Search: “safety”

AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery

arXiv:2610.14959v1 Announce Type: cross Abstract: Formulaic alpha mining seeks to discover executable expressions that capture predictive market signals in a vast, noisy search space. Recent LLM-based

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ANTE: Anticipate, Then Execute -- Learning Internal World Models for Coding Agents from Verified Beliefs

DGX agent

arXiv:2610.15368v1 Announce Type: new Abstract: Rapid advances in large language models have made software engineering agents capable of tackling complex coding tasks. Within a containerized environme

safetyarxiv-cs-lg

14 Oct 2026

Anticipatory Textile MMG-Strain Sensing for Confirm-Then-Assist Soft Knee Exosuits

arXiv:2610.15563v1 Announce Type: new Abstract: Soft wearable robots must commit to assistance around movement onset, when the available signals are ambiguous. Surface electromyography (sEMG) detects

AnyRank: Reliable Anytime Policy Ranking via Sequential Testing

arXiv:2610.14603v1 Announce Type: new Abstract: Due to the high cost of hardware experiments, robot policies are predominantly evaluated by comparing their empirical performance on a limited number of

ARMADA: Autonomous Online Failure Detection and Human Shared Control Empower Scalable Real-world Deployment and Adaptation

arXiv:2510.02298v2 Announce Type: replace Abstract: Imitation learning has shown promise in learning from large-scale real-world datasets. However, pretrained policies usually perform poorly without s

ATOM: Abstract Transition and Option Modeling - Experience-Informed Hierarchical Navigation

arXiv:2610.17555v1 Announce Type: new Abstract: We propose ATOM (Abstract Transition and Option Modeling), a hierarchical navigation framework that combines higher-level graph planning with reusable l

Auditing LLM Program Search for Bermudan Stopping with Primal--Dual Certificates

arXiv:2610.14063v1 Announce Type: new Abstract: LLM program-search agents are usually judged by the score that drives their search. We study LLM-driven search over Python exercise policies for Bermuda

Axiom-0: Hierarchical Self-Refinement for Vision-Language-Action Policies

arXiv:2610.14155v1 Announce Type: cross Abstract: Reliable manipulation requires policies that generalize across tasks, layouts, and object interactions. Vision-language-action (VLA) models combine pr

Barrier-Constrained Guidance For Real-time Asynchronous VLA Execution

arXiv:2610.14997v1 Announce Type: new Abstract: While asynchronous execution enables VLA models to operate in real-time environments by predicting the next chunk during execution, it inevitably introd

Before We Act: Learning Action-Conditioned Team-Response Beliefs for Decentralized Multi-Robot Manipulation

arXiv:2610.14551v1 Announce Type: new Abstract: In multi-robot manipulation, an action's value depends on how teammates respond. We present method{} (Cooperative Action--Response Belief Estimation), a

Belief Embedding Tree Search

arXiv:2610.15046v1 Announce Type: cross Abstract: Beliefs in a Partially Observable Markov Decision Process (POMDP) are posterior distributions over the hidden state given the agent's history of actio

Beyond Fixed Visual History: Learning Motion-Adaptive Temporal Receptive Fields for Complex-Terrain Quadruped Locomotion

arXiv:2610.14753v1 Announce Type: new Abstract: Versatile quadruped locomotion depends on a tight coupling between what the robot perceives and what its body can execute. Yet existing visual controlle

Beyond Gate Weights: Decision-Level Interpretation of Radiomics-Image Feature Fusion for RCC Classification

arXiv:2610.14152v1 Announce Type: cross Abstract: Gate coefficients describe how a fusion layer weights intermediate features, but they do not necessarily reflect the magnitude of branch contributions

Beyond Policy Rollouts: Embedding-Space Directional Exploration with Anchored Noise for LLM RLVR

arXiv:2610.16105v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) typically uses small rollout groups per prompt, where repeated sampling from the same policy can y

BGC-Nav: Cross-Altitude Multi-UAV Collaborative Navigation via Belief-Guided Coupling

arXiv:2610.14702v1 Announce Type: new Abstract: Despite recent advances in Aerial Vision-and-Language Navigation (VLN), single Unmanned Aerial Vehicle (UAV) search-and-rescue in outdoor scenarios rema

Bias Robustness of Genetic Multi-Criteria Sorting versus Learned Rankers under Proxy Skew and Label Contamination

arXiv:2610.14015v1 Announce Type: cross Abstract: Learning-to-rank (LTR) models can align rankings with undesignated signals when those signals appear as features or leak into training labels. We comp

Box-WAM: Box Trajectories as Compact State Representations for World Action Models

arXiv:2610.17526v1 Announce Type: new Abstract: World action models (WAMs) are a category of policy models that ex- plicitly predict both actions and future world states. They have been shown to outpe

Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling

arXiv:2610.14913v1 Announce Type: cross Abstract: Reinforcement learning (RL) is essential for adaptive decision-making, e.g., robust Meta-RL and domain randomization (DR), and efficient post-training

C^2LUE: Clinically Aligned Patient-Level Ultrasound Diagnosis via Evidence-Aware Multi-Instance Learning

arXiv:2610.14143v1 Announce Type: cross Abstract: Patient-level ultrasound diagnosis demands not only high predictive accuracy but also clinically aligned mitigation of asymmetric diagnostic risks. Wh

C{AE}NL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control

arXiv:2610.17509v1 Announce Type: cross Abstract: Active learning changes the representations used to acquire subsequent labels. We study this interaction through Collapse-Based Active Neural Learning

CAG-WAM: Causal Action-Gain World Action Models for Robot Manipulation

arXiv:2610.14720v1 Announce Type: new Abstract: World Action Models (WAMs) combine action generation with future prediction, allowing predicted futures to support action evaluation and robot decision

Calibrated Disambiguation for Partial Multi-label Learning

arXiv:2610.17635v1 Announce Type: cross Abstract: Partial multi-label learning (PML) aims to train a classifier on datasets whose instances are over-annotated with not only relevant labels but also ir

CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance

arXiv:2610.14936v1 Announce Type: cross Abstract: Diffusion policies are increasingly used in robotic control and manipulation, vision-language-action models, and world-model-based agents. Their abili

Characterizing and Mitigating Distributional Bias in Physics-Informed Generative Models

arXiv:2610.16582v1 Announce Type: new Abstract: Physics-informed generative models augment a data-fitting objective with residual penalties that encourage physical constraints to hold in generated sam

Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR

arXiv:2610.17824v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a principal recipe for improving the reasoning of large language models (LLMs). It rest

Chunk-wise Model-based Policy Optimization

arXiv:2610.14564v1 Announce Type: cross Abstract: Model-based reinforcement learning has shown superior sample efficiency by leveraging the learned latent dynamics either in the form of derivative-fre

ClearNav: Perception-Aligned Quadrotor Flight Using Differential-Physics

arXiv:2610.17419v1 Announce Type: cross Abstract: Autonomous quadrotor flight in cluttered environment is an key capability for tasks like inspection and search. Navigation policies trained in simulat

Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI

arXiv:2506.15468v2 Announce Type: replace-cross Abstract: We propose co-creative learning as a novel paradigm where humans and AI, i.e., biological and artificial agents, mutually integrate their part

Collaborative Planning for UAV-UGV Teams under Operational Constraints via Masked Error-bounded Neuro-Symbolic Diffusion

arXiv:2610.18850v1 Announce Type: new Abstract: Collaborative planning under operational constraints is essential for heterogeneous robot teams in large-scale missions. Unmanned Aerial Vehicles (UAVs)

COMPASS: Contextual Moment Prior Alignment for Generalizable AIGI Detection

arXiv:2610.18034v1 Announce Type: new Abstract: Generalizable AI-generated image (AIGI) and deepfake detection remain challenging due to the asymmetry phenomenon, where adaptation to limited training

Compositional Average Velocity Modeling for Efficient Diffusion Offline Reinforcement Learning

arXiv:2610.14941v1 Announce Type: new Abstract: Diffusion-based policies have recently emerged as a powerful paradigm for offline reinforcement learning, but their reliance on multi-step denoising int

Compositional RL: Evaluating and Training LLMs for Collaborative Reasoning

arXiv:2610.17456v1 Announce Type: cross Abstract: Language models are often evaluated alone but increasingly deployed together, routed between, made to debate, ensembled, or collaboratively trained. F

Constrained Diffusion Models with Primal-Dual Inference

arXiv:2606.17192v2 Announce Type: replace Abstract: This paper develops constrained diffusion models with primal-dual inference (PDI) to sample from optimal distributions of entropy-regularized optimi

Constraint-Decomposed Impedance Control from Generative Policy for Articulated Object Manipulation

arXiv:2610.16316v1 Announce Type: new Abstract: Articulated object manipulation tasks such as opening doors, tightening bolts, and pulling drawers impose kinematic constraints on a robot end-effector'

Controllable Diverse Sampling through Pullback Directions

arXiv:2610.14294v1 Announce Type: cross Abstract: Conditional diffusion models can generate high-quality images that align closely with a given prompt. However, samples generated from different random

Cross-Material Magnetic Core Loss Prediction via Operating-Condition Matching and Partial Optimal Transport

arXiv:2610.14964v1 Announce Type: new Abstract: Cross-material magnetic core loss prediction requires adapting to new material responses with limited measurements. Global feature alignment overlooks d

Cross-Subject and Cross-Montage EEG Transfer Learning via Individual Tangent Space Alignment and Spatial-Riemannian Feature Fusion

arXiv:2508.08216v2 Announce Type: replace Abstract: Personalised music-based interventions offer a powerful means of supporting motor rehabilitation by dynamically tailoring auditory stimuli to provid

CrossPortrait: Cross-Species Portrait Animation via Adversarial Motion Alignment

arXiv:2610.17532v1 Announce Type: new Abstract: Cross-species portrait animation, which aims to animate a source portrait of species A using a driving video of species B, is particularly challenging d

Data-Centric Recursive Self-Improvement for Navigation via Policy-Synchronized Language-Action Dual Bootstrapping

arXiv:2610.15782v1 Announce Type: new Abstract: Vision-language navigation requires agents to follow natural-language instructions to reach target locations, making high-quality language supervision e

Data-Free Continual Learning for World Action Model with Rectified Generative Replay

arXiv:2610.18038v1 Announce Type: cross Abstract: World action models (WAMs) built on pretrained video generation models adapt quickly to new robotic tasks, but they forget previously learned tasks un

DEFlow: Deep Equilibrium Flow Maximum-Entropy Reinforcement Learning

arXiv:2610.16597v1 Announce Type: new Abstract: Expressive flow policies can represent multiple high-value actions, but sampling them with few integration steps while retaining tractable action densit

Delta Flow Matching for Robot Foundation Models

arXiv:2610.15794v1 Announce Type: new Abstract: Flow matching is a common generative recipe for robot foundation models. Vision-language-action models (VLAs) generate action chunks, while world-action

Density-Matrix Reinforcement Learning

arXiv:2610.17214v1 Announce Type: new Abstract: What information does a reinforcement learning policy omit? At each state, a standard policy stores one probability per action. It has no place to recor

Dex4D: Task-Agnostic Point Track Policy for Zero-Shot Dexterous Manipulation

arXiv:2602.15828v2 Announce Type: replace-cross Abstract: Learning generalist policies capable of accomplishing a wide range of everyday tasks remains an open challenge in dexterous manipulation. In p

DexCast: Learning Real-World Dexterous Control from Human Videos

arXiv:2610.14691v1 Announce Type: new Abstract: Recent advances in 3D hand-object interaction (HOI) perception have opened new opportunities for learning dexterous robotic skills from human videos. Ho

DexFLEX: Contact-Aware Foundation Controller for Command-Guided Dexterity

arXiv:2610.14372v1 Announce Type: cross Abstract: Dexterous robot hands can receive useful motion intent from teleoperation or learned policies, but successful execution also depends on contact decisi

DexNinja: Learning Robust Dexterous Cutting Policy with a Real-to-Sim-to-Real Data Engine

arXiv:2610.17208v1 Announce Type: cross Abstract: Cutting a piece of food looks effortless in human hands. For a robot, however, every cut is a high-contact interaction with a deformable, elasto-plast

DexStitch: Learning and Composing Dexterous Skills from Human Videos via Contact-Aware Robotic Inpainting

arXiv:2610.17789v1 Announce Type: cross Abstract: A long-horizon manipulation task consists of primitive skills and the transitions connecting them. Learning skills that can be executed in sequence re