixed judges constr

VETTA: Coordinating Turn- and Token-Level Credit Assignment for Multi-Turn LLM Agents

arXiv:2610.08402v1 Announce Type: new Abstract: Multi-turn LLM agents often receive sparse task feedback across several interactions, while generating each response token by token. This creates two re

ViDAL: A Visual Dynamics-Grounded Action Latent Space for Vision-Language-Action Models

arXiv:2610.08150v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become a central paradigm for robot policy learning, which predict actions in three forms: raw action chunks, d

Visual-Invariance-Augmented Feature Optimal Alignment for Transferable Adversarial Attacks against Closed-Source MLLMs

arXiv:2610.06977v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) remain vulnerable to transferable adversarial examples, especially in black-box settings where only open-sour

VLA-ACL: Action-Consistent Visual Token Pruning for Efficient Vision-Language-Action Models

arXiv:2610.08133v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong robotic manipulation performance but incur high computational costs from processing long token sequ

VOMMI: Collecting and Leveraging Portable Demonstrations for Mobile Manipulation

arXiv:2610.08220v1 Announce Type: cross Abstract: Portable mobile-manipulation demonstrations can help alleviate data scarcity for embodied intelligence, but obtaining reliable, low-cost, and robot-fr

WASD: Wasserstein-based Knowledge Distillation for Large Language Models

arXiv:2610.07706v1 Announce Type: cross Abstract: Autoregressive large language models (LLMs) have rapidly advanced in capability, but their increasing scale comes with substantial computational and m

We Need Explanation Cards to Connect Explanation Algorithms to the Real World

arXiv:2606.16786v2 Announce Type: replace Abstract: Algorithmic explanations are intended to help stakeholders understand opaque algorithmic decisions, but in practice, they often fall short. First, t

What Does Frechet Distance Measure? A Directional Decomposition

arXiv:2610.05518v1 Announce Type: cross Abstract: The Frechet distance is a de facto standard for evaluating generative models across domains, appearing as FID for images and FVD for videos. It summar

What Must Replay Preserve? Separating Correctable Bias from Class Correspondence

arXiv:2610.07077v1 Announce Type: new Abstract: Class-incremental learning must recognize all classes seen so far without task labels. Logit replay methods such as DER and DER++ mitigate forgetting by

What pass@k Cannot Measure: Evaluating Diversity and Capability Retention after Post-Training

arXiv:2610.07405v1 Announce Type: new Abstract: pass@k, the fraction of problems a model solves within k sampled attempts, is the field's default protocol for deciding whether reinforcement-learning (

When better traffic forecasts fail to improve signal control: a layered diagnostic study of forecast-to-decision value

arXiv:2610.06992v1 Announce Type: new Abstract: Improved traffic forecasts do not necessarily yield better signal-control decisions. We investigate this gap through a layered diagnostic study using 29

When Does AI Supervision Help? A Role-Aware Study of Network Fraud Decision Management with Blockchain Auditability

arXiv:2610.07434v1 Announce Type: new Abstract: When does a second artificial intelligence (AI) component improve a primary network-fraud decision rather than add operational burden? We study this que

When Does External Guidance Help LLM Reasoning? A Bias-Variance Theory of Guidance-Augmented GRPO

arXiv:2610.06861v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become the dominant paradigm for eliciting multi-step reasoning in large language models, an

When Plans Change Answers: Formalizing Cost-Accuracy Optimization for Semantic Queries

arXiv:2610.08089v1 Announce Type: cross Abstract: In semantic query engines, predicates are evaluated by machine-learned models, and the choice of a query plan affects not only the cost of a query but

When Tools Lie: Reliability of Mathematical Agents Under Corrupted Tool Feedback

arXiv:2610.08097v1 Announce Type: cross Abstract: Mathematical problem solving often requires deterministic computational steps that agents delegate to tools and implicitly trust. Yet tools can fail s

Where Does the Audio Jailbreak Live? A Controlled Frequency-Depth Audit of AdvWave-P on Qwen2-Audio

arXiv:2610.07005v1 Announce Type: cross Abstract: We audit frequency and decoder-depth claims for AdvWave-P, an additive audio jailbreak, on Qwen2-Audio. The protocol masks frequency components of the

Which Image Property Carries the Jailbreak? A Controlled Dissection of Image-to-Text Jailbreaks

arXiv:2610.07009v1 Announce Type: cross Abstract: Image-to-text jailbreaks place harmful intent in text, image content, or the relationship between them. We examine image-side factors across four publ

Whose Face Is It Anyway? A Multi-Model Audit of Facial Affect Recognition on Children, and Why the Gap Is the Head, Not the Features

arXiv:2610.08279v1 Announce Type: new Abstract: Facial affect models are trained almost entirely on adults, yet are increasingly applied to children in education, health, and developmental research. W