Search: “papers”

Geo-Data-Driven HD Map Generation Workflow with Integrated Reference-Free Constraint-Based Verification

arXiv:2605.18921v1 Announce Type: new Abstract: High-definition (HD) maps are core artifacts for automated driving systems, but their generation commonly relies on sensor-intensive mobile mapping camp

applicationsarxiv-cs-ro

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Research

GLUT: 3D Gaussian Lookup Table for Continuous Color Transformation

DGX agent

arXiv:2605.19889v1 Announce Type: cross Abstract: 3D Lookup Tables (3D LUTs) are widely used for color mapping, but their grid-based representation requires discretizing the RGB space, leading to a ca

researcharxiv-cs-cv

20 May 2026

Local Ai

Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South

arXiv:2605.19190v1 Announce Type: cross Abstract: Despite the global deployment of text-to-image (T2I) models, their safety frameworks are largely calibrated to a Western-centric default, creating sig

local-aiarxiv-cs-ai

GoLongRL: Capability-Oriented Long Context Reinforcement Learning with Multitask Alignment

arXiv:2605.19577v1 Announce Type: new Abstract: We present GoLongRL, a fully open-source, capability-oriented post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR

Local Ai

GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation

arXiv:2605.05480v2 Announce Type: replace-cross Abstract: The main XAI attribution methods for deep neural networks -- GradCAM, SHAP, LIME, Integrated Gradients -- operate on separate theoretical foun

local-aiarxiv-cs-ai

Graph Neural Networks for Community Detection in Graph Signal Analysis

model-releasesarxiv-cs-lg

arXiv:2605.19733v1 Announce Type: cross Abstract: Community detection is a central problem in graph analysis, with applications ranging from network science to graph signal processing. In recent years

Harnessing Self-Supervised Features for Art Classification

arXiv:2605.18974v1 Announce Type: cross Abstract: Classifying artworks presents a significant challenge due to the complex interplay of fine-grained details and abstract features that condition the st

applicationsarxiv-cs-ai

Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training

arXiv:2605.18822v1 Announce Type: cross Abstract: Post-training has become essential for adapting large language models (LLMs) to complex downstream behaviors, including instruction following, prefere

HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation

arXiv:2605.18932v1 Announce Type: cross Abstract: In this work, we propose HypergraphFormer, a novel and efficient approach to floor plan generation based on learning hypergraph representations with a

Implicit Action Chunking for Smooth Continuous Control

arXiv:2605.19592v1 Announce Type: cross Abstract: Reinforcement learning often produces high-frequency oscillatory control signals that undermine the safety and stability required for physical deploym

Agents

IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra

arXiv:2508.16112v2 Announce Type: replace Abstract: Spectral analysis provides crucial clues for the elucidation of unknown materials. Among various techniques, infrared spectroscopy (IR) plays an imp

agentsarxiv-cs-ai

k-Inductive Neural Barrier Certificates for Unknown Nonlinear Dynamics

arXiv:2605.20108v1 Announce Type: cross Abstract: While conventional (k=1) discrete-time barrier certificate conditions impose strict safety constraints by requiring the function to be non-increasing

K-Quantization and its Impact on Output Performance

arXiv:2605.19645v1 Announce Type: new Abstract: Recent advancements in large language models (LLMs) have shown their remarkable capacities in many NLP tasks. However, their substantial size often pres

KG-ASG: Collision-Knowledge-Guided Closed-Loop Adversarial Scenario Generation With Primary-Support Attribution

arXiv:2605.18895v1 Announce Type: cross Abstract: Safety validation of autonomous driving systems requires high-risk scenario coverage, clear collision semantics, executable trajectories, and attribut

KVBuffer: IO-aware Serving for Linear Attention

arXiv:2605.19049v1 Announce Type: cross Abstract: Linear attention has recently gained significant attention for long-context inference due to its constant decoding cost with respect to context length

Learn-by-Wire Training Control Governance: Bounded Autonomous Training Under Stress for Stability and Efficiency

arXiv:2605.19008v1 Announce Type: new Abstract: Modern language-model training is increasingly exposed to instability, degraded runs, and wasted compute, especially under aggressive learning-rate, sca

Learned Static Function Data Structures

arXiv:2510.27588v3 Announce Type: replace-cross Abstract: We consider the task of constructing a data structure for associating a static set of keys with values, while allowing arbitrary output values

Tutorials

Learning Interpretable Point-Based Clinical Risk Scores via Direct Optimization

arXiv:2605.19113v1 Announce Type: cross Abstract: Many clinical risk scores are deployed as additive rules with nonnegative integer points assigned to relevant binary predictive features. These intege

tutorialsarxiv-cs-lg

Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting

arXiv:2605.19074v1 Announce Type: cross Abstract: The rapid global expansion of solar photovoltaic (PV) capacity-reaching a record 597 GW in 2024-highlights the urgent need for robust forecasting mode

Learning Orthonormal Bases for Function Spaces

arXiv:2605.19959v1 Announce Type: new Abstract: Infinite-dimensional orthonormal basis expansions play a central role in representing and computing with function spaces due to their favorable linear a

Less Back-and-Forth: A Comparative Study of Structured Prompting

arXiv:2605.20149v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used for open-ended tasks, but underspecified prompts can lead to low-quality answers and additional interacti

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection

arXiv:2504.00470v2 Announce Type: replace-cross Abstract: To develop a trustworthy AI system, which aim to identify the input regions that most influence the models decisions. The primary task of exis

LLM Benchmark Datasets Should Be Contamination-Resistant

arXiv:2605.19999v1 Announce Type: cross Abstract: Benchmark datasets are critical for reproducible, reliable, and discriminative evaluation of LLMs. However, recent studies reveal that many benchmark

Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation

arXiv:2603.16284v2 Announce Type: replace Abstract: Despite the significant advancements in Large Vision-Language Models (LVLMs), their tendency to generate hallucinations undermines reliability and r

Long-term Power Grid Planning via Answer Set Programming

arXiv:2605.20172v1 Announce Type: cross Abstract: The Power grid is a critical infrastructure underpinning all aspects of modern society and its services. Maintaining its effectiveness requires contin

applicationsarxiv-cs-ai

LoRA vs. Full Fine-Tuning: A Theoretical Perspective

arXiv:2605.19018v1 Announce Type: new Abstract: Fine-tuning adapts a pre-trained model to downstream tasks using a small amount of labeled data. Low-Rank Adaptation (LoRA) is an efficient fine-tuning

MapAnything: Evaluating Monocular Metric Depth Models for 3D Urban Asset Localization

arXiv:2509.14839v2 Announce Type: replace Abstract: City administrations increasingly rely on comprehensive databases and urban digital twins of city assets, such as traffic signs and trees, as well a

Mapping Uncharted Symmetries: Machine Discovery in Combinatorics

arXiv:2605.19063v1 Announce Type: new Abstract: Inspired by long-standing open problems in algebraic combinatorics, we show that modern machine learning can meaningfully contribute to verifiable mathe

Mechanistic Interpretability Needs Philosophy

arXiv:2506.18852v2 Announce Type: replace-cross Abstract: Mechanistic interpretability (MI) aims to explain how neural networks work by uncovering their underlying mechanisms. As the field grows in in

Industry

Mega IPOs on the way: SpaceX releases filing as OpenAI reportedly prepares for September listing

SpaceX Corp. today published the filing for its upcoming initial public offering. The move came against the backdrop of reports that OpenAI Group PBC could submit the paperwork for its own IPO to regu

industrysiliconangle

Memory-Augmented Reinforcement Learning Agent for CAD Generation

arXiv:2605.19748v1 Announce Type: new Abstract: Automatic generation of computer-aided design (CAD) models is a core technology for enabling intelligence in advanced manufacturing. Existing generation

MiMuon: Mixed Muon Optimizer with Improved Generalization for Large Models

arXiv:2605.19619v1 Announce Type: cross Abstract: Matrix-structured parameters frequently appear in many artificial intelligence models such as large language models. More recently, an efficient Muon

Motion-2-To-3: Leveraging 2D Motion Data for 3D Motion Generations

arXiv:2412.13111v2 Announce Type: replace Abstract: Text-driven human motion synthesis has showcased its potential for revolutionizing motion design in the movie and game industry. Existing methods of

MTraining: Distributed Dynamic Sparse Attention for Efficient Ultra-Long Context Training

arXiv:2510.18830v2 Announce Type: replace Abstract: The adoption of long context windows has become a standard feature in Large Language Models (LLMs), as extended contexts significantly enhance their

Agents

Multi-Headed Transformer Architectures as Time-dependent Wasserstein Gradient Flows

arXiv:2605.18870v1 Announce Type: new Abstract: In recent years, transformer architectures have revolutionized the field of language processing, opening the door to previously unforeseen possibilities

agentsarxiv-cs-lg

Local Ai

Multi-Pedestrian Safety Warning at Urban Intersections Use Case of Digital Twin

arXiv:2605.18823v1 Announce Type: new Abstract: Digital twins (DTs) for urban transportation systems have gained increasing attention; however, their systematic evaluation in safety-critical scenarios

local-aiarxiv-cs-lg

Nonlinearity as Rank: Generative Low-Rank Adapter with Radial Basis Functions

arXiv:2602.05709v2 Announce Type: replace Abstract: Low-rank adaptation (LoRA) approximates the update of a pretrained weight matrix using the product of two low-rank matrices. However, standard LoRA

Not All Tokens Are Worth Caching: Learning Semantic-Aware Eviction for LLM Prefix Caches

model-releasesarxiv-cs-lg

arXiv:2605.18825v1 Announce Type: new Abstract: Prefix caching is a key optimization in Large Language Model (LLM) serving, reusing attention Key-Value (KV) states across requests with shared prompt p

Open-Set Domain Adaptation Under Background Distribution Shift: Challenges and A Provably Efficient Solution

arXiv:2512.01152v4 Announce Type: replace-cross Abstract: As we deploy machine learning systems in the real world, a core challenge is to maintain a model that is performant even as the data shifts. S

applicationsarxiv-cs-ai

OpenCompass: A Universal Evaluation Platform for Large Language Models

arXiv:2605.19276v1 Announce Type: new Abstract: In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large lang

P2DNav: Panorama-to-Downview Reasoning for Zero-shot Vision-and-Language Navigation

arXiv:2605.19634v1 Announce Type: cross Abstract: Vision-and-language navigation (VLN) requires an embodied agent to ground natural-language instructions into executable navigation actions in unseen e

Passive Construction Site Safety Monitoring via Persona-Scaffolded Adversarial Chain-of-Thought VLM Verification

arXiv:2605.19869v1 Announce Type: cross Abstract: Construction remains the deadliest industry sector in the United States, with 1,055 fatal worker injuries recorded in 2023, and the majority preventab

Personalized Face Privacy Protection From a Single Image

arXiv:2605.19032v1 Announce Type: new Abstract: Photos of faces uploaded online are vulnerable to malicious actors who can scrape facial images from online sources and intrude on personal privacy via

Phase-Aware Mixture of Experts for Agentic Reinforcement Learning

arXiv:2602.17038v3 Announce Type: replace Abstract: Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a single

PixVerve: Advancing Native UHR Image Generation to 100MP with a Large-Scale High-Quality Dataset

model-releasesarxiv-cs-cv

arXiv:2605.20147v1 Announce Type: new Abstract: Text-to-Image (T2I) models have recently seen notable progress around 1K and 2K resolution. With the extreme desire for better visual experience and the

PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks

arXiv:2605.19145v1 Announce Type: new Abstract: In the literature, many continual learning (CL) algorithms have been proposed to address the issue of catastrophic forgetting in ML models (i.e., learni

Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence

arXiv:2605.18893v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their scalability is increasingly strained by the size of r

applicationsarxiv-cs-lg

Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance

arXiv:2605.18801v1 Announce Type: new Abstract: Data is fundamental to large language models (LLMs). However, understanding of what makes certain data useful for different stages of an LLM workflow, i