Search: “safety”

Fairness under Prediction Error in Predict-then-Optimize Pipelines

arXiv:2610.18593v1 Announce Type: new Abstract: Fairness constraints in predict-then-optimize pipelines are necessarily enforced on predicted outcomes hat{y}, since true outcomes y are unobserved at d

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Feature-Context Consistency: Unsupervised Adversarial Detection with Drift Stability on Attributed Graphs

DGX agent

arXiv:2610.16694v1 Announce Type: new Abstract: Adversarial detection on evolving attributed graphs faces two challenges: fraud labels arrive late or not at all, and natural distributional drift erode

safetyarxiv-cs-lg

14 Oct 2026

FENAS: A Few-Shot Meta-Ensembling Framework for Efficient Neural Architecture Search with Synergized Zero-Cost Proxies

arXiv:2610.15307v1 Announce Type: new Abstract: Efficient neural architecture search (NAS) requires accurate performance estimation under limited evaluation budgets. Existing zero-cost proxies (ZCPs)

Fitted Q-Iteration with Continuous Actions and Adaptive Data: Smoothed Coverage and Minimax Rates

arXiv:2605.05791v2 Announce Type: replace Abstract: Fitted Q-iteration (FQI) is well understood when the data come from a fixed distribution and the action set is finite, whereas value-based continuou

Flow Policy Gradients for Robot Control

arXiv:2602.02481v2 Announce Type: replace Abstract: Likelihood-based policy gradient methods are the dominant approach for training robot control policies from rewards. These methods rely on different

Flow-Support-Weighted Proposals for Test-Time Verified VLA Policies

arXiv:2610.14588v1 Announce Type: cross Abstract: Test-time sampling promises to turn one action prediction into a set of distinct executable candidates that a verifier can choose among. Downstream fi

Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation

arXiv:2610.17161v1 Announce Type: new Abstract: Teleoperation is widely used to collect high-quality demonstrations for robot learning. Dexterous manipulation, however, requires coordinated control of

ForeTac: Predictive Contact Guidance for Generative Robot Policies

arXiv:2610.18118v1 Announce Type: new Abstract: Visually similar actions can produce contact loss, jamming, slippage, or irreversible damage, while tactile feedback may arrive after a critical transit

Formal-Verification-Guided Bayesian Optimization for Non-Intrusive Last-Mile Coverage Closure

arXiv:2610.17449v1 Announce Type: cross Abstract: Last-mile functional coverage closure for modern chip designs remains costly because a mature UVM environment exposes many controls while direct hits

From Hitter to Player: Separating Motor Execution and Strategy in Competitive Humanoid Table Tennis

arXiv:2610.14067v1 Announce Type: new Abstract: Competitive humanoid table tennis requires two distinct abilities: executing a desired return and deciding where to return the ball. We separate these t

From Orbit Matching to Docking: A Hierarchical Reinforcement-Learning Approach to Multiphase Rendezvous Trajectory Design

arXiv:2610.17468v1 Announce Type: cross Abstract: As the complexity of space missions increases, autonomous spacecraft guidance must accommodate transitions between mission phases with distinct dynami

From Parallel to Sequential: Small-Batch Noise Restores Simplicity Bias of Muon

arXiv:2610.16868v1 Announce Type: new Abstract: Simplicity bias, which can arise from sequential feature learning during training, has been proposed as a general explanation for the success of deep le

From Sound to Sense: An Anatomy of Semantic Supervision for Speech-Driven Visual Grounding in Remote Sensing Imagery

arXiv:2610.16364v1 Announce Type: new Abstract: Speech-driven visual grounding in remote sensing (RSVG) removes the typing bottleneck of text-based interaction and is intended to preserve prosodic int

From Video to Vision: Learning Generalizable Humanoid Carrying from Human Motion

arXiv:2610.14561v1 Announce Type: new Abstract: Most existing approaches rely on video or motion-capture data for reinforcement learning (RL) training; however, all of these methods require an upper-l

From Volpiano to Pixels: 30,000 Lines of OMR Ground Truth for Medieval Chant

arXiv:2610.16862v1 Announce Type: new Abstract: Optical music recognition (OMR) for medieval chant lacks training data linking manuscript images to musical symbols across notational families. Using ex

Frozen Site-Probe Accuracy Is Not a Standalone Harmonization Score: An Audit of Evaluation Practice for Multi-Site MRI

arXiv:2610.14140v1 Announce Type: cross Abstract: Image-level MRI harmonization is often evaluated with a frozen site probe, a site classifier trained on raw scans and applied unchanged to harmonized

Game-Theoretic Reinforcement Learning based Topology-Agnostic Defense for Virtual Power Plants

arXiv:2610.15183v1 Announce Type: new Abstract: The rapid proliferation of distributed energy resources (DERs) aggregated by virtual power plants (VPPs) elevates cybersecurity risk by expanding the at

Generative Flow on Symbolic Abstractions for Embodiment Capability Discovery

arXiv:2610.17174v1 Announce Type: cross Abstract: Achieving complex, long-horizon robot behavior remains challenging because successful execution depends on skills grounded in a robot's physical affor

GeoFix: Geometry Fixing for 4D Consistent Generation

arXiv:2610.14293v1 Announce Type: new Abstract: Video diffusion models increasingly serve as priors for 4D generation, but the generated videos often lack geometric consistency. Recent approaches addr

Geometric Structure Predicts Per-Example Neighborhood Loss Under PCA Truncation

arXiv:2610.17742v1 Announce Type: cross Abstract: Dimensionality reduction can preserve the average performance of text embeddings even when a substantial fraction of their dimensions is removed. Aggr

GeoMotionGPT: Geometry-Aligned Motion Understanding with Large Language Models

arXiv:2601.07632v5 Announce Type: replace Abstract: Discrete motion tokenization has recently enabled Large Language Models (LLMs) to serve as versatile backbones for motion understanding and motion-l

Getting Space Right: Agentic Rewards for Image Generation

arXiv:2610.14852v1 Announce Type: new Abstract: Reinforcement learning (RL) is promising for spatial text-to-image generation, but existing work largely focuses on policy optimization, assuming accura

GreenWAM: A World Foundation Model for Robotics

arXiv:2610.18774v1 Announce Type: new Abstract: A robot needs a world model that predicts how its actions will change the scene and a policy that chooses those actions. Models are usually built and ev

GuidanceNFT: NFT-Based Reward Fine-Tuning of Guided Diffusion Models

arXiv:2610.15856v1 Announce Type: new Abstract: DiffusionNFT (NFT) fine-tunes diffusion models toward task-specific rewards, but optimizing these rewards does not guarantee fidelity to the full prompt

Hand2Robot: Object-Centric 4D Hand Reconstruction from Human Demonstrations for Robotics

arXiv:2610.15769v1 Announce Type: cross Abstract: Human demonstrations can capture task-relevant manipulation without using the target robot, but the recorded hand motions cannot directly serve as rob

Heuristics are Not Enough: Calibrated and Corrected Caching for Video Diffusion Acceleration

arXiv:2610.16545v1 Announce Type: new Abstract: Feature caching has become a mainstream training-free strategy for accelerating video diffusion models by reusing intermediate outputs across denoising

High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation

arXiv:2508.05570v2 Announce Type: replace-cross Abstract: In this paper, we study the bias and high-order error bounds of the Linear Stochastic Approximation (LSA) algorithm with Polyak-Ruppert (PR) a

HodgePhaseNet: Discrete Hodge Decomposition of Semantic Flows in Transformer Attention

arXiv:2610.14971v1 Announce Type: new Abstract: WavePhaseNet introduced frequency-resolved semantic representations, QuantumPhaseNet introduced gauge-covariant phase connections, and ModularPhaseNet m

How Shared Representations Become Specialized: Formation, Maintenance, and Latent Susceptibility

arXiv:2610.15830v1 Announce Type: new Abstract: When several predictions use the same internal representation, information about some targets can remain easier to recover than information about others

I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation

arXiv:2610.17748v1 Announce Type: new Abstract: Policies trained offline from teleoperated demonstrations can degrade outside their training distribution. This gap is particularly acute in long-horizo

Images Talk Back: Guiding MMDiTs by Suppressing Image-to-Text Feedback

arXiv:2610.13941v2 Announce Type: replace Abstract: Multimodal diffusion transformers (MMDiTs) allow the evolving image to update internal text representations through joint attention. These represent

Imitation Learning via Discrete and Rhythmic Primitive Composition

arXiv:2610.15639v1 Announce Type: new Abstract: Long-horizon robotic manipulation is inherently composed of semantically meaningful sub-tasks. While Movement Primitives (MPs) can model these component

Improving Neural Symbolic Regression with a Diverse Prior and Post-training Alignment

arXiv:2610.17994v1 Announce Type: new Abstract: Symbolic regression is the problem of finding an expression that fits a dataset. Solving it requires searching a combinatorial space of candidate expres

Improving Policy Learning from Delayed Interventions

arXiv:2610.17298v1 Announce Type: new Abstract: In this work, we explore the idea of expert intervention delays in human-supervised robotic systems for imitation learning system. Previous works in imi

In View or Out of View? A Vision-Aware Filter that Infers and Reacts to the Human's Field of View

arXiv:2610.14577v1 Announce Type: new Abstract: During human-robot interaction, sight plays an important role. When the human sees a robot, they can observe the robot's actions and better predict the

Inference on the Value of Policy Improvement in Adaptive Experiments

arXiv:2610.15789v1 Announce Type: cross Abstract: In this paper, we develop an asymptotically normal estimator for the value of policy improvement (VPI) under adaptively collected data. This value, wh

Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient

arXiv:2610.14723v1 Announce Type: new Abstract: A method integrating policy gradient with fuzzy inference has been proposed. This method, hereafter referred to as the base model, has high flexibility

Intent-guided Adaptive Stiffening for Visuomotor Policies under Unknown Resistance

arXiv:2610.18386v1 Announce Type: new Abstract: Manipulation tasks such as unscrewing a cap or sliding a part in contact with a surface require imposing force along a high-friction direction: the robo

Interaction-as-a-Judge

arXiv:2610.17895v1 Announce Type: new Abstract: Coding agents are tasked with producing sophisticated interactive applications such as websites, desktop applications, and games. While humans assess th

Interaction Latents as Actions for Contact-Rich Manipulation

arXiv:2610.18855v1 Announce Type: new Abstract: Contact-rich manipulation requires robots to coordinate not only where to move, but also how contact and force should evolve throughout an interaction.

Is the Platform Part of the Measurement? A Protocol and Simulation Study for Cross-Platform Equivalence of Machine-Labelled Policy Sentiment

arXiv:2610.16757v1 Announce Type: new Abstract: Comparing machine-labelled sentiment across social media platforms is now routine: differences in classifier scores between platforms are read as differ

Joint Contrastive-Adversarial Representation Learning for Unsupervised Domain Adaptation

arXiv:2610.15613v1 Announce Type: cross Abstract: Effective unsupervised domain adaptation (UDA) requires a representation that captures transferable structure from both the labeled source domain and

JustOPD: On-Policy Distillation from Autoregressive to Diffusion Language Models

arXiv:2610.17397v1 Announce Type: new Abstract: On-policy distillation (OPD) is a post-training approach for large language models (LLMs) that enables a student model to learn a teacher model's distri

Kairos: Transactional Security Enforcement at the System Boundary for LLM Agents

arXiv:2610.18457v1 Announce Type: cross Abstract: Large language model (LLM) agents are becoming an integral part of the software development lifecycle, and the capabilities granted to them allow an a

KedgeFlow: A Programmable Control Plane for Enterprise AI Agents

arXiv:2610.14810v1 Announce Type: cross Abstract: Enterprise AI agents can execute altered versions of approved actions, concurrently reuse the same permission, or retry operations whose responses wer

Learning omega-Powered Conditional Distributions with Importance-Weighted Flow Matching

arXiv:2610.16419v1 Announce Type: cross Abstract: Classifier-Free Guidance (CFG) with guidance scale gamma is commonly motivated by the omega-powered conditional distribution obtained by setting omega

Learning Robust Visuomotor Policies via Dynamics Alignment

arXiv:2510.27114v2 Announce Type: replace-cross Abstract: Generative BC policies model expert action distributions well, but they do not explicitly reason about what scene a candidate action would pro

Learning to Assist: Collaborative VLAs for Implicit Human-Robot Collaboration

arXiv:2606.12475v2 Announce Type: replace Abstract: Human-robot collaboration (HRC) combines the complementary strengths of humans and robots to improve task efficiency. However, many existing collabo