Search: “safety”

How to Train a Critic Stably and Efficiently

arXiv:2608.23566v1 Announce Type: cross Abstract: Group-based reinforcement learning methods such as GRPO for large language models avoid training a critic by sampling multiple responses for each prom

Hybrid Panels: Toward Human-AI Collaboration in Survey Research

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arXiv:2608.22582v1 Announce Type: cross Abstract: Large-scale population surveys are essential for generating robust social and scientific insights, yet they face significant challenges, including dec

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25 Aug 2026

Hyper^2: Unleashing Hyperbolic Geometry's Full Potential via Dual-Space Consistency

arXiv:2608.22238v1 Announce Type: new Abstract: HyperbolicCD pioneered hyperbolic geometry for point cloud completion by replacing the Euclidean Chamfer distance with arcosh(1+alpha||x-y||^2), but the

LagrangeGS: Non-Conservative Lagrangian System on Dynamic 3D Gaussian Splatting

arXiv:2608.22773v1 Announce Type: new Abstract: Dynamic 3D Gaussian Splatting (3DGS) achieves photorealistic reconstruction of time-varying scenes, and recent physics-aware extensions improve extrapol

Language Chain in Alignment: Cross-Lingual Ranking Preference Optimization

arXiv:2608.23149v1 Announce Type: cross Abstract: The alignment of Large Language Models heavily relies on English-centric high-quality preference data, which often leads to suboptimal performance in

LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models

arXiv:2608.22403v1 Announce Type: new Abstract: Human video is playing an increasingly central role in training World Action Models (WAMs), owing to its diversity and low collection cost relative to t

Learning Implicit Constitutive Laws for Dynamic 3D Gaussian Splatting from Monocular Videos

arXiv:2608.22102v1 Announce Type: cross Abstract: We present GCA (Gaussian Constitutive Alignment), a framework for learning implicit constitutive laws from monocular dynamic video of deformable objec

Learning in PINNs: Phase transition, diffusion equilibrium, and generalization

arXiv:2403.18494v2 Announce Type: replace Abstract: We investigate the learning dynamics of fully-connected neural networks through the lens of the neural gradient signal-to-noise ratio (SNR), examini

Learning to Control Coupled-Dynamics Environments with Joint Markov Decision Processes

arXiv:2608.22765v1 Announce Type: new Abstract: Coupled-dynamics environments expose the one-step outcomes that would follow from several possible counterfactual actions under a common realization of

Let the Bullets Fly: Multimodal Fake News Detection with Temporal-Aligned Generative Danmaku

arXiv:2608.22832v1 Announce Type: new Abstract: The social interactions among crowds via extit{Danmaku} (a.k.a., bullet comments) on modern multimedia platforms can facilitate both viewpoint conflicts

LitReview Arena: Evaluating Literature Review Agents with Battle-Style Peer Review Platform

arXiv:2608.21374v1 Announce Type: new Abstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspect

Long-Horizon Audio-Visual Generation for Persistent Stories and Interactive Worlds

arXiv:2608.23383v1 Announce Type: new Abstract: Video generation is progressing beyond isolated clips toward long-form narratives and interactive worlds, requiring models to preserve identities, follo

MCP-Universe RL: A Framework for Training MCP Tool-Use Agents via Reinforcement Learning

arXiv:2608.22167v1 Announce Type: new Abstract: Reinforcement learning (RL) has become an effective way to improve the tool-use ability of large language models (LLMs), but most existing RL frameworks

Memory-Enhanced Neural Solvers for Routing Problems

arXiv:2406.16424v4 Announce Type: replace Abstract: Routing Problems are central to many real-world applications, yet remain challenging due to their (NP-)hard nature. Amongst existing approaches, heu

Meta-Moderator: Empowering Multi-Agent Debate with Meta-Cognition

arXiv:2608.23029v1 Announce Type: new Abstract: Multi-agent debate can improve large language model reasoning by eliciting diverse hypotheses and critiques, yet its performance is often constrained by

Mitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decoding

arXiv:2608.21415v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases from

Mitigating Sample-Level Imbalance via Probabilistic Separation for Adaptive Multimodal Fusion

arXiv:2510.21797v4 Announce Type: replace-cross Abstract: Multimodal learning faces modality imbalance, where dominant modalities suppress weaker ones due to inconsistent convergence rates. Existing s

Mitigating Speaker Leakage in Cascaded Multi-talker ASR with Diarization-based Transcript Correction

arXiv:2608.22196v1 Announce Type: cross Abstract: While cascaded multi-talker ASR (MT-ASR) leverages state-of-the-art foundation models, its performance is often capped by speaker leakage during separ

Mode-Dependent Rectification for Stable PPO Training

arXiv:2602.05619v2 Announce Type: replace-cross Abstract: Mode-dependent architectural components (layers that behave differently during training and evaluation, such as Batch Normalization or dropout

Model-Based Reinforcement Learning for Heterogeneous Multi-Robot Task Assignment Under Distribution Shifts

arXiv:2608.21554v1 Announce Type: cross Abstract: Heterogeneous multi-robot service systems must assign requests to compatible robots, construct feasible schedules, and adapt as new tasks arrive onlin

More Experts, Worse Dynamics: Inverse Scaling and Spectral Bias in Mixture-of-Experts State-Space Models

arXiv:2608.21840v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures are commonly motivated as a way to increase expressivity by decomposing complex systems into simpler local dynami

MorFiC: Fixing Value Miscalibration for Zero-Shot Quadruped Transfer

arXiv:2603.14554v2 Announce Type: replace Abstract: Generalizing learned locomotion policies across quadrupedal robots with different morphologies remains a challenge. Policies trained on a single rob

MorphoCLIP: Text-Supervised Contrastive Learning for Perturbation Matching in Cell Painting Images

arXiv:2608.22690v1 Announce Type: new Abstract: Cell Painting microscopy captures how cells change after a chemical or genetic perturbation. Connecting these images to the perturbations that produced

MOSH-WM: Mask-Grounded Soft-Hamiltonian Dynamics for Object-Centric World Models

arXiv:2608.22750v1 Announce Type: new Abstract: Object-centric world models forecast future videos by evolving a set of entity slots, but the variables receiving dynamics supervision are often unconst

Motion-Aware Vision-Reference Alignment for Referring Multi-Object Tracking

arXiv:2511.17681v2 Announce Type: replace Abstract: Referring Multi-Object Tracking (RMOT) extends conventional multi-object tracking (MOT) by introducing natural language references for multi-modal f

ODG-NoMaD: Overhead-Camera Direction-Guided NoMaD

arXiv:2608.21395v1 Announce Type: cross Abstract: NoMaD [31] is a learned vision-navigation policy that unifies goal-conditioned navigation and exploration in a single goal-masked diffusion policy. In

On the Capability Separation Between World-Model Policy Learning and Imitated World-Action Models

arXiv:2608.22197v1 Announce Type: new Abstract: World-action models predict a future outcome and then infer an associated action. Although this factorization can improve representation learning and da

On the Optimized Use of Non-Orthonormality Constraints for the Quasi-Static INS Alignment of Autonomous Underwater and Surface Vehicles

arXiv:2608.21390v1 Announce Type: new Abstract: Inertial navigation systems are specialized navigation apparatuses that equip almost all autonomous underwater and surface vehicles. They require precis

Optimal Transport for Handwritten Text Recognition in a Low-Resource Regime

arXiv:2509.16977v2 Announce Type: replace Abstract: Handwritten Text Recognition (HTR) is a task of central importance in the field of document image understanding. State-of-the-art methods for HTR re

Personalized and Aspiration-Oriented Career Path Recommendation

arXiv:2608.22056v1 Announce Type: new Abstract: Fulfilling career aspirations is important for growth of employee and organization. We propose a data driven methodology to recommend personalized caree

Perturb the Thought, Not the Pixels: Latent-Space Rollout Diversification for Reinforcement Learning of Vision-Language Models

arXiv:2608.21595v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning ability of vision-language models (VLMs), and diversifying the rollouts wit

Pointing-VLA: Typed Spatial Grounding Interfaces for Vision-Language-Action Manipulation

arXiv:2608.23138v1 Announce Type: cross Abstract: Vision-language-action (VLA) models often expose spatial grounding through autoregressive text coordinates or opaque action tokens, creating brittle i

Posterior Information Dynamics of Diffusion Models for Linear Inverse Problems

arXiv:2608.21709v1 Announce Type: new Abstract: Diffusion models are widely used as priors for linear inverse problems, yet endpoint quality does not reveal when measurement information enters reverse

Privileged Critic Training Enables Sensor-Free Thruster Fault Adaptation in End-to-End RL

arXiv:2608.22976v1 Announce Type: new Abstract: Fault-tolerant navigation for thruster-actuated robots requires online adaptation to failures that are neither binary nor fully observable: thrusters ma

Q-Learning with Stable Infinite-Dimensional Linear Function Approximation

arXiv:2608.22636v1 Announce Type: new Abstract: Q-learning with linear function approximation can be unstable because an arbitrary approximation architecture need not preserve the Bellman contraction.

QIRL: Optimized Question-Image Relation Learning for Bias-Robust Visual Question Answering

arXiv:2504.03337v2 Announce Type: replace Abstract: Existing bias mitigation methods for Visual Question Answering (VQA), a typical Artificial intelligence application, endure two main limitations. Fi

RAG Collapse: LLM Responses Collapse When Retrieved Documents Are Self-Authored

arXiv:2608.22118v1 Announce Type: new Abstract: LLM responses are based on the internet (via training or RAG), and AI is now used to generate a significant amount of content online (Paredes et al., 20

ReART: Reference-Guided Retrieval and Refinement for Emotion-Aware Art Generation

arXiv:2608.22329v1 Announce Type: cross Abstract: Emotion-aware artistic image generation requires a model to satisfy semantic content, artistic style, and target emotion simultaneously. The key chall

Reasoning Meets Personalization: Unleashing the Potential of Large Reasoning Model for Personalized Generation

arXiv:2505.17571v2 Announce Type: replace Abstract: Personalization is a critical task in modern intelligent systems, with applications spanning diverse domains, including interactions with large lang

Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary

arXiv:2603.06813v2 Announce Type: replace Abstract: In a stationary decentralized Markov game, learning peers generate an episode-indexed sequence of induced MDPs for any focal agent. The joint game r

Reward-Free Continual Adaptation for Resilient Space Robots

arXiv:2608.23452v1 Announce Type: cross Abstract: Space robots operate in extreme environments where hardware degradation can critically compromise traditional control strategies. While continual rein

RIACT: A Responsible AI System for Personalized Study Habit Tracking and Early Burnout Signal Detection in University Students

arXiv:2608.21379v1 Announce Type: new Abstract: Student burnout is highly prevalent in higher education, with reported rates ranging from 12% to over 70% and consistently exceeding those of the workin

Risk-Sensitive Reinforcement Learning with Smoothed Quantile Objectives

arXiv:2608.22227v1 Announce Type: new Abstract: Reinforcement Learning (RL) has achieved tremendous success in recent years. However, the classical foundations of RL do not account for the risk sensit

Robust Bimanual Vision-Language-Action Models via Embarrassingly Simple Modality Masking

arXiv:2608.22419v1 Announce Type: cross Abstract: Query-based Vision-Language-Action (VLA) models offer low-latency inference that is attractive for bimanual robotic manipulation, but we observe that

Robustness Analysis of Agentic AI to Inconsistent and Incomplete Tool Responses

arXiv:2608.22676v1 Announce Type: new Abstract: Robustness to a bad tool return means answering it in the way that return calls for, which depends on how the tool went wrong. A tool that has failed an

RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains

arXiv:2602.03002v2 Announce Type: replace Abstract: Humanoid perceptive locomotion has made significant progress and shows great promise, yet achieving robust multi-directional locomotion on complex t

Runtime Action Interference for AI Control of AlphaStar in StarCraft II

arXiv:2608.21398v1 Announce Type: cross Abstract: A trained reinforcement learning policy does not determine the complete behavior that users encounter: deployment code still schedules, admits, suppre

Scaling Curriculum Learning For Autonomous Driving

arXiv:2608.22549v1 Announce Type: new Abstract: Batched simulators for autonomous driving have recently enabled training reinforcement learning (RL) agents at scale, encompassing thousands of traffic