Search: “safety”

Retrieval Is Not Enough: Refreshing Memory for Frozen Time-Series Forecasters

arXiv:2610.07834v1 Announce Type: new Abstract: Retrieval-augmented time-series forecasting uses the continuations of historical segments similar to the current context as references for a forecaster.

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Revar3r: gauge-aware perturbation uncertainty for feed-forward 3d reconstruction

DGX agent

arXiv:2610.07883v1 Announce Type: new Abstract: A correctly reconstructed distant point appears uncertain even when a frozen 3D model processes equivalent inputs because its output frame rotates fract

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7 Oct 2026

Revisiting Numerical Forecasting Models for Language-Based Trajectory Prediction

arXiv:2610.07954v1 Announce Type: new Abstract: Language-based trajectory predictors represent coordinates as discrete tokens and learn auxiliary tasks such as destination and group reasoning. This fo

Revisiting Temporal Regularization for Smooth Control in Deep Reinforcement Learning

arXiv:2610.07910v1 Announce Type: new Abstract: Deep Reinforcement Learning policies can produce nonsmooth action oscillations that hinder deployment on physical robots. Existing architectural and pen

Sample-Optimal Estimation of the Frechet Inception Distance

arXiv:2610.07114v1 Announce Type: cross Abstract: The Frechet Inception Distance (FID) is widely used to evaluate generative models, but its empirical plug-in estimator suffers from finite-sample bias

SanSi: A Looped Typed Decision Model for System 1.5 Thinking

arXiv:2610.07730v1 Announce Type: cross Abstract: Typed decision models answer a declared question without generating text: a decision head returns a probability for each of the declared options in a

Secure Speculative Decoding for Large Language Models

arXiv:2610.08678v1 Announce Type: cross Abstract: Speculative decoding accelerates inference for a large language model (LLM), referred to as the target model, by first using a smaller model, referred

Self-Referenced Social Preferences: Cooperation without Observing Others Rewards

arXiv:2610.07881v1 Announce Type: new Abstract: Social preferences can promote cooperation in multi-agent reinforcement learning, but existing approaches often require agents to observe the rewards of

Self-Retrospection Distillation: Turning Post-hoc Experiences into Prior Foresight

arXiv:2610.08077v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) turns agent experience into learning signals primarily through scalar outcome rewards after intera

Sensitivity Shaping for Latent Modeling

arXiv:2606.14585v2 Announce Type: replace-cross Abstract: Generative dynamics models enable planning in challenging systems, but safe deployment requires detecting policy-induced out-of-distribution (

Sensor-Layout-Agnostic Navigation via Geometric Observation Canonicalization

arXiv:2610.08306v1 Announce Type: new Abstract: Existing visual navigation policies are inherently bound to fixed camera configurations, creating a fundamental barrier to zero-shot deployment across h

SharedKV-BT: Node-Local Typed Decisions for Behavior-Tree Agents

arXiv:2610.07327v1 Announce Type: cross Abstract: Agent tasks require sequences of interdependent decisions. Autoregressive models support more flexible decision interfaces than conventional classifie

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

Sparse2comm: Towards Robust Cooperative 3D Object Detection

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

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

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

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 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

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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

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

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.

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 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

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