Search: “framework”

Code-as-Room: Generating 3D Rooms from Top-Down View Images via Agentic Code Synthesis

model-releasesarxiv-cs-cv

arXiv:2605.18451v1 Announce Type: new Abstract: Designing realistic and functional 3D indoor rooms is essential for a wide range of applications, including interior design, virtual reality, gaming, an

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Research

ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation

DGX agent

arXiv:2605.17301v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in

researcharxiv-cs-ai

19 May 2026

Consent Chain Degradation in Embodied Multi-Agent Systems: Bridging the Gap Between AI Agent Governance and Robot Ethics

arXiv:2605.16300v1 Announce Type: cross Abstract: Robotic systems are moving from isolated platforms to interconnected multi-agent ecosystems that operate in human environments. This shift raises a go

safetyarxiv-cs-ai

Controlling Decision Drift in Multimodal Sentiment Analysis with Missing Modalities

arXiv:2605.16889v1 Announce Type: new Abstract: Multimodal sentiment analysis relies on textual, acoustic, and visual signals, yet real-world data often suffer from modality missing and quality imbala

safetyarxiv-cs-cv

Counterfactual Explanations Under Concept Drift

arXiv:2605.17651v1 Announce Type: new Abstract: Counterfactual explanations (CFEs) provide actionable recourse, but most methods assume a static framework with fixed data and a trained classifier. Thi

CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimation

model-releasesarxiv-cs-cv

arXiv:2605.16431v1 Announce Type: new Abstract: Computed tomography (CT) images are frequently degraded by acquisition artifacts, including noise, blur, streaking, aliasing, and metal artifacts. Yet C

DECODE: Domain-aware Continual Domain Expansion for Motion Prediction

arXiv:2411.17917v2 Announce Type: replace Abstract: Motion prediction is critical for autonomous vehicles to effectively navigate complex environments and accurately anticipate the behaviors of other

agentsarxiv-cs-cv

Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches

arXiv:2605.18692v1 Announce Type: new Abstract: Optimization models developed by operations research (OR) experts are often deployed as decision-support systems in industrial settings. However, real-w

agentsarxiv-cs-ai

Local Ai

Diagnosing Korean-Language LLM Political Bias via Census-Grounded Agent Simulation

arXiv:2605.18395v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit systematic political biases in voter simulations, but their underlying mechanisms and cross-lingual generalizatio

local-aiarxiv-cs-ai

Differentiable Optimization Layers for Guaranteed Fairness in Deep Learning

arXiv:2605.17118v1 Announce Type: new Abstract: Differentiable optimization layers are traditionally integrated in predict-then-optimize frameworks where a neural model estimates parameters that subse

safetyarxiv-cs-lg

Distributed 3D Leader-Follower Formation Control with Field-of-View Safety via Control Barrier Functions

arXiv:2605.17533v1 Announce Type: cross Abstract: This letter proposes a distributed 3D leader-follower formation (3D-LFF) control framework for multi-UAV systems that achieves formation tracking whil

safetyarxiv-cs-ro

Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation

model-releasesarxiv-cs-lg

arXiv:2502.02463v3 Announce Type: replace-cross Abstract: While Bayesian inference provides a principled framework for reasoning under uncertainty, its widespread adoption is limited by the intractabi

Local Ai

Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing

arXiv:2605.16433v1 Announce Type: cross Abstract: Task allocation in smart manufacturing systems needs to operate under decentralized decision-making, dynamic workloads, and shared resource constraint

local-aiarxiv-cs-ai

Factorized Latent Dynamics for Video JEPA: An Empirical Study of Auxiliary Objectives

arXiv:2605.17165v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPA) are a promising framework for self-supervised video representation learning, yet the behavior of auxilia

FG-TreeSeg: Flow-Guided Tree Crown Segmentation without Instance Annotations

arXiv:2602.00470v2 Announce Type: replace Abstract: Individual tree crown segmentation is an important task in remote sensing for forest biomass estimation and ecological monitoring. However, accurate

From a Single Demonstration to a General Policy for Contact-Rich Manipulation

arXiv:2605.17601v1 Announce Type: new Abstract: We present a Learning from Demonstration (LfD) framework that achieves one-shot generalization in multi-stage, contact-rich manipulation tasks. Central

safetyarxiv-cs-ro

Function graph transformers universally approximate operators between function spaces

arXiv:2605.17968v1 Announce Type: new Abstract: We study the approximation of nonlinear operators between function spaces by transformers. Our approach is to lift functions to measures supported on th

GaussianZoom: Progressive Zoom-in Generative 3D Gaussian Splatting with Geometric and Semantic Guidance

arXiv:2605.18252v1 Announce Type: new Abstract: We introduce GaussianZoom, a generative zoom-in 3D reconstruction system with an iterative progressive framework that combines geometry-consistent scene

Generalized Functional ANOVA in Closed-Form: A Unified View of Additive Explanations

arXiv:2605.18422v1 Announce Type: cross Abstract: The functional ANOVA, or Hoeffding decomposition, provides a principled framework for interpretability by decomposing a model prediction into main eff

Generating Realistic Safety-Critical Scenarios for Vehicle-Pedestrian Interactions

arXiv:2605.17229v1 Announce Type: new Abstract: Automated driving system deployment requires rigorous validation across safety-critical vehicle-pedestrian interactions, yet real-world datasets rarely

safetyarxiv-cs-ro

Hardware

GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment

arXiv:2605.16435v1 Announce Type: cross Abstract: Heatwaves are an important problem in cities, and climate change makes this problem more difficult. In this paper, we present a GPU-based deep learnin

hardwarearxiv-cs-ai

Tutorials

High-Resolution Reference Image Assisted Volumetric Super-Resolution of Cardiac Diffusion Weighted Imaging

arXiv:2310.20389v2 Announce Type: replace-cross Abstract: Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is the only in vivo method to non-invasively examine the microstructure of the human hear

tutorialsarxiv-cs-cv

Applications

HighSync: High-Quality Lip Synchronization via Latent Diffusion Models

arXiv:2605.16918v1 Announce Type: new Abstract: We present HighSync, an end-to-end diffusion-based framework for high-fidelity lip synchronization that generates photorealistic talking-face videos ali

applicationsarxiv-cs-cv

How Do Electrocardiogram Models Scale?

arXiv:2605.17276v1 Announce Type: cross Abstract: While scaling laws have established a fundamental framework for foundation models in natural language processing, their applicability to electrocardio

Tutorials

How to safeguard AI workloads with Unity AI Gateway Guardrails

Unity AI Gateway Guardrails is a Databricks feature designed to protect AI workloads by implementing safety measures and policy controls within the Unity Catalog framework. The tool enables organizati

tutorialsdatabricks

Iterative Chow Filtering for Learning with Distribution Shift

arXiv:2605.17251v1 Announce Type: cross Abstract: Recent work due to Goel et al. gave the first efficient algorithms for learning with distribution shift in the challenging PQ framework. In this setti

Latency-Aware Deep Learning Benchmark for Real-Time Cyber-Physical Attack and Fault Classification in Inverter-Dominated Power Grids

arXiv:2605.17256v1 Announce Type: cross Abstract: This work introduces a latency-aware benchmarking framework for evaluating deep learning models in power system anomaly detection using high-fidelity,

Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction

arXiv:2605.18211v1 Announce Type: cross Abstract: We introduce Graph-Augmented Sequence-to-Sequence (GA-S2S), a novel framework that integrates a T5-small encoder-decoder with a Relational Graph Atten

researcharxiv-cs-ai

Hardware

Lightweight Gaussian Process Inference in C++ on Metal and CUDA

arXiv:2605.17898v1 Announce Type: new Abstract: Gaussian process (GP) inference in Python is dominated by libraries such as GPyTorch and GPflow, which are built on deep-learning frameworks and inherit

hardwarearxiv-cs-lg

Lightweight Physics-Aware Zero-Shot Ultrasound Plane-Wave Denoising

arXiv:2506.21499v2 Announce Type: replace-cross Abstract: Ultrasound Coherent Plane-Wave Compounding (CPWC) enhances image contrast by combining echoes from multiple steered transmissions. While incre

Matrix-Decoupled Concentration for Autoregressive Sequences: Dimension-Free Guarantees for Sparse Long-Context Rewards

arXiv:2605.06017v2 Announce Type: replace Abstract: Sequence-level evaluations in autoregressive Large Language Models (LLMs) rely on highly dependent token generation. Establishing tight concentratio

Monocular Depth Perception Enhancement Based on Joint Shading/Contrast Model and Motion Parallax (JSM)

arXiv:2605.17252v1 Announce Type: new Abstract: Stereoscopic 3D displays adopt a binocular depth cue to provide depth perception. However, users should be equipped with expensive special devices to ap

Multilingual OCR-Aware Fine-Tuning and Prompt-Guided Chain-of-Thought Reasoning for Multimodal Large Language Models

model-releasesarxiv-cs-cl

arXiv:2605.16409v1 Announce Type: cross Abstract: Optical character recognition (OCR) and multilingual text understanding remain major failure modes of multimodal large language models (MLLMs), partic

Neuroscience-inspired Staged Representation Learning with Disentangled Coarse- and Fine-Grained Semantics for EEG Visual Decoding

model-releasesarxiv-cs-cv

arXiv:2605.16923v1 Announce Type: new Abstract: Decoding visual information from electroencephalography (EEG) signals remains a fundamental challenge in brain-computer interfaces and medical rehabilit

Overeager Coding Agents: Measuring Out-of-Scope Actions on Benign Tasks

arXiv:2605.18583v1 Announce Type: cross Abstract: Coding agents now run autonomously with shell, file, and network privileges. When a user issues a benign request, the agent sometimes does more than a

PEIRA: Learning Predictive Encoders through Inter-View Regressor Alignment

arXiv:2605.17671v1 Announce Type: cross Abstract: Non-contrastive self-supervised learning (SSL) is an effective framework for predictive representation learning, but popular (and in practice effectiv

safetyarxiv-cs-ai

PopuLoRA: Co-Evolving LLM Populations for Reasoning Self-Play

arXiv:2605.16727v1 Announce Type: new Abstract: We introduce PopuLoRA, a population-based asymmetric self-play framework for reinforcement learning with verifiable rewards (RLVR) post-training of LLMs

agentsarxiv-cs-ai

Proof-Carrying Certificates for LLM Pipelines: A Trust-Boundary Architecture

arXiv:2605.16407v1 Announce Type: cross Abstract: We present a framework for verifying the deterministic structured computations surrounding a large language model rather than the model itself, extend

agentsarxiv-cs-cl

QuantFPFlow: Quantum Amplitude Estimation for Fokker--Planck Policy Optimisation in Continuous Reinforcement Learning

arXiv:2605.16429v1 Announce Type: cross Abstract: We introduce extbf{QuantFPFlow}, a reinforcement learning framework that integrates quantum amplitude estimation into the Fokker--Planck~(FP) formulat

safetyarxiv-cs-ai

REBAR: Reference Ethical Benchmark for Autonomy Readiness

model-releasesarxiv-cs-ro

arXiv:2605.18423v1 Announce Type: new Abstract: As autonomous systems grow more advanced, objective metrics to evaluate their ethical and legal compliance are critical for informing end users of their

Applications

Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift

arXiv:2605.16411v1 Announce Type: cross Abstract: Hallucination remains a fundamental challenge in vision-language models (VLMs), where autoregressive generation may produce linguistically plausible y

applicationsarxiv-cs-ai

Rethinking Generative Image Pretraining: How Far Are We From Scaling Up Next-Pixel Prediction?

arXiv:2511.08704v2 Announce Type: replace Abstract: This paper investigates the scaling properties of autoregressive next-pixel prediction, a simple, end-to-end yet under-explored framework for unifie

SEDD: Scalable and Efficient Dataset Deduplication with GPUs

model-releasesarxiv-cs-cl

arXiv:2501.01046v4 Announce Type: replace Abstract: Dataset deduplication is widely recognized as a crucial preprocessing step that enhances data quality and improves the performance of large language

Skim: Speculative Execution for Fast and Efficient Web Agents

arXiv:2605.16565v1 Announce Type: new Abstract: Skim is a speculative execution framework for web agents that exploits the predictable structure of purpose-built websites. Today's web-agent expense is

agentsarxiv-cs-ai

Hardware

Spherical Harmonic Optimal Transport: Application to Climate Models Comparisons

arXiv:2605.18389v1 Announce Type: new Abstract: Optimal transport provides a powerful framework for comparing measures while respecting the geometry of their support, but comes with an expensive compu

hardwarearxiv-cs-lg

Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning

arXiv:2605.18656v1 Announce Type: cross Abstract: Federated Learning is a leading framework for training ML and AI models collaboratively across numerous user devices or databases. We study the trade-

Stress-Testing Neural Network Verifiers with Provably Robust Instances

arXiv:2605.17153v1 Announce Type: new Abstract: Neural network verifiers aim to provide formal guarantees on model behavior, but existing verification benchmarks are fundamentally limited by their lac

The Symmetries of Three-Layer ReLU Networks

model-releasesarxiv-cs-lg

arXiv:2605.18319v1 Announce Type: new Abstract: We develop a framework for analyzing parameter symmetries in deep ReLU networks and obtain a complete characterization of the generic parameter fibers f