Search: “applications”

Revisiting Semantic Role Labeling: Efficient Structured Inference with Dependency-Informed Analysis

arXiv:2605.02505v1 Announce Type: new Abstract: Semantic Role Labeling (SRL) provides an explicit representation of predicate-argument structure, capturing linguistically grounded relations such as wh

researcharxiv-cs-cl

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SAMamba3D: adapting Segment Anything for generalizable 3D segmentation of multiphase pore-scale images

DGX agent

arXiv:2605.00916v1 Announce Type: new Abstract: Reliable segmentation of multiphase pore-scale X-ray images of rocks is necessary to quantify fluid saturation, connectivity, and interfacial geometry.

model-releasesarxiv-cs-cv

5 May 2026

SHARP: Spectrum-aware Highly-dynamic Adaptation for Resolution Promotion in Remote Sensing Synthesis

arXiv:2603.21783v2 Announce Type: replace Abstract: Text-to-image generation powered by Diffusion Transformers (DiTs) has made remarkable strides, yet remote sensing (RS) synthesis lags behind due to

Agents

Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture

arXiv:2605.02529v1 Announce Type: new Abstract: Autonomous surface vessels for floating-waste removal operate under varying hydrodynamics, external disturbances, and challenging water-surface percepti

agentsarxiv-cs-ro

SixthSense: Task-Agnostic Proprioception-Only Whole-Body Wrench Estimation for Humanoids

arXiv:2605.01427v1 Announce Type: new Abstract: Humanoid robots are entering our physical world at scale, yet as oversized toys--good at singing and dancing, but short on force-interaction capabilitie

researcharxiv-cs-ro

SparseContrast: Dynamic Sparse Attention for Efficient and Accurate Contrastive Learning in Medical Imaging

arXiv:2605.00887v1 Announce Type: new Abstract: We propose SparseContrast, a new framework that merges dynamic sparse attention with contrastive learning for medical imaging, with a focus on chest X-r

Stability and Generalization for Decentralized Markov SGD

arXiv:2605.01701v1 Announce Type: new Abstract: Stochastic gradient methods are central to large-scale learning, yet their generalization theory typically relies on independent sampling assumptions. I

Stable Blanket with Hidden Variables and Cycles

arXiv:2605.01856v1 Announce Type: cross Abstract: Stabilized regression aims to identify a set of predictors whose conditional relationship with a response variable remains invariant across different

Super-resolution of airborne laser scanning point clouds for forest inventory

arXiv:2605.02201v1 Announce Type: new Abstract: Airborne Laser Scanning (ALS) can collect point clouds across large areas, enabling large-scale forest inventory. However, ALS point clouds are sparse a

local-aiarxiv-cs-cv

SwiftPie: Lightning-fast Subject-driven Image Personalization via One step Diffusion

arXiv:2605.01510v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success in high-quality image synthesis, sparking interest in image-guided generation tasks such as subject-dr

safetyarxiv-cs-cv

Synthetic Users, Real Differences: an Evaluation Framework for User Simulation in Multi-Turn Conversations

arXiv:2605.02624v1 Announce Type: new Abstract: There is growing interest in exploring user simulation as an alternative to gathering and scoring real user-chatbot interactions for AI chatbot evaluati

researcharxiv-cs-cl

Task-Related Token Compression in Multimodal Large Language Models from an Explainability Perspective

arXiv:2506.01097v2 Announce Type: replace Abstract: Existing Multimodal Large Language Models (MLLMs) process a large number of visual tokens, leading to significant computational costs and inefficien

safetyarxiv-cs-cv

The Banach-Butterfly Invariant: Influence-Adaptive Walsh Geometry for Ternary Polynomial Threshold Functions

arXiv:2605.01637v1 Announce Type: new Abstract: We introduce the Banach-Butterfly Invariant (BBT), an influence-adaptive Banach geometry on the Walsh-Hadamard butterfly factorization. For a Boolean fu

The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification

arXiv:2510.01020v2 Announce Type: replace Abstract: We study sequential testing for a binary disease outcome when risk follows an unknown logistic model. At each round, the decision maker may either p

To Do or Not to Do: Ensuring the Safety of Visuomotor Policies Learned from Demonstrations

arXiv:2605.01201v1 Announce Type: new Abstract: Task success has historically been the primary measure of policy performance in imitation learning (IL) research. This characteristics strictly limits t

safetyarxiv-cs-ro

TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness

arXiv:2506.20588v2 Announce Type: replace Abstract: The increasing ubiquity of video content and the corresponding demand for efficient access to meaningful information have elevated video summarizati

TRIP-Evaluate: An Open Multimodal Benchmark for Evaluating Large Models in Transportation

arXiv:2605.00907v1 Announce Type: new Abstract: Large language models (LLMs) and multimodal large models (MLLMs) are increasingly used for transportation tasks such as regulation question answering, t

Unbox Responsible GeoAI: Navigating Climate Extreme and Disaster Mapping

arXiv:2605.00315v1 Announce Type: cross Abstract: As climate extreme and disaster events become more frequent and intense, Geospatial Artificial Intelligence (GeoAI) has emerged as a transformative ap

researcharxiv-cs-ai

Visual Chart Representations for Cryptocurrency Regime Prediction: A Systematic Deep Learning Study

arXiv:2605.00875v1 Announce Type: new Abstract: Technical traders have long relied on visual analysis of candlestick charts to identify market patterns and predict price movements. While deep learning

WILD SAM: A Simulated-and-Real Data Augmentation for Autonomous Driving Perception under Challenging Weather

arXiv:2605.01081v1 Announce Type: new Abstract: The performance of state-of-the-art object detectors degrades significantly under adverse weather, causing a safety-critical domain shift problem for au

Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamics

arXiv:2505.13150v2 Announce Type: replace Abstract: Behavioral Foundation Models (BFMs) proved successful in producing policies for arbitrary tasks in a zero-shot manner, requiring no test-time traini

safetyarxiv-cs-lg

Zero-Shot Interpretable Image Steganalysis for Invertible Image Hiding

arXiv:2605.01331v1 Announce Type: new Abstract: Image steganalysis, which aims at detecting secret information concealed within images, has become a critical countermeasure for assessing the security

A Unified Deep Learning Framework for Motion Correction in Medical Imaging

arXiv:2409.14204v4 Announce Type: replace-cross Abstract: Deep learning has shown significant value in medical image registration for motion correction, however, current techniques are either limited

local-aiarxiv-cs-cv

Adaptive Norm-Based Regularization for Neural Networks

arXiv:2605.00171v1 Announce Type: cross Abstract: In this paper, we study norm-based regularization methods for neural networks. We compare existing penalization approaches and introduce two regulariz

AIDA-ReID: Adaptive Intermediate Domain Adaptation for Generalizable and Source-Free Person Re-Identification

arXiv:2605.00111v1 Announce Type: new Abstract: Person re-identification (Re-ID) aims to match images of the same individual across non-overlapping camera views and remains challenging due to domain s

Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration

arXiv:2605.00310v1 Announce Type: new Abstract: Super-resolution (SR) techniques have made major advances in reconstructing high-resolution images from low-resolution inputs. The increased resolution

Bias in Large Language Models: Origin, Evaluation, and Mitigation

arXiv:2411.10915v2 Announce Type: replace Abstract: Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This

safetyarxiv-cs-cl

Copula-enhanced Vision Transformer for high myopia diagnosis through OU UWF fundus images

arXiv:2501.06540v2 Announce Type: replace Abstract: The advancement of AI-assisted myopia screening necessitates the joint diagnosis of both-eye (OU) high myopia (HM) status and the prediction of axia

Tutorials

Efficient Mutation Testing of Quantum Machine Learning Models

arXiv:2605.00107v1 Announce Type: cross Abstract: Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer par

tutorialsarxiv-cs-lg

EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinement

arXiv:2605.00628v1 Announce Type: cross Abstract: Text-to-SQL enables non-expert users to query databases in natural language, yet real-world schemas often suffer from ambiguous, abbreviated, or incon

local-aiarxiv-cs-cl

Elimination Templates in Macaulay2

arXiv:2605.00278v1 Announce Type: cross Abstract: We introduce the package exttt{EliminationTemplates} for the Macaulay2 computer algebra system, which provides tools for constructing automatic solver

Event-based Civil Infrastructure Visual Defect Detection: ev-CIVIL Dataset and Benchmark

arXiv:2504.05679v2 Announce Type: replace Abstract: Small unmanned aerial vehicle (UAV)-based visual inspections are a more efficient alternative to manual methods for examining civil structural defec

Exploring the System 1 Thinking Capability of Large Reasoning Models

arXiv:2504.10368v4 Announce Type: replace Abstract: This paper explores the system 1 thinking capability of Large Reasoning Models (LRMs), the intuitive ability to respond efficiently with minimal tok

FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources

arXiv:2605.00011v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative intelligence across decentralized data source devices in a privacy-preserving way. While substantial resea

Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy

arXiv:2510.16450v2 Announce Type: replace Abstract: Annotation-efficient segmentation of the numerous mitochondria instances from various electron microscopy (EM) images is highly valuable for biologi

InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information

arXiv:2508.07630v2 Announce Type: replace Abstract: We introduce InterChart, a diagnostic benchmark that evaluates how well vision-language models (VLMs) reason across multiple related charts, a task

Introducing WARM-VR: Benchmark Dataset for Multimodal Wearable Affect Recognition in Virtual Reality

arXiv:2605.00184v1 Announce Type: new Abstract: With the growing integration of human-computer interaction into everyday life, advances in machine learning have enabled systems to better perceive and

Learn where to Click from Yourself: On-Policy Self-Distillation for GUI Grounding

arXiv:2605.00642v1 Announce Type: cross Abstract: Graphical User Interface (GUI) grounding maps natural language instructions to the visual coordinates of target elements and serves as a core capabili

safetyarxiv-cs-cv

Lightweight Domain Adaptation of a Large Language Model for Legal Assistance in the Indian Context

arXiv:2505.22003v2 Announce Type: replace Abstract: In India, access to legal assistance for the general public has been observed to have a critical gap, as many citizens are not able to take full adv

Prefer-DAS: Learning from Local Preferences and Sparse Prompts for Domain Adaptive Segmentation of Electron Microscopy

arXiv:2602.19423v3 Announce Type: replace Abstract: Domain adaptive segmentation (DAS) is a promising paradigm for delineating intracellular structures from various large-scale electron microscopy (EM

local-aiarxiv-cs-cv

REALM: An RGB and Event Aligned Latent Manifold for Cross-Modal Perception

arXiv:2605.00271v1 Announce Type: new Abstract: Event cameras provide several unique advantages over standard frame-based sensors, including high temporal resolution, low latency, and robustness to ex

Reasoning-Intensive Regression

arXiv:2508.21762v3 Announce Type: replace Abstract: AI researchers and practitioners increasingly apply large language models (LLMs) to what we call reasoning-intensive regression (RiR), i.e., deducin

Reinforcement Learning with LLM-Guided Action Spaces for Synthesizable Lead Optimization

arXiv:2604.07669v2 Announce Type: replace Abstract: Lead optimization in drug discovery requires improving therapeutic properties while ensuring that molecular modifications correspond to feasible syn

safetyarxiv-cs-lg

Revealing graph bandits for maximizing local influence

arXiv:2605.00489v1 Announce Type: new Abstract: We study a graph bandit setting where the objective of the learner is to detect the most influential node of a graph by requesting as little information

SC-Taxo: Hierarchical Taxonomy Generation under Semantic Consistency Constraints using Large Language Models

arXiv:2605.00620v1 Announce Type: new Abstract: Scientific literature is expanding at an unprecedented pace, making it increasingly challenging to efficiently organize and access domain knowledge. A h

Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification

arXiv:2605.00218v1 Announce Type: cross Abstract: Mobile remote identity verification (RIdV) systems are exposed to attacks that manipulate or replace the facial video stream, including presentation a

Smart Profit-Aware Crop Advisory System: Kisan AI

arXiv:2605.00133v1 Announce Type: new Abstract: Modern crop advisory systems exhibit a critical limitation termed extit{economic blindness}. These systems primarily optimize for biological yield, ofte

Statistical Testing Framework for Clustering Pipelines by Selective Inference

arXiv:2603.18413v3 Announce Type: replace-cross Abstract: A data analysis pipeline is a structured sequence of steps that transforms raw data into meaningful insights by integrating multiple analysis