Search: “safety”

SatBLIP: Context Understanding and Feature Identification from Satellite Imagery with Vision-Language Learning

arXiv:2604.14373v1 Announce Type: new Abstract: Rural environmental risks are shaped by place-based conditions (e.g., housing quality, road access, land-surface patterns), yet standard vulnerability i

Safety

Scouting By Reward: VLM-TO-IRL-Driven Player Selection For Esports

DGX agent

arXiv:2604.14474v1 Announce Type: new Abstract: Traditional esports scouting workflows rely heavily on manual video review and aggregate performance metrics, which often fail to capture the nuanced de

safetyarxiv-cs-lg

17 Apr 2026

SPAGBias: Uncovering and Tracing Structured Spatial Gender Bias in Large Language Models

arXiv:2604.14672v1 Announce Type: new Abstract: Large language models (LLMs) are being increasingly used in urban planning, but since gendered space theory highlights how gender hierarchies are embedd

Step-level Denoising-time Diffusion Alignment with Multiple Objectives

arXiv:2604.14379v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as a powerful tool for aligning diffusion models with human preferences, typically by optimizing a single rewa

StoryCoder: Narrative Reformulation for Structured Reasoning in LLM Code Generation

arXiv:2604.14631v1 Announce Type: new Abstract: Effective code generation requires both model capability and a problem representation that carefully structures how models reason and plan. Existing app

The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery

arXiv:2604.14176v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) leverages labeled data to categorize unlabeled samples from known or unknown classes. Most previous methods jointly

The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows

arXiv:2604.14807v1 Announce Type: cross Abstract: The rapid integration of large language models (LLMs) into everyday workflows has transformed how individuals perform cognitive tasks such as writing,

The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure

arXiv:2604.14197v1 Announce Type: new Abstract: Large language model (LLM) performance depends heavily on prompt design, yet prompt construction is often described and applied inconsistently. Our purp

“The sharpest drop came from people who used the model for direct answers, not from those who used it more like a hint system, which suggest…

“The sharpest drop came from people who used the model for direct answers, not from those who used it more like a hint system, which suggests the real issue is not AI exposure itself but replacing eff

safetygary-marcus--x

The Specification Trap: Why Static Value Alignment Alone Is Insufficient for Robust Alignment

arXiv:2512.03048v4 Announce Type: replace-cross Abstract: Static content-based AI value alignment is insufficient for robust alignment under capability scaling, distributional shift, and increasing au

To See or To Please: Uncovering Visual Sycophancy and Split Beliefs in VLMs

arXiv:2603.18373v2 Announce Type: replace Abstract: When VLMs answer correctly, do they genuinely rely on visual information or exploit language shortcuts? We introduce the Tri-Layer Diagnostic Framew

Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms

arXiv:2506.09457v3 Announce Type: replace Abstract: Direct Alignment Algorithms (DAAs), such as Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO), have emerged as efficie

Towards Deploying VLA without Fine-Tuning: Plug-and-Play Inference-Time VLA Policy Steering via Embodied Evolutionary Diffusion

arXiv:2511.14178v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have demonstrated significant potential in real-world robotic manipulation. However, pre-trained VLA policies st

safetyarxiv-cs-ro

Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt

arXiv:2604.13715v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) enable general audio understanding and demonstrate remarkable performance across various audio tasks. However, the

safetyarxiv-cs-ai

Towards Trustworthy 6G Network Digital Twins: A Framework for Validating Counterfactual What-If Analysis in Edge Computing Resources

arXiv:2604.14787v1 Announce Type: cross Abstract: Network Digital Twins (NDTs) enable safe what-if analysis for 6G cloud-edge infrastructures, but adoption is often limited by fragmented workflows fro

UniDoc-RL: Coarse-to-Fine Visual RAG with Hierarchical Actions and Dense Rewards

arXiv:2604.14967v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) extends Large Vision-Language Models (LVLMs) with external visual knowledge. However, existing visual RAG systems t

Unsupervised Skeleton-Based Action Segmentation via Hierarchical Spatiotemporal Vector Quantization

arXiv:2604.15196v1 Announce Type: new Abstract: We propose a novel hierarchical spatiotemporal vector quantization framework for unsupervised skeleton-based temporal action segmentation. We first intr

Wasserstein Formulation of Reinforcement Learning. An Optimal Transport Perspective on Policy Optimization

arXiv:2604.14765v1 Announce Type: new Abstract: We present a geometric framework for Reinforcement Learning (RL) that views policies as maps into the Wasserstein space of action probabilities. First,

When Fairness Metrics Disagree: Evaluating the Reliability of Demographic Fairness Assessment in Machine Learning

arXiv:2604.15038v1 Announce Type: cross Abstract: The evaluation of fairness in machine learning systems has become a central concern in high-stakes applications, including biometric recognition, heal

When Missing Becomes Structure: Intent-Preserving Policy Completion from Financial KOL Discourse

arXiv:2604.14333v1 Announce Type: new Abstract: Key Opinion Leader (KOL) discourse on social media is widely consumed as investment guidance, yet turning it into executable trading strategies without

Why Do Vision Language Models Struggle To Recognize Human Emotions?

arXiv:2604.15280v1 Announce Type: new Abstract: Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans. Vision-language models (VLMs) have made trem

Your LLM Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception

arXiv:2510.23853v3 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly used to interact with and execute tasks in dynamic environments. However, a critical yet overlook

A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models

arXiv:2604.13240v1 Announce Type: new Abstract: Mapping the spatial distribution of species is essential for conservation policy and invasive species management. Species distribution models (SDMs) are

A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies

arXiv:2604.13645v1 Announce Type: cross Abstract: Co-training, which combines limited in-domain real-world data with abundant surrogate data such as simulation or cross-embodiment robot data, is widel

A Multi-Model Approach to English-Bangla Sentiment Classification of Government Mobile Banking App Reviews

arXiv:2604.13057v1 Announce Type: new Abstract: For millions of users in developing economies who depend on mobile banking as their primary gateway to financial services, app quality directly shapes f

A 'race' implies that it is something that is worth 'winning', which is not the case for superintelligence. Whoever finished first is just t…

A 'race' implies that it is something that is worth 'winning', which is not the case for superintelligence. Whoever finished first is just the first to trigger irrecoverable catastrophe. 'There is no

safetyconnor-leahy--x

Action Images: End-to-End Policy Learning via Multiview Video Generation

arXiv:2604.06168v2 Announce Type: replace Abstract: World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model t

Activation-Guided Local Editing for Jailbreaking Attacks

arXiv:2508.00555v2 Announce Type: replace-cross Abstract: Jailbreaking is an essential adversarial technique for red-teaming these models to uncover and patch security flaws. However, existing jailbre

Ad firms settle with Trump FTC over claims they boycotted conservative media

Three major advertising companies—Dentsu, Publicis, and WPP—settled with the Federal Trade Commission over allegations they colluded on anti-misinformation policies that reduced ad revenue for conserv

safetyars-technica

ADP-DiT: Text-Guided Diffusion Transformer for Brain Image Generation in Alzheimer's Disease Progression

arXiv:2604.13495v1 Announce Type: new Abstract: Alzheimer's disease (AD) progresses heterogeneously across individuals, motivating subject-specific synthesis of follow-up magnetic resonance imaging (M

Alignment as Institutional Design: From Behavioral Correction to Transaction Structure in Intelligent Systems

arXiv:2604.13079v1 Announce Type: cross Abstract: Current AI alignment paradigms rely on behavioral correction: external supervisors (e.g., RLHF) observe outputs, judge against preferences, and adjust

Beyond Arrow's Impossibility: Fairness as an Emergent Property of Multi-Agent Collaboration

arXiv:2604.13705v1 Announce Type: new Abstract: Fairness in language models is typically studied as a property of a single, centrally optimized model. As large language models become increasingly agen

Beyond State Consistency: Behavior Consistency in Text-Based World Models

arXiv:2604.13824v1 Announce Type: new Abstract: World models have been emerging as critical components for assessing the consequences of actions generated by interactive agents in online planning and

Bias-Corrected Adaptive Conformal Inference for Multi-Horizon Time Series Forecasting

arXiv:2604.13253v1 Announce Type: new Abstract: Adaptive Conformal Inference (ACI) provides distribution-free prediction intervals with asymptotic coverage guarantees for time series under distributio

Biased Federated Learning under Wireless Heterogeneity

arXiv:2503.06078v2 Announce Type: replace Abstract: Federated learning (FL) has emerged as a promising framework for distributed learning, enabling collaborative model training without sharing private

Can people please stop taking shots at @sama, and boycott his company instead? Violence is not the answer.

Can people please stop taking shots at @sama, and boycott his company instead? Violence is not the answer. This is really bad. The scary part in the US is that it doesn’t matter whether you are the CE

safetygary-marcus--x

CausalDisenSeg: A Causality-Guided Disentanglement Framework with Counterfactual Reasoning for Robust Brain Tumor Segmentation Under Missing Modalities

arXiv:2604.13409v1 Announce Type: new Abstract: In clinical practice, the robustness of deep learning models for multimodal brain tumor segmentation is severely compromised by incomplete MRI data. Thi

Character Beyond Speech: Leveraging Role-Playing Evaluation in Audio Large Language Models via Reinforcement Learning

arXiv:2604.13804v1 Announce Type: new Abstract: The rapid evolution of multimodal large models has revolutionized the simulation of diverse characters in speech dialogue systems, enabling a novel inte

Composite Silhouette: A Subsampling-based Aggregation Strategy

arXiv:2604.13816v1 Announce Type: new Abstract: Determining the number of clusters is a central challenge in unsupervised learning, where ground-truth labels are unavailable. The Silhouette coefficien

Data-Efficient RLVR via Off-Policy Influence Guidance

arXiv:2510.26491v2 Announce Type: replace Abstract: Data selection is a critical aspect of Reinforcement Learning with Verifiable Rewards (RLVR) for enhancing the reasoning capabilities of large langu

Debate to Align: Reliable Entity Alignment through Two-Stage Multi-Agent Debate

arXiv:2604.13551v1 Announce Type: new Abstract: Entity alignment (EA) aims to identify entities referring to the same real-world object across different knowledge graphs (KGs). Recent approaches based

Depth-Aware Image and Video Orientation Estimation

arXiv:2604.13995v1 Announce Type: new Abstract: This paper introduces a novel approach for image and video orientation estimation by leveraging depth distribution in natural images. The proposed metho

DiPO: Disentangled Perplexity Policy Optimization for Fine-grained Exploration-Exploitation Trade-Off

arXiv:2604.13902v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed significant advances in the reasoning capabilities of Large Language Models (LLMs).

Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching

arXiv:2509.21912v2 Announce Type: replace Abstract: Guidance provides a simple and effective framework for posterior sampling by steering the generation process towards the desired distribution. When

Doc-V*:Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA

arXiv:2604.13731v1 Announce Type: new Abstract: Multi-page Document Visual Question Answering requires reasoning over semantics, layouts, and visual elements in long, visually dense documents. Existin

Echoes Over Time: Unlocking Length Generalization in Video-to-Audio Generation Models

arXiv:2602.20981v3 Announce Type: replace Abstract: Scaling multimodal alignment between video and audio is challenging, particularly due to limited data and the mismatch between text descriptions and

Enhanced Text-to-Image Generation by Fine-grained Multimodal Reasoning

arXiv:2604.13491v1 Announce Type: new Abstract: With the rapid progress of Multimodal Large Language Models (MLLMs), unified MLLMs that jointly perform image understanding and generation have advanced

Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning

arXiv:2604.13598v1 Announce Type: new Abstract: Recent reinforcement learning (RL) approaches have advanced radiology report generation (RRG), yet two core limitations persist: (1) report-level reward