Search: “safety”

TreeSpark: Calibrated, Load-Adaptive Draft Trees for Semi-Autoregressive Speculative Decoding

arXiv:2609.22098v1 Announce Type: new Abstract: Speculative decoding accelerates language-model inference by letting a cheap drafter propose tokens that the target model verifies in parallel. Recent b

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UniPoint: Unified Point-Level Sensor Fusion for Humanoid Locomotion Across Challenging Terrains

DGX agent

arXiv:2609.23666v1 Announce Type: new Abstract: Open-world deployment requires humanoid robots to cross highly heterogeneous terrain safely, with perception that simultaneously provides wide coverage,

safetyarxiv-cs-ro

22 Sept 2026

Unlocking Pretrained Vision Transformers for Time Series Classification

arXiv:2506.08641v3 Announce Type: replace-cross Abstract: Adapting vision models for time series analysis is compelling, yet all existing approaches are falling short of dedicated time series foundati

Uranus: Building the Next-Generation Simulation Infrastructure for Embodied AI

arXiv:2609.24815v1 Announce Type: new Abstract: Scalable simulation is essential for robot data generation, policy training, evaluation, and safe iteration, yet real-world interaction is costly and co

Verti-WM: A Physics-Aided Exteroceptive World Model for Off-Road Reinforcement Learning

arXiv:2609.23118v1 Announce Type: new Abstract: Reinforcement learning for off-road navigation requires extensive vehicle-terrain interaction data, which are costly to collect in high-fidelity simulat

VISTA: Video-Injected Stylized Text-to-Animation

arXiv:2609.23817v1 Announce Type: new Abstract: We present VISTA, a two-stage framework for generating stylized 3D human motion by fusing structural content from text prompts with expressive style fro

Visuomotor Robotic Pruning in Planar Orchards Using Hybrid Reinforcement Learning

arXiv:2609.24906v1 Announce Type: new Abstract: Dormant tree pruning is labor-intensive yet essential for maintaining modern high-productivity fruit orchards. In this work, we focus on pruning of mode

vla.simd: Efficient CPU Inference for Language-Conditioned Manipulation

arXiv:2609.24274v1 Announce Type: new Abstract: Deploying language-conditioned manipulation without a dedicated GPU requires efficient inference and action chunks that cover the delay between policy q

VPRune: Efficient Training-free Pre-LLM Visual Token Pruning

arXiv:2609.24485v1 Announce Type: new Abstract: Visual token pruning is a promising approach to reducing the inference cost of large vision-language models (LVLMs), yet aggressive token reduction ofte

VRWKV-Editor: Reducing quadratic complexity in transformer-based video editing

arXiv:2509.25998v4 Announce Type: replace Abstract: In light of recent progress in video editing, deep learning models focusing on both spatial and temporal dependencies have emerged as the primary me

What are Key Factors for Updates in RL for LLM Reasoning?

arXiv:2606.22570v2 Announce Type: replace Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a promising framework for enhancing the reasoning ability of large language mod

What do VLM-Based Vision-Language Navigation Models Rely on: Interpreting and Steering Policy Behavior

arXiv:2609.24576v1 Announce Type: cross Abstract: Modern Vision-Language Navigation (VLN) models rely mostly on pre-trained large Vision-Language Models (VLMs) to predict navigation actions. While thi

When and Why Do Linear Bias Probes Fail? A Geometric and Statistical Theory of Bias Detectability in Large Language Model Representations

arXiv:2609.22337v1 Announce Type: cross Abstract: Linear probing is the standard instrument for detecting social biases in the hidden representations of large language models. Yet reported probe accur

When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Reads

arXiv:2609.22299v1 Announce Type: cross Abstract: When a robot faces unfamiliar physical conditions, a common approach is to collect evidence about what changed and adapt. For such diagnosis to improv

When Residualization Helps an Audit: Format Effects, Slice Gains, and Their Limits

arXiv:2609.24194v1 Announce Type: new Abstract: Evaluation scores used around LLM systems -- including reward models, rerankers, and LLM judges -- can track surface form instead of the quality they cl

When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting

arXiv:2609.24862v1 Announce Type: new Abstract: Agentic time series forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models, reasoning st

Whole-Body UMI: Transferring UMI Manipulation Skills to Humanoid Whole-Body Manipulation via Real-Time Motion Generation

arXiv:2609.22829v1 Announce Type: new Abstract: Collecting whole-body demonstrations for humanoid manipulation mostly relies on teleoperation, which is costly and hard to scale up. The Universal Manip

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory and In-Context Learning

arXiv:2607.18840v2 Announce Type: replace Abstract: World Action Models (WAMs) offer a promising paradigm for robotic manipulation by jointly modeling visual state transitions and robot actions. Howev

2nd Place Solution to the HANDS 2026 Workshop Challenge-Dexterous Grasp Motion Track: Single-Shot Trajectory Warping for Grasp Motion Generation

arXiv:2609.21511v1 Announce Type: cross Abstract: This report describes our 2nd place solution to the HANDS 2026 workshop challenge (Dexterous Grasp Motion track) in conjunction with ECCV 2026. In thi

A Generative Grammar Underlying the Voynich Manuscript, the Pastiche Hypothesis: Evidence from Large Language Models

arXiv:2609.20835v1 Announce Type: new Abstract: Background: The Voynich Manuscript is a fifteenth-century codex written in an unknown script whose content remains undeciphered. Previous studies sugges

A Sim-to-Real Integration Pipeline for Training and Deployment of Chunk-Based VLA Manipulation Policies

arXiv:2609.21817v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become a prominent paradigm for mapping multimodal inputs, including semantic instructions, visual observations

Ability-Residual Decoupled Modeling for Affective Cognitive Diagnosis

arXiv:2609.21214v1 Announce Type: new Abstract: Cognitive diagnosis infers students' concept mastery from response logs. However, students' responses are not determined by mastery alone: non-cognitive

AcousticDiffusion: Semantically Conditioned Audio-Guided Diffusion Policy for Search-and-Rescue Assistance

arXiv:2609.21792v1 Announce Type: new Abstract: Navigating toward human callers is an important capability for rescue robots operating where visual contact is degraded or occluded. We present Acoustic

Adaptive Rollout Truncation Based on Epistemic Uncertainty for Efficient Offline World Model Training

arXiv:2609.21482v1 Announce Type: cross Abstract: Accurate neural world models are central to model-based robotics, where they enable robots to predict future states from previously observed trajector

Aligning with Lived Experience: Heterogeneous Benefits of Fine Tuning in Mental Health Support Generation

arXiv:2609.21075v1 Announce Type: cross Abstract: As access to professional mental healthcare remains limited, many individuals turn to online platforms such as Reddit to seek peer support situated wi

ArenaFlow: From Trajectory Ranking to Hierarchical Credit Propagation for Open-Ended Agent RL

arXiv:2609.21378v1 Announce Type: new Abstract: Reinforcement learning has substantially improved large language model (LLM) agents in verifiable domains, but remains difficult to apply to open-ended

ASGARD: Action-Space Guard for UAV Resilience via Reinforcement Learning

arXiv:2609.20982v1 Announce Type: new Abstract: Reinforcement learning (RL) controllers have been recently adopted for Unmanned Aerial Vehicles (UAV) navigation and control. However, they are suscepti

Local Ai

Assessment of Machine Learning-Based Critical Heat Flux Models in the CTF Subchannel Code for Square Rod Bundle Prediction

arXiv:2609.21995v1 Announce Type: new Abstract: The prediction of critical heat flux (CHF), a key safety-related quantity in nuclear thermal hydraulics, remains an important challenge due to its direc

local-aiarxiv-cs-lg

AtomEgo: Exploring Ego-Robot Integration for Embodied Foundation Model Pretraining

arXiv:2609.21461v1 Announce Type: cross Abstract: Embodied foundation models are constrained by the limited scale and diversity of robot demonstrations, motivating the use of large-scale egocentric hu

Benchmarking Gender Bias in Machine Translation Evaluation Metrics across Occupations

arXiv:2609.21490v1 Announce Type: new Abstract: Gender bias remains a persistent concern in machine translation (MT), affecting both generated translations and their automatic evaluation. When a sourc

Beyond Exact Match: Task-Aware GRPO for Cross-Domain PCBA Visual Question Answering

arXiv:2609.21276v1 Announce Type: cross Abstract: In automated Printed Circuit Board Assembly (PCBA) inspection, standards-guided decisions require systems to jointly reason over fine-grained visual c

Beyond Gaussian Worlds: Latent Geometry Matters for JEPAs

arXiv:2609.21656v1 Announce Type: new Abstract: Recent Joint-Embedding Predictive Architectures (JEPAs) prevent representation collapse by constraining learned representations to follow a prescribed t

Beyond Kinematics: Benchmarking Simulation Fidelity for Muscle-Driven Imitation Learning

arXiv:2609.21909v1 Announce Type: new Abstract: In this work, we conduct a systematic comparison of two state-of-the-art motion-imitation reinforcement learning (MIRL) pipelines, one built on SCONE/Hy

Calibrating Teacher--Student Discrepancy for On-Policy Distillation

arXiv:2609.21619v1 Announce Type: new Abstract: On-policy distillation (OPD) improves reasoning models by learning the token-level discrepancy between a stronger teacher and an on-policy student. Howe

CARF: Contrastive Attraction-Repulsion of Failure-Guided Flow Matching

arXiv:2609.21982v1 Announce Type: new Abstract: Robot demonstration collection often produces imperfect or failed trajectories in addition to successful demonstrations. Existing methods typically expl

CIPL: A Channel-Aware Framework for Recoverable Privacy Leakage in LLM Agents

arXiv:2609.21686v1 Announce Type: cross Abstract: Privacy leakage in LLM agents is commonly evaluated within individual components such as memory, retrieval, or tool-use pipelines, which makes it diff

Classification-oriented adaptive sensing via posterior sampling

arXiv:2609.21812v1 Announce Type: cross Abstract: Recent advances in diffusion models have enabled high-performance, instance-adaptive compressed sensing through posterior sampling, without task-speci

Collab-Solver: Collaborative Solving Policy Learning for Mixed-Integer Linear Programming

arXiv:2508.03030v3 Announce Type: replace Abstract: Mixed-integer linear programming (MILP) has been a fundamental problem in combinatorial optimization. Conventional MILP solving mainly relies on car

Complex Problem Solving in Large Language Models: A Statistical Control Survey and Diagnostic Framework

arXiv:2609.20973v1 Announce Type: cross Abstract: Complex problem solving (CPS) with large language models (LLMs) is often framed as a matter of stronger reasoning or longer generation. Yet early-step

Continuous Delayed-Memory Stochastic Gradient Descent and Continuous-Time Reinforcement Learning from History of Astrophysical Time Series Studies

arXiv:2609.20906v1 Announce Type: new Abstract: Quasars are luminous objects in the universe that exhibit stochastic brightness variations encoding information about the supermassive black holes power

Deep Reinforcement Learning with Buffered Quantile Objectives

arXiv:2609.21327v1 Announce Type: cross Abstract: Quantile-based reinforcement learning provides an interpretable approach to risk-sensitive decision-making by optimizing a prescribed quantile of the

Demonstration Synthesis from a Single Scan via Gaussian Splatting for Visuomotor Policy Learning

arXiv:2609.21112v1 Announce Type: new Abstract: Training a visuomotor policy calls for abundant demonstrations that closely match the target environment, yet collecting them anew remains expensive. Ex

DEXTERA: From a Single Image to Deployable Dexterous Manipulation via Real-to-Sim-to-Real

arXiv:2609.21045v1 Announce Type: new Abstract: Collecting real-world robot data for dexterous manipulation is costly and time-consuming. While high-fidelity physics simulators enable scalable data sy

Diverse and Adaptable Arm Coordination for Octopus-Crawling via Diffusion-Based Uncertainty-Aware Optimization

arXiv:2609.21138v1 Announce Type: cross Abstract: Octopus crawling motivates soft robots that exploit redundancy, yet discovering and organizing diverse coordination modes for adaptation remains chall

Dual-Interest Sequential Product Recommendation With Multi-Granular SSM

arXiv:2609.21548v1 Announce Type: new Abstract: Sequential recommendation aims to predict the next item a user will interact with based on their historical behavior. Advances in Transformers have sign

Dynamic Lagging using Stable-Prefix Training for Simultaneous Translation

arXiv:2609.05799v2 Announce Type: replace Abstract: In streaming simultaneous speech translation, the speech translation system is trained to learn a read-write policy that alternates between consumin

ExpBoN: Exponential-Noise Best-of-n for Efficient Test-Time LLM Alignment

arXiv:2609.21899v1 Announce Type: new Abstract: Best-of-n (BoN) sampling is a simple yet effective inference-time alignment method, but hard maximization provides only coarse control over the trade-of

FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift

arXiv:2609.21057v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training without sharing raw data, but its performance degrades under non-IID data and stochastic cl