safety

Long-Distance Real-World Navigation of the Legged-Wheeled Robot Go2-W Using Deep Reinforcement Learning

arXiv:2606.21387v1 Announce Type: new Abstract: Legged-wheeled robots have long been studied for their potential to combine the efficient flat-ground mobility of wheels with the rough-terrain capabili

Safety

LP-NavOA: Integrated Local Navigation and Obstacle Avoidance for Humanoid Robots under Limited Perception

DGX agent

arXiv:2606.23249v1 Announce Type: new Abstract: Humanoid local navigation in cluttered environments must jointly resolve obstacle avoidance, sparse-goal recovery, and stable whole-body locomotion unde

safetyarxiv-cs-ro

23 Jun 2026

MAGNIFIED: RL Fine-tuning of Multimodal Large Language Models for Motion Planning

arXiv:2606.20641v1 Announce Type: cross Abstract: Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in semantic understanding and common sense reasoning, making them

Maintain Plasticity in Long-timescale Continual Test-time Adaptation

arXiv:2412.20034v2 Announce Type: replace Abstract: Continual test-time domain adaptation (CTTA) aims to adjust pre-trained source models to perform well over time across non-stationary target environ

MAPS: Multi-Anchor Projection Similarity for Joint Vision-Language Geo-Localization

arXiv:2606.22543v1 Announce Type: new Abstract: Humans localize places by integrating perceptual cues from vision with semantic reasoning from language, forming a scene understanding that is both intu

Measuring Model-Induced Discrimination via Efficient Fairness Approximation

arXiv:2405.09251v2 Announce Type: replace Abstract: Providing various machine learning (ML) applications in the real world, concerns about discrimination hidden in ML models are growing, particularly

Memory Contagion: Cross-Temporal Propagation of Evaluator Bias via Agent Memory

arXiv:2606.23195v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly rely on memory systems to maintain long-term coherence. Recent work shows that agent memories degrade dur

MemoryVAM: Integrating Memory into Video Action Model for Robot Manipulation

arXiv:2606.20679v1 Announce Type: cross Abstract: Video-world-model policies learn action-relevant representations by predicting future observations. However, they condition on only a short observatio

Meta copied slot machines to addict kids to Instagram. Now Zuckerberg is turning his company into a prediction market. Meta’s business model…

Meta copied slot machines to addict kids to Instagram. Now Zuckerberg is turning his company into a prediction market. Meta’s business model is profiting from addiction—kids, gamblers, & more. Stop it

Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation

arXiv:2606.22146v1 Announce Type: new Abstract: Meta-reinforcement learning is a promising approach to multi-objective optimisation because it enables rapid policy adaptation across changing environme

Mind the Privileged-to-Camera Gap: Actor-Centric Sidecar Supervision for Camera-First Open-Loop Waypoint Prediction

arXiv:2606.20772v1 Announce Type: new Abstract: Camera-first autonomous-driving models predict future ego waypoints from images, ego-state features, and route commands, but waypoint supervision alone

Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis

arXiv:2505.12462v3 Announce Type: replace Abstract: Robust reinforcement learning (RL) under the average-reward criterion is essential for long-term decision-making, particularly when the environment

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning

arXiv:2606.21943v1 Announce Type: new Abstract: Reinforcement learning (RL) has become central to LLM post-training, yet the methods that dominate current pipelines, PPO and GRPO, represent only a nar

Motion-Aware Reinforcement Learning For Object Localization

arXiv:2606.21764v1 Announce Type: new Abstract: We present MARLNet (Motion-Aware Reinforcement Learning Network), a PPO-based bounding-box refinement agent that incorporates a constant-velocity motion

MotionPyramid: Hierarchical Motion Representation and Residual Interfaces

arXiv:2606.20705v1 Announce Type: new Abstract: We ask whether the representational hierarchy seen in perception, from local primitives such as edges to higher level structures such as parts and objec

Multi-Year-to-Decadal Temperature Prediction using a Machine Learning Model-Analog Framework

arXiv:2502.17583v2 Announce Type: replace-cross Abstract: Multi-year-to-decadal climate predictions are a key tool in understanding the range of potential regional climate futures. Here, we present a

MV-WAM: Manifold-Aware World Action Model with Value Augmentation

arXiv:2606.21088v1 Announce Type: new Abstract: Achieving robust and generalizable manipulation across diverse environments remains a fundamental challenge in embodied robotics. Recent world action mo

My biggest NY-12 competitors aren't on the ballot today: Trump's AI oligarchs. If they can defeat me—the author of the nation's strongest AI…

My biggest NY-12 competitors aren't on the ballot today: Trump's AI oligarchs. If they can defeat me—the author of the nation's strongest AI safety bill in one of the nation's bluest districts—they ca

NegAS: Negative Label Guided Attention and Scoring for Out-of-Distribution Object Detection with Vision-Language Models

arXiv:2606.22537v1 Announce Type: new Abstract: Out-of-Distribution (OOD) detection is essential for ensuring the robustness and reliability of object detection systems deployed in safety-critical app

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation

arXiv:2606.21061v1 Announce Type: new Abstract: Stochastic segmentation seeks to represent multiple plausible masks for a single image, which is essential in safety- and quality-critical applications

Neural Conjugate Aggregation: Identifiable Unsupervised Multi-Sensor Regression under Heterogeneous Sensor Bias

arXiv:2606.22200v1 Announce Type: new Abstract: We study regression-based data fusion under uncertainty, where multiple noisy and biased measurement sources are available but ground-truth labels are a

Noise is Signal: Density-Based Outliers as Leading Indicators of Occupational Emergence in Labor Market Text

arXiv:2606.22769v1 Announce Type: new Abstract: Standard NLP pipelines for occupational clustering discard the 10-15% of job postings that density-based methods assign to noise. We argue this is an er

Objective-Behavior Alignment: Diagnostics for MORL Policy Selection

arXiv:2606.21321v1 Announce Type: new Abstract: Real-world decision-making often requires optimizing multiple competing objectives simultaneously. In reinforcement learning (RL), this is typically add

OFMU: Optimization-Driven Framework for Machine Unlearning

arXiv:2509.22483v2 Announce Type: replace Abstract: Large language models deployed in sensitive applications increasingly require the ability to unlearn specific knowledge, such as user requests, copy

OmniNWM: Omniscient Driving Navigation World Models

arXiv:2510.18313v5 Announce Type: replace Abstract: Autonomous driving world models are expected to work effectively across three core dimensions: state, action, and reward. However, existing methods

On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners

arXiv:2606.23668v1 Announce Type: new Abstract: Large Language Models (LLMs) are frequently portrayed as general-purpose solvers capable of solving arbitrary tasks. We argue that this view overlooks a

On the Position Bias of On-Policy Distillation

arXiv:2606.22600v1 Announce Type: new Abstract: On-Policy Distillation (OPD) improves the learning efficiency of standard reinforcement learning through dense, token-level supervision from teachers. I

One Image is All You Need: Agentic One-Shot Image Generation via Text-Based World Models for Long-Tail Spatial Perception

arXiv:2606.20764v1 Announce Type: new Abstract: Reliable spatial decision automation, such as autonomous driving and maritime surveillance, critically depends on robust visual perception. However, rea

One Size does not Fit All: Heterogeneous Latent Space Alignment for Unsupervised Domain Adaptation

arXiv:2606.21415v1 Announce Type: new Abstract: Domain shift remains a major obstacle to the reliable deployment of machine learning models in high-stakes environments such as healthcare. While Domain

oops

oops Goldman reckons that AI will create about 10trn of discounted value for the world, or up to 22trn. But markets have priced in 27trn of additional value creation. So the size of the 'bubble' has n

OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation

arXiv:2606.22174v1 Announce Type: new Abstract: Whole-body humanoid loco-manipulation requires coordinating the robot's entire kinematic chain. However, most existing systems typically decouple the up

'Oracle is under financial pressure because of an expensive build-out of AI data centers for customers like OpenAI.' https://www.bloomberg.c…

'Oracle is under financial pressure because of an expensive build-out of AI data centers for customers like OpenAI.' https://www.bloomberg.com/news/articles/2026-06-22/oracle-layoffs-fueled-by-ai-redu

Overcoming Imperfect Kinematics in Surgical Robotics Through Sim-to-Real Visuomotor Learning

arXiv:2606.21396v1 Announce Type: new Abstract: Robot-Assisted Surgery is integral to modern minimally invasive procedures, with automation emerging as the next frontier to enhance precision and reduc

PAIWorld: A 3D-Consistent World Foundation Model for Robotic Manipulation

arXiv:2606.18375v2 Announce Type: replace Abstract: World foundation models (WFMs) are powerful simulators, yet they predominantly operate in a single-view setting and lack the multi-view 3D consisten

PanoVine: Whole-Body Visuomotor Control for Soft Growing Vine Robot

arXiv:2606.22923v1 Announce Type: new Abstract: Vine robots, a class of soft, growing robots, are suitable for navigating complex and confined environments due to their compliant bodies and self-suppo

PG-MAP: Joint MAP Optimization for Inference-Time Alignment of Diffusion and Flow-Matching Models

arXiv:2606.22958v1 Announce Type: cross Abstract: Inference-time alignment of pretrained text-to-image models is typically performed along a single control axis, such as classifier-free guidance, atte

phi-Scene: Physically Grounded Image-to-3D Scene Reconstruction

arXiv:2606.21596v1 Announce Type: new Abstract: Reconstructing compositional 3D scenes from a single image is a fundamental challenge in 3D world modeling. Recent methods can recover high-fidelity, co

Physically-guided Image Generation for Multi-Projection Mapping

arXiv:2606.22477v1 Announce Type: new Abstract: Projection Mapping (PM) enables seamless superimposition of digital content onto real-world 3D objects, serving as a fundamental technique for immersive

PISCES: Annotation-free Text-to-Video Post-Training via Optimal Transport-Aligned Rewards

arXiv:2602.01624v2 Announce Type: replace Abstract: Text-to-video (T2V) generation aims to synthesize videos with high visual quality and temporal consistency that are semantically aligned with input

PJ-RoPE: A Fourier-Jet-Affine Position Space for Relative Attention

arXiv:2606.05345v2 Announce Type: replace Abstract: We organize relative-position mechanisms in attention as a learnable Fourier-Jet-Affine position space. The starting point is lag-shift dynamics: a

Platooning Connected, Autonomous, and Human-Driven Vehicles: A Deep Reinforcement Learning-based Approach

arXiv:2606.20648v1 Announce Type: cross Abstract: Conventionally, existing vehicle platooning approaches are designed for connected vehicles, typically including connected autonomous vehicles and conn

PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning

arXiv:2606.21139v1 Announce Type: cross Abstract: Latent action pretraining learns representations of visual change from pairs of observations, but existing methods typically encode each transition as

Policy-as-Data: Learning Generalizable HOI Diffusion Models from Simulated Physics

arXiv:2606.22806v1 Announce Type: new Abstract: Synthesizing realistic Human-Object Interactions (HOI) is critical for creating embodied avatars and functional virtual environments. However, current d

PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models

arXiv:2606.22540v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models provide a unified paradigm for robotic manipulation, yet their real-world deployment is often bottlenecked by execut

Pose-Agnostic Robotic Functional Grasping via Observation-Action Canonicalization

arXiv:2606.21148v1 Announce Type: new Abstract: Functional robotic grasping requires a policy that generalizes across diverse object geometries and poses while maintaining task-specific contact precis

Pose Anything Anywhere:Model-free Object Poses from Arbitrary References

arXiv:2606.23634v1 Announce Type: new Abstract: Estimating the 6D pose of unseen objects is a fundamental yet challenging problem for open-world robotics and embodied perception. Model-based methods a

Pose6DAug: Physically Plausible Multi-view Object Swapping for Robot Data Augmentation

arXiv:2606.20118v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) policies have shown strong potential for general-purpose manipulation, yet they often fail on novel, out-of-distr

Predicting cognitive load in immersive driving scenarios with a hybrid CNN-RNN model

arXiv:2408.06350v2 Announce Type: replace-cross Abstract: One debatable issue in traffic safety research is that cognitive load from sec-ondary tasks reduces primary task performance, such as driving.