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Proximal-Based Generative Modeling for Bayesian Inverse Problems

arXiv:2605.13278v1 Announce Type: cross Abstract: Score-based diffusion models demonstrate superior performance in generative tasks but encounter fundamental bottlenecks in inverse problems due to the

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Pyramid Forcing: Head-Aware Pyramid KV Cache Policy for High-Quality Long Video Generation

DGX agent

arXiv:2605.13111v1 Announce Type: new Abstract: Autoregressive video generation enables streaming and open-ended long video synthesis, but still suffers from long-term degradation caused by accumulate

safetyarxiv-cs-cv

14 May 2026

Pyramid Self-contrastive Learning Framework for Test-time Ultrasound Image Denoising

arXiv:2605.12567v1 Announce Type: cross Abstract: The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images. Conventional denoising methods rely on explicit no

Q-Flow: Stable and Expressive Reinforcement Learning with Flow-Based Policy

arXiv:2605.13435v1 Announce Type: cross Abstract: There is growing interest in utilizing flow-based models as decision-making policies in reinforcement learning due to their high expressive capacity.

QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling

arXiv:2605.13833v1 Announce Type: cross Abstract: Modeling long-range dependencies in sequential data remains a central challenge in machine learning. Transformers address this challenge through atten

researcharxiv-cs-cv

QSMOTE-PGM/kPGM: QSMOTE Based PGM and kPGM for Imbalanced Dataset Classification

arXiv:2512.16960v2 Announce Type: replace Abstract: Quantum-inspired machine learning (QiML) employs mathematical principles from quantum theory, such as Hilbert-space representations and quantum stat

researcharxiv-cs-lg

Quantifying LLM Safety Degradation Under Repeated Attacks Using Survival Analysis

arXiv:2605.12869v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in a wide range of applications, yet remain vulnerable to adversarial jailbreak attacks that ci

Quantifying Potential Observation Missingness in Inverse Reinforcement Learning

arXiv:2605.12831v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL), which infers reward functions from demonstrations, is a valuable tool for modeling and understanding decision-maki

applicationsarxiv-cs-lg

Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach

arXiv:2605.13830v1 Announce Type: new Abstract: Decision tree ensembles (DTE) are a popular model for a wide range of AI classification tasks, used in multiple safety critical domains, and hence verif

Quantitative Certification of Agentic Tool Selection

arXiv:2510.03992v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed in agentic systems, where a fundamental task is mapping user intents to relevant extern

Query-Conditioned Test-Time Self-Training for Large Language Models

arXiv:2605.13369v1 Announce Type: cross Abstract: Large language models (LLMs) are typically deployed with fixed parameters, and their performance is often improved by allocating more computation at i

Agents

QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Systems

arXiv:2511.17855v2 Announce Type: replace Abstract: Robots must learn from both what people do and what they say, but either modality alone is often incomplete: physical corrections are grounded but a

agentsarxiv-cs-ai

Qwen-Image-VAE-2.0 Technical Report

arXiv:2605.13565v1 Announce Type: new Abstract: We present Qwen-Image-VAE-2.0, a suite of high-compression Variational Autoencoders (VAEs) that achieve significant advances in both reconstruction fide

R-DMesh: Video-Guided 3D Animation via Rectified Dynamic Mesh Flow

arXiv:2605.13838v1 Announce Type: new Abstract: Video-guided 3D animation holds immense potential for content creation, offering intuitive and precise control over dynamic assets. However, practical d

safetyarxiv-cs-cv

Radial Compensation: Fixing Radius Distortion in Chart-Based Generative Models on Riemannian Manifolds

arXiv:2511.14056v2 Announce Type: replace-cross Abstract: We study the base distribution in chart-based generative models on Riemannian manifolds. Standard methods sample in Euclidean tangent space an

RAG-GNN: Integrating Retrieved Knowledge with Graph Neural Networks for Precision Medicine

arXiv:2602.00586v2 Announce Type: replace-cross Abstract: Network topology excels at structural predictions but fails to capture functional semantics encoded in biomedical literature. We present RAG-G

Agents

RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records

arXiv:2507.15867v2 Announce Type: replace-cross Abstract: Rare diseases affect 1 in 10 Americans yet remain systematically underdocumented in clinical records. ICD-based systems cannot capture their b

agentsarxiv-cs-ai

Re-evaluating Minimum Bayes Risk Decoding for Automatic Speech Recognition

arXiv:2510.19471v2 Announce Type: replace-cross Abstract: Recent work has shown that sample-based Minimum Bayes Risk (MBR) decoding outperforms beam search in text-to-text generation tasks, such as ma

researcharxiv-cs-lg

Real-World Challenges in Fake News Detection: Dealing with Posts by Cold Users

arXiv:2605.12511v1 Announce Type: cross Abstract: Social media serves as a primary source of information in the current digital era. Many people consume a vast range of information in a very short spa

applicationsarxiv-cs-lg

Real2Sim: A Physics-driven and Editable Gaussian Splatting Framework for Autonomous Driving Scenes

arXiv:2605.13591v1 Announce Type: new Abstract: Reliable autonomous driving relies on large-scale, well-labeled data and robust models. However, manual data collection is resource-intensive, and tradi

safetyarxiv-cs-cv

RealICU: Do LLM Agents Understand Long-Context ICU Data? A Benchmark Beyond Behavior Imitation

arXiv:2605.13542v1 Announce Type: new Abstract: Intensive care units (ICU) generate long, dense and evolving streams of clinical information, where physicians must repeatedly reassess patient states u

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations

arXiv:2605.12813v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance across many tasks but remain vulnerable to hallucinations, motivating the need for realistic a

Realtime-VLA FLASH: Speculative Inference Framework for Diffusion-based VLAs

arXiv:2605.13778v1 Announce Type: cross Abstract: Diffusion-based vision-language-action models (dVLAs) are promising for embodied intelligence but are fundamentally limited in real-time deployment by

applicationsarxiv-cs-cv

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

arXiv:2510.13999v3 Announce Type: replace-cross Abstract: Sparsely-activated Mixture-of-Experts (SMoE) models offer efficient pre-training and low latency but their large parameter counts create signi

Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning

arXiv:2507.01908v3 Announce Type: replace Abstract: Instruction-based image editing (IIE) has advanced rapidly with the success of diffusion models. However, existing efforts primarily focus on simple

Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMO

arXiv:2605.12578v1 Announce Type: cross Abstract: The integration of terahertz communications and ultra-massive multiple-input multiple-output (UM-MIMO) systems in 6G networks is motivated by their ab

researcharxiv-cs-lg

Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering

arXiv:2605.12961v1 Announce Type: new Abstract: Image clustering aims to partition unlabeled image datasets into distinct groups. A core aspect of this task is constructing and leveraging prior knowle

Reducing cross-sample prediction churn in scientific machine learning

model-releasesarxiv-cs-lg

arXiv:2605.13826v1 Announce Type: new Abstract: Scientific machine learning reports predictive performance. It does not report whether the same prediction would survive a different draw of training da

Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models

model-releasesarxiv-cs-lg

arXiv:2605.13587v1 Announce Type: cross Abstract: Near-infrared spectroscopy (NIRS) is rapid and non-destructive, but reliable calibration still depends heavily on spectral preprocessing. In routine p

Agents

Reinforced Collaboration in Multi-Agent Flow Networks

arXiv:2605.12943v1 Announce Type: new Abstract: Multi-agent systems provide a powerful way to extend large language models (LLMs) by decomposing a complex task into specialized subtasks handled by dif

agentsarxiv-cs-lg

Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation

arXiv:2510.14244v2 Announce Type: replace-cross Abstract: Domain adaptation methods aim to bridge the gap between datasets by enabling knowledge transfer across domains, reducing the need for addition

Relative Pose-Velocity Estimation Using Dual IMU Measurements and Relative Position Sensing

arXiv:2605.13031v1 Announce Type: cross Abstract: This paper addresses the problem of estimating the relative pose (position and orientation) and velocity of a vehicle with respect to a moving target,

researcharxiv-cs-ro

Representing Higher-Order Networks: A Survey of Graph-Based Frameworks

arXiv:2605.12509v1 Announce Type: cross Abstract: Many real-world phenomena are naturally modeled by graphs and networks. However, classical graph models are often limited to pairwise interactions and

applicationsarxiv-cs-ai

Repurposing Image Diffusion Models for Training-Free Music Style Transfer on Mel-spectrograms

arXiv:2411.15913v4 Announce Type: replace-cross Abstract: Music style transfer blends source structure with reference style to enable personalized music creation. However, existing zero-shot methods o

Local Ai

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity

arXiv:2605.13434v1 Announce Type: new Abstract: Asynchronous stochastic gradient descent (ASGD) is a standard way to exploit heterogeneous compute resources in distributed learning: instead of forcing

local-aiarxiv-cs-lg

Respecting Self-Uncertainty in On-Policy Self-Distillation for Efficient LLM Reasoning

arXiv:2605.13255v1 Announce Type: new Abstract: On-policy self-distillation trains a reasoning model on its own rollouts while a teacher, often the same model conditioned on privileged context, provid

Local Ai

Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle

arXiv:2605.13021v1 Announce Type: cross Abstract: Graph coarsening is a graph dimensionality reduction technique that aims to construct a smaller and more tractable graph while preserving the essentia

local-aiarxiv-cs-ai

Rethinking Generalization in Graph Neural Networks: A Structural Complexity Perspective

model-releasesarxiv-cs-lg

arXiv:2605.13597v1 Announce Type: new Abstract: Graph neural networks (GNNs) have emerged as a fundamental tool for learning from graph-structured data, achieving strong performance across a wide rang

Rethinking Graph Convolution for 2D-to-3D Hand Pose Lifting

arXiv:2605.13604v1 Announce Type: new Abstract: Graph convolutional networks (GCNs) are widely used for 3D hand pose estimation, where the hand skeleton is encoded as a fixed adjacency graph. We revis

ReTool-Video: Recursive Tool-Using Video Agents with Meta-Augmented Tool Grounding

arXiv:2605.13228v1 Announce Type: cross Abstract: Video understanding requires active evidence seeking, motivating tool-augmented video agents for temporal reasoning, cross-modal understanding, and co

Tutorials

Retrieval-Augmented Tutoring for Algorithm Tracing and Problem-Solving in AI Education

arXiv:2605.12988v1 Announce Type: new Abstract: Students learning algorithms often need support as they interpret traces, debug reasoning errors, and apply procedures across unfamiliar problem instanc

tutorialsarxiv-cs-ai

Tutorials

Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation

arXiv:2605.12975v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on m

tutorialsarxiv-cs-ai

Revealing Interpretable Failure Modes of VLMs

arXiv:2605.12674v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly used in safety-critical applications because of their broad reasoning capabilities and ability to general

Revealing the Gap in Human and VLM Scene Perception through Counterfactual Semantic Saliency

arXiv:2605.13047v1 Announce Type: cross Abstract: Evaluating whether large vision-language models (VLMs) align with human perception for high-level semantic scene comprehension remains a challenge. Tr

Tutorials

Revisit What You See: Revealing Visual Semantics in Vision Tokens to Guide LVLM Decoding

arXiv:2506.09522v3 Announce Type: replace-cross Abstract: Large Vision Language Models (LVLMs) achieve strong performance across multimodal tasks by integrating visual perception with language underst

tutorialsarxiv-cs-ai

Revisiting DAgger in the Era of LLM-Agents

arXiv:2605.12913v1 Announce Type: new Abstract: Long-horizon LM agents learn from multi-turn interaction, where a single early mistake can alter the subsequent state distribution and derail the whole

safetyarxiv-cs-lg

Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective

arXiv:2605.12969v1 Announce Type: cross Abstract: RLVR has become a widely adopted paradigm for improving LLMs' reasoning capabilities, and GRPO is one of its most representative algorithms. In this p

Reward-Weighted On-Policy Distillation with an Open Property-Equivalence Verifier for NL-to-SVA Generation

arXiv:2605.13501v1 Announce Type: cross Abstract: LLM-based generation of SystemVerilog Assertions (SVA) is often reported as nearing saturation, with the strongest specialized model reaching {sim}76%

safetyarxiv-cs-lg