Search: “framework”

FedCritic: Serverless Federated Critic Learning-based Resource Allocation for Multi-Cell OFDMA in 6G

arXiv:2605.21418v1 Announce Type: cross Abstract: In sixth-generation (6G) ultra-dense networks, aggressive frequency reuse amplifies inter-cell interference (ICI), making multi-cell orthogonal freque

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Federated LoRA Fine-Tuning for LLMs via Collaborative Alignment

DGX agent

arXiv:2605.21217v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) has emerged as a powerful tool for parameter-efficient fine-tuning of large language models (LLMs). This paper studies LoRA

model-releasesarxiv-cs-lg

21 May 2026

Industry

From emissions reporting to decarbonization decisions

This Databricks article discusses how organizations can leverage data and analytics to transition from simply tracking and reporting carbon emissions to making strategic decarbonization decisions. It

industrydatabricks

Industry

From 'What Happened?' to 'What Will Happen?'

This Databricks blog post discusses the evolution from descriptive analytics (analyzing past events) to predictive analytics (forecasting future outcomes), likely exploring how modern data platforms e

industrydatabricks

FT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents

model-releasesarxiv-cs-lg

arXiv:2603.01712v2 Announce Type: replace-cross Abstract: Fine-tuning large language models for vertical domains remains labor-intensive, requiring practitioners to curate data, configure training, an

GAMR: Geometric-Aware Manifold Regularization with Virtual Outlier Synthesis for Learning with Noisy Labels

arXiv:2605.20727v1 Announce Type: new Abstract: Deep neural networks (DNNs) experience significant performance degradation when processing noisy labels, primarily due to overfitting on mislabeled data

safetyarxiv-cs-cv

Gaussian Sheaf Neural Networks

arXiv:2605.21435v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become the de facto standard for learning on relational data. While traditional GNNs' message passing is well suited f

applicationsarxiv-cs-lg

Generation of Heterogeneous PET Images from Uniform Organ Activity Maps Using a Pretrained Domain-Adapted Diffusion Model

arXiv:2605.20267v1 Announce Type: new Abstract: Synthetic PET images are valuable for quantitative imaging workflow development, scalable virtual imaging trials, and deep learning model training, but

researcharxiv-cs-cv

Graph Autoencoder for Process Monitoring

arXiv:2602.03004v2 Announce Type: replace Abstract: To improve the reliability and interpretability of industrial process monitoring, this article proposes a Causal Graph Spatial-Temporal Autoencoder

applicationsarxiv-cs-lg

GraphDiffMed: Knowledge-Constrained Differential Attention with Pharmacological Graph Priors for Medication Recommendation

arXiv:2605.20188v1 Announce Type: new Abstract: Recommending safe and effective medication combinations from electronic health records (EHRs) is a core clinical AI problem, yet it remains difficult be

Head-Aware Key-Value Compression for Efficient Autoregressive Image Generation

arXiv:2605.20600v1 Announce Type: new Abstract: Autoregressive (AR) visual generation has achieved remarkable performance but suffers from high memory usage and low throughput, as it requires caching

tutorialsarxiv-cs-cv

Agents

HITL-D: Human In The Loop Diffusion Assisted Shared Control

arXiv:2605.21460v1 Announce Type: new Abstract: Autonomous manipulation systems have achieved remarkable capabilities, yet the integration of human expertise with diffusion-based policies in shared co

agentsarxiv-cs-ro

How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective

arXiv:2502.17773v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate survey responses, but synthetic data can be misaligned with the human populatio

Agents

How Well Do Vision-Language Models Understand Sequential Driving Scenes? A Sensitivity Study

arXiv:2604.06750v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) are increasingly proposed for autonomous driving tasks, yet their performance on sequential driving scenes remains poo

agentsarxiv-cs-cv

'I didn't Make the Micro Decisions': Measuring, Inducing, and Exposing Goal-Level AI Contributions in Collaboration

arXiv:2605.21363v1 Announce Type: new Abstract: As large language models (LLMs) increasingly shape how users form, refine, and extend their goals, attributing contributions in human-AI collaboration b

applicationsarxiv-cs-cl

Local Ai

IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools

arXiv:2605.20682v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown remarkable capability in bridging visual perception and textual reasoning, enabling zero-shot unders

local-aiarxiv-cs-cv

Inference Time Policy Optimization for Offline RL with Differentiable World Models

arXiv:2603.22430v2 Announce Type: replace Abstract: Offline Reinforcement Learning (RL) learns optimal policies from fixed datasets, training a policy once and deploying it at inference time without f

Hardware

Instant GPU Efficiency Visibility at Fleet Scale

arXiv:2605.20799v1 Announce Type: cross Abstract: We present Overall FLOP Utilization (OFU), a hardware-level, precision-agnostic GPU efficiency metric for AI workloads on HPC systems, derived from tw

hardwarearxiv-cs-lg

Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning

arXiv:2605.21475v1 Announce Type: new Abstract: Relational prediction tasks are fundamental in many real-world applications, where data are naturally stored in relational databases (RDBs). Relational

applicationsarxiv-cs-lg

It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs

arXiv:2605.20258v1 Announce Type: new Abstract: Contextual Integrity (CI) defines privacy not merely as keeping information hidden, but as governing information flows according to the norms of a given

iTryOn: Mastering Interactive Video Virtual Try-On with Spatial-Semantic Guidance

arXiv:2605.21431v1 Announce Type: new Abstract: Video Virtual Try-On (VVT) aims to seamlessly replace a garment on a person in a video with a new one. While existing methods have made significant stri

tutorialsarxiv-cs-cv

JobArabi: An Arabic Corpus and Analysis of Job Announcements from Social Media

model-releasesarxiv-cs-cl

arXiv:2605.20960v1 Announce Type: new Abstract: This paper introduces JobArabi, a large-scale corpus of Arabic job announcements collected from social media between January 2024 and October 2025. The

JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA

arXiv:2605.20284v1 Announce Type: new Abstract: Industrial anomaly detection has been significantly advanced by Large Multimodal Models (LMMs), enabling diverse human instructions beyond detection, pa

LASH: Adaptive Semantic Hybridization for Black-Box Jailbreaking of Large Language Models

arXiv:2605.21362v1 Announce Type: new Abstract: Jailbreak attacks expose a persistent gap between the intended safety behavior of aligned large language models and their behavior under adversarial pro

safetyarxiv-cs-cl

Latent Geometry as a Structural Monitor: Eigenspace Alignment for Anomaly Detection in Anonymity Networks

arXiv:2605.20391v1 Announce Type: cross Abstract: Traditional anomaly detection marks events when measured signals cross predefined thresholds. This captures the moment of transition but not the struc

Latent Process Generator Matching

arXiv:2605.20547v1 Announce Type: new Abstract: Many recent flow-matching and diffusion-style generative models rely on auxiliary stochastic dynamics during training: a richer process is simulated to

tutorialsarxiv-cs-lg

Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory

model-releasesarxiv-cs-cl

arXiv:2602.06025v2 Announce Type: replace Abstract: Memory is increasingly central to Large Language Model (LLM) agents operating beyond a single context window, yet most existing systems rely on offl

Learning to Defer in Non-Stationary Time Series via Switching State-Space Models

arXiv:2601.22538v2 Announce Type: replace Abstract: Learning-to-defer (L2D) routes each decision to a system's own predictor or to an external expert. Streaming time-series settings break the offline-

tutorialsarxiv-cs-lg

Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment

arXiv:2605.20780v1 Announce Type: cross Abstract: Physics-informed diffusion models typically enforce PDE constraints only on final outputs, leaving intermediate representations unconstrained and pron

safetyarxiv-cs-cv

LER-YOLO: Reliability-Aware Expert Routing for Misaligned RGB-Infrared UAV Detection

arXiv:2605.20667v1 Announce Type: new Abstract: Detecting small unmanned aerial vehicles from RGB-infrared remote-sensing pairs remains challenging due to tiny target scale, cluttered backgrounds, and

Let EEG Models Learn EEG

arXiv:2605.21280v1 Announce Type: new Abstract: High-fidelity EEG generation is critical for alleviating data scarcity and addressing privacy constraints in large-scale neural modeling. Despite recent

tutorialsarxiv-cs-cv

Linear-DPO: Linear Direct Preference Optimization for Diffusion and Flow-Matching Generative Models

arXiv:2605.21123v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) is successful for alignment in LLMs but still faces challenges in text-to-image generation. Existing studies are co

safetyarxiv-cs-cv

Listwise Policy Optimization: Group-based RLVR as Target-Projection on the LLM Response Simplex

arXiv:2605.06139v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a standard approach for large language models (LLMs) post-training to incentivize r

Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning

arXiv:2605.20201v1 Announce Type: new Abstract: Recent large language models support inputs of up to 10 million tokens, yet they perform poorly on long-context tasks that require complex reasoning. Su

researcharxiv-cs-cl

MagBridge-Battery: A Synthetic Bridge Dataset for Li-ion Magnetometry and State-of-Health Diagnostics

model-releasesarxiv-cs-lg

arXiv:2605.20240v1 Announce Type: new Abstract: Battery health diagnostics today rely overwhelmingly on electrochemical signals measured at the cell terminals. A parallel literature has shown that mag

Map-Mono-Ego: Map-Grounded Global Human Pose Estimation from Monocular Egocentric Video

arXiv:2605.20889v1 Announce Type: new Abstract: Monocular egocentric human pose estimation is essential for ubiquitous activity monitoring. However, understanding the user's absolute location within t

researcharxiv-cs-cv

Markovian Circuit Tracing for Transformer State Dynamic

model-releasesarxiv-cs-lg

arXiv:2605.20824v1 Announce Type: new Abstract: Many sequence computations are easier to study as movement through internal states than as isolated local circuits. We introduce Markovian Circuit Traci

Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?

arXiv:2605.20606v1 Announce Type: new Abstract: Dataset distillation (DD) compresses a large training set into a small synthetic set for efficient training, but most DD methods optimize only clean acc

researcharxiv-cs-cv

Mitigating Label Bias with Interpretable Rubric Embeddings

arXiv:2605.21455v1 Announce Type: new Abstract: Statistical decision algorithms are increasingly deployed in domains where ground-truth labels are hard to obtain, such as hiring, university admissions

Agents

Mix-Quant: Quantized Prefilling, Precise Decoding for Agentic LLMs

arXiv:2605.20315v1 Announce Type: new Abstract: LLM agents have recently emerged as a powerful paradigm for solving complex tasks through planning, tool use, memory retrieval, and multi-step interacti

agentsarxiv-cs-cl

Mobile UMI: Cross-View Diffusion Policy with Decoupled Kinematics for Mobile Manipulation

arXiv:2605.20894v1 Announce Type: new Abstract: Mobile imitation learning on portable demonstration interfaces faces two coupled bottlenecks: locomotion-contaminated action labels and inference-induce

safetyarxiv-cs-ro

Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport

arXiv:2605.20989v1 Announce Type: new Abstract: Single-cell RNA sequencing provides insights into gene expression at single-cell resolution, yet inferring temporal processes from these static snapshot

Motion-Robust Deep Reconstruction for Free-Breathing Cardiac Cine MRI

arXiv:2605.20687v1 Announce Type: cross Abstract: Conventional cardiac cine MRI relies on breath-hold Cartesian acquisitions, which are vulnerable to motion artifacts and can be uncomfortable or infea

Multi-Head Attention as Ensemble Nadaraya-Watson Estimation: Variance Reduction, Decorrelation, and Optimal Head Diversity

arXiv:2605.20271v1 Announce Type: cross Abstract: We develop a rigorous statistical theory of multi-head attention (MHA) as an ensemble of Nadaraya-Watson (NW) kernel regression estimators. Building o

Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards

arXiv:2605.20865v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) plays a pivotal role in improving the reasoning ability of large language models. However, widely

Multimodal Optimal Transport for Training-free Temporal Segmentation in Surgical Robotics

arXiv:2602.24138v2 Announce Type: replace Abstract: Automated recognition of surgical phases and steps is a fundamental capability for intraoperative decision support, workflow automation, and skill a

Musical Attention Transformer: Music Generation Using a Music-Specific Attention Model

arXiv:2605.21081v1 Announce Type: cross Abstract: This study aims to enhance the quality of music generation using Transformers by incorporating meta-information. While Transformer-based approaches ar

Neural Estimation of Pairwise Mutual Information in Masked Discrete Sequence Models

arXiv:2605.20187v1 Announce Type: new Abstract: Understanding dependencies between variables is critical for interpretability and efficient generation in masked diffusion models (MDMs), yet these mode