All entries

arXiv:2606.08030v1 Announce Type: cross Abstract: Agentic tutoring systems introduce a coordination challenge: multiple agents may propose different but reasonable interventions, yet only one response

agentsarxiv-cs-ai

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis

DGX agent

arXiv:2606.08670v1 Announce Type: new Abstract: Large and demographically balanced datasets are essential for reliable neuroimaging biomarkers. Full-resolution 3D brain MRI synthesis can support data

safetyarxiv-cs-cv

9 Jun 2026

Tutorials

Weak-Driven Learning: How Weak Agents make Strong Agents Stronger

arXiv:2602.08222v2 Announce Type: replace Abstract: As post-training optimization becomes central to improving large language models, we observe a persistent saturation bottleneck: once models grow hi

tutorialsarxiv-cs-ai

WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces

arXiv:2606.09426v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly operate in runtimes that combine visual desktop control, command-line execution, code editing, browsers, and ext

Web Agents Should Use Typed Actions Instead of Click-Based Browsing

arXiv:2602.17245v2 Announce Type: replace Abstract: This position paper argues that building a reliable agentic Web requires shifting from low-level interaction primitives to typed actions supported b

Wedge Sampling: Efficient Tensor Completion with Nearly-Linear Sample Complexity

arXiv:2602.05869v2 Announce Type: replace-cross Abstract: We introduce Wedge Sampling, a new non-adaptive sampling scheme for low-rank tensor completion. We study recovery of an order-k low-rank tenso

Weighted universal approximation of differentiable maps on infinite-dimensional manifolds

arXiv:2606.09820v1 Announce Type: cross Abstract: We generalize the universal approximation theorem for functional input neural networks (FNN) to differentiable maps by including the approximation of

What Makes a Desired Graph for Relational Deep Learning?

arXiv:2606.08491v1 Announce Type: new Abstract: Relational deep learning (RDL) converts relational databases (RDBs) into heterogeneous graphs, but graphs derived directly from database schemas are oft

What Makes Video World Model Latents Action-Relevant: Prediction over Reconstruction

arXiv:2606.07687v1 Announce Type: cross Abstract: Video world models are increasingly used to provide predictive visual representations, yet it remains unclear which pretraining signals induce action-

What neurosurgeons need to see: synthetic intra-operative MRI from ultrasound for brain-shift compensation in brain tumour surgery

arXiv:2606.07658v1 Announce Type: new Abstract: Maximal safe resection is the primary objective in glioma surgery. Neuronavigation guidance is progressively degraded by brain shift after dural opening

researcharxiv-cs-cv

What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks

arXiv:2606.09700v1 Announce Type: cross Abstract: Large language model (LLM)-powered content moderation systems have become a critical defense against harmful online content. However, these systems pr

What's the Point? Spatial Grammar & Index Resolution for Sign Language Processing

arXiv:2606.08056v1 Announce Type: cross Abstract: Sign language models are predominantly trained with gloss-sequence or text supervision, thereby under-modeling non-lexical and productive construction

When Are Neural Interaction Discoveries Real? Identifiability, Recoverability, and a Pre-Fit Diagnostic

arXiv:2606.08390v1 Announce Type: new Abstract: When a neural time-series model reports that one variable modulates another's effect on a target, is the discovered interaction a property of the data o

When Behavioral Safety Evaluation Fails: A Representation-Level Perspective

arXiv:2606.08044v1 Announce Type: cross Abstract: Large Language Model (LLM) safety has often been evaluated at the behavior level, which provides limited evidence of internal robustness, as these eva

When Benign Inputs Lead to Severe Harms: Eliciting Unsafe Unintended Behaviors of Computer-Use Agents

arXiv:2602.08235v2 Announce Type: replace-cross Abstract: Although computer-use agents (CUAs) hold significant potential to automate increasingly complex OS workflows, they can demonstrate unsafe unin

When Do Local Score Models Extrapolate Across Size? A Diagnostic Theory and Benchmark

model-releasesarxiv-cs-lg

arXiv:2606.09705v1 Announce Type: new Abstract: Scientific generative modeling often requires size transfer, where models trained on small systems are evaluated on larger ones. While translation-invar

When Does Delegation Beat Majority? A Delegation-Based Aggregator for Multi-Sample LLM Inference

arXiv:2606.08098v1 Announce Type: new Abstract: Majority voting over sampled answers is the dominant unsupervised aggregator for multi-sample LLM inference. We show that piping the signals every sampl

When No Answer Is Correct: Diagnosing Absent Answer Detection for MLLMs in Video Understanding

arXiv:2606.08239v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made substantial advancements in video understanding, yet the reliability of their responses remains under

Agents

When Should an AI Scientist Stop? Verifiable Experiment Steering and Refusal for Autonomous Discovery

arXiv:2606.07576v1 Announce Type: new Abstract: We present CARTOGRAPH, a verification layer for AI scientists that couples unresolved-subspace experiment steering (select), explicit ambiguity closure

agentsarxiv-cs-lg

Agents

When Video Misreads: Closed-Loop Distillation of Reading Heuristics for Exploratory Manipulation Trace QA

arXiv:2606.08542v1 Announce Type: cross Abstract: Exploratory manipulation often turns an apparent failed attempt into the key evidence for what to do next. For example, a robot pulls a locked cabinet

agentsarxiv-cs-ai

Local Ai

When Vision Misleads, Let Location Speak: A Worldwide Image Geo-Localization Method via Location Attention Mechanism and Large Multimodal Models

arXiv:2606.08918v1 Announce Type: new Abstract: Worldwide image geo-localization aims to determine the capture location of an image on a global scale. Existing methods often mislocalize images by matc

local-aiarxiv-cs-cv

Where Does the Answer Come From? Benchmarking View-Level Visual Evidence Identification in Multi-View MLLMs for Autonomous Driving

arXiv:2606.09644v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) achieve strong results on visual reasoning benchmarks, but answer accuracy alone does not indicate whether a

Where Instruction Hierarchy Breaks: Diagnosing and Repairing Failures in Reasoning Language Models

arXiv:2606.07808v1 Announce Type: new Abstract: Reasoning language models deployed in agentic workflows must follow an instruction hierarchy: when instructions from different sources conflict, the mod

Where the Score Lives: A Wavelet View of Diffusion

arXiv:2606.08309v1 Announce Type: cross Abstract: Score-based generative models have had remarkable success over the last decade in generating a diverse set of visually plausible images. A variety of

researcharxiv-cs-cv

WhiFlash: Accelerating Speculative Decoding with Token-Level Cross-Paradigm Routing

arXiv:2606.07710v1 Announce Type: cross Abstract: The autoregressive nature of large language models (LLMs) remains a significant bottleneck for inference, particularly in complex agentic workloads. W

Who Earns the Safety? Intervention-Aware Quantum Predictive Control with Safety Attribution

arXiv:2606.09778v1 Announce Type: cross Abstract: Hard safety filters are increasingly placed downstream of learned controllers to guarantee constraint satisfaction at run time. Yet a filtered control

Why Limit the Residual Stream to Layers and Not Tokens? Persistent Memory for Continuous Latent Reasoning

arXiv:2606.07720v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable reasoning abilities on mathematical and multi-hop planning tasks. The CoCoNuT (Chain of Contin

Wispy to Voluminous: Prior-free Multi-view Capture of Strand-level Facial Hair

arXiv:2606.08041v1 Announce Type: cross Abstract: Facial hair is a defining trait of personal identity, yet remains a critical bottleneck for digital avatars. Recent volumetric methods achieve photore

applicationsarxiv-cs-cv

X-OP: Cross-Morphology Whole-Body Teleoperation via MPC Retargeting

arXiv:2606.07934v1 Announce Type: new Abstract: Whole-body teleoperation is essential for scalable robot data collection in loco-manipulation tasks, yet existing approaches relying on exoskeleton suit

safetyarxiv-cs-ro

X-Palm: Paired Multispectral-to-Smartphone Dataset for Cross-Domain Palmprint Authentication

arXiv:2606.08437v1 Announce Type: cross Abstract: Palmprint modality offers a privacy-preserving biometric solution, yet its deployment is hindered by the domain gap between controlled enrollment and

applicationsarxiv-cs-cv

Tutorials

XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection

arXiv:2502.09194v1 Announce Type: cross Abstract: Generative Artificial Intelligence (AI) techniques have become integral part in advancing next generation wireless communication systems by enabling s

tutorialsarxiv-cs-ai

Your Model Already Knows: Attention-Guided Safety Filter for Vision-Language-Action Models

model-releasesarxiv-cs-lg

arXiv:2606.09749v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated impressive end-to-end performance across a variety of robotic manipulation tasks. However, these

Your Self-Play Algorithm is Secretly an Adversarial Imitator: Understanding LLM Self-Play through the Lens of Imitation Learning

arXiv:2602.01357v2 Announce Type: replace Abstract: Self-play post-training methods has emerged as an effective approach for finetuning large language models and turn the weak language model into stro

safetyarxiv-cs-lg

Zero and Few Shot Load Forecasting with Large Language Models

arXiv:2411.11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before b

applicationsarxiv-cs-lg

Zero-Flow Encoders

arXiv:2602.00797v3 Announce Type: replace-cross Abstract: Flow-based methods have achieved significant success in various generative modeling tasks, capturing nuanced details within complex data distr

applicationsarxiv-cs-lg

Zero-Parameter Geometric Gating for Temporally Stable Low-Altitude UAV Video Semantic Segmentation

arXiv:2606.09162v1 Announce Type: new Abstract: Video semantic segmentation for low-altitude UAVs requires temporal consistency, yet dense optical flow introduces spatially structured noise in the pla

Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline

arXiv:2606.07965v1 Announce Type: new Abstract: Large Visual Language Models (LVLMs) have achieved remarkable success in vision tasks. However, the significant differences between industrial and natur

Zero-Shot Semantic Re-Identification for Autonomous Driving: A VLM Baseline Study

arXiv:2606.09362v1 Announce Type: new Abstract: Re-Identification (ReID) in autonomous driving is typically formulated as a visual matching problem, where observations of vehicles, pedestrians, and cy

Zero Touch Predictive Orchestration: Automating Time-Series Models for the Cloud-Edge Continuum

arXiv:2606.09787v1 Announce Type: new Abstract: The Cloud-Edge Continuum (CEC) enables latency-critical applications by distributing resources to the far edge, but its extreme volatility makes proacti

ZIPP:Zero-shot Image Personalization from Personas

arXiv:2606.08841v1 Announce Type: new Abstract: Text-to-image diffusion models are increasingly deployed in open-ended creative contexts, yet their outputs remain impersonal, optimized for aggregate a

3DMorph: Single-Image-Guided Local 3D Shape Editing and Morphing

arXiv:2606.07115v1 Announce Type: new Abstract: Despite recent progress in 3D generation, intuitive editing of existing shapes remains limited. Unlike images, which benefit from well-established inpai

A Causal Probabilistic Framework for Perception-Informed Closed-Loop Simulation of Autonomous Driving

arXiv:2606.07186v1 Announce Type: new Abstract: Software-in-the-loop (SIL) simulation is a cornerstone for the validation of modern automotive safety functions. However, many current frameworks utiliz

safetyarxiv-cs-ro

A Comprehensive Anatomy of Human and DeepSeek-R1 LLM Mathematical Reasoning

arXiv:2606.07410v1 Announce Type: cross Abstract: The emergence of 'Aha moments' in large language models, particularly DeepSeek-R1-0120, has raised the question of whether these systems genuinely rea

A Conformation-Centric Generative Foundation Model for Linear Polymer Modeling and Design

arXiv:2510.16023v2 Announce Type: replace Abstract: Linear polymers, macromolecules formed from monomers covalently bonded into continuous chains, underpin countless technologies and are indispensable

A Cross-view Fusion Framework for Robust 6-DoF Grasp Pose Estimation

arXiv:2606.06878v1 Announce Type: cross Abstract: In this paper, we propose a cross-view fusion framework that enhances the robustness of 6-DoF grasp pose estimation in corner views. Our framework all

A Dynamic Self-Evolving Extraction System

arXiv:2603.06915v2 Announce Type: replace Abstract: The extraction of structured information from raw text is a fundamental component of many NLP applications, including document retrieval, ranking, a

applicationsarxiv-cs-cl

A Four-Condition Diagnostic Protocol for Evidence Utilization in Long-Context and Retrieval-Augmented Language Models

model-releasesarxiv-cs-cl

arXiv:2606.06758v1 Announce Type: new Abstract: Final-answer accuracy, retrieval recall, and citation overlap do not by themselves identify whether a long-context or retrieval-augmented language model

A Geometric Account of Activation Steering through Angle-Norm Decomposition

arXiv:2606.06735v1 Announce Type: new Abstract: Linear activation steering has gained popularity as a simple and empirically effective way to control language model behavior. More recently, spherical