Search: “research”

Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization

arXiv:2607.01080v1 Announce Type: new Abstract: We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert

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Beyond Pixel Overlap: A Framework for Decomposing Segmentation Evaluation Metrics

DGX agent

arXiv:2607.00886v1 Announce Type: new Abstract: Evaluation metrics are central to binary target segmentation because they determine how progress is measured, compared, and interpreted. In this paper,

researcharxiv-cs-cv

2 Jul 2026

Breaking the Weak Recovery Limit in Random Phase Retrieval with Learned Regularizers

arXiv:2509.15026v2 Announce Type: replace-cross Abstract: We seek to recover an unknown signal from nonlinear amplitude-only measurements, a challenging inverse problem. Strong theoretical guarantees

Bridging Quantum Computing Paradigms toward Semiconductor Yield: A Controlled CV-versus-DV Comparison on Wafer-Map Defect Classification

arXiv:2607.00961v1 Announce Type: cross Abstract: Realizing quantum neural networks (QNNs) in industry requires knowing which quantum computing paradigm suits which task. Motivated by AI accelerators

Calibrated Test-Time Guidance for Bayesian Inference

arXiv:2602.22428v2 Announce Type: replace-cross Abstract: Test-time guidance is a widely used mechanism for steering pretrained diffusion models toward outcomes specified by a reward function. Existin

CAT: Confidence-Adaptive Thinking for Efficient Reasoning of Large Reasoning Models

arXiv:2607.00862v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have achieved remarkable success on complex tasks by leveraging long chain-of-thought (CoT) trajectories, yet they frequ

CausalMix: Data Mixture as Causal Inference for Language Model Training

arXiv:2607.01104v1 Announce Type: cross Abstract: In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance. Recent methods optimize mixture weights via

Characterizing and Identifying Separable Graphical Models

arXiv:2607.01057v1 Announce Type: cross Abstract: We study a broad class of graphical models whose independencies correspond to vertex separation in mixed graphs with directed, undirected, and bidirec

Closed-loop coupling of personalised and foundation models for real-time treatment guidance with MRI

arXiv:2607.00500v1 Announce Type: cross Abstract: Image-guided therapies, including radiotherapy, biopsy and deep brain stimulation, rely on real-time targeting of anatomical structures. However, in t

Constructing Epistemic AI Literacy: Detecting Epistemic Aims and Processes in Student-AI Co-Programming

arXiv:2607.00211v1 Announce Type: new Abstract: Epistemic thinking plays a central role in students' learning processes when applying generative artificial intelligence (GenAI), particularly in progra

Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation

arXiv:2601.08127v2 Announce Type: replace-cross Abstract: Expert-annotated training data remains the critical bottleneck for AI in histopathology, particularly for rare pathologies where even dozens o

Controllable Narrative Rendering for Enhanced Assisted Writing

arXiv:2607.00009v1 Announce Type: cross Abstract: Despite the remarkable proficiency of large language models (LLMs) in basic writing assistance, their utility in creative writing is fundamentally hin

Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks

arXiv:2607.00553v1 Announce Type: cross Abstract: Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their su

Cross4D-JEPA: Dense Cross-modal Correspondence Distillation for 4D Point Cloud Representation Learning

arXiv:2607.00514v1 Announce Type: cross Abstract: Automatic understanding of dynamic 4D point clouds, the 3D-point sequences captured over time by depth sensors and LiDAR, is central to robotics and e

Crystalite: A Lightweight Transformer for Efficient Crystal Modeling

arXiv:2604.02270v2 Announce Type: replace-cross Abstract: Generative models for crystalline materials often rely on equivariant graph neural networks, which capture geometric structure well but are co

DART: Difficulty-Adaptive Routing for Zero-Shot Video Temporal Grounding

arXiv:2607.00672v1 Announce Type: new Abstract: Zero-shot video temporal grounding (VTG) localizes events in untrimmed videos from natural language queries without task-specific training. Existing met

DART-VLN: Test-Time Memory Decay and Anti-Loop Regularization for Discrete Vision-Language Navigation

arXiv:2607.01043v1 Announce Type: cross Abstract: Memory-based discrete vision-language navigation (VLN) agents must act under partial observability, yet even strong frozen backbones remain vulnerable

Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners

arXiv:2607.00125v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable abilities when analyzing images, yet translating these capabilities to few-shot im

Decoupled Guidance: Disentangling Subject and Context Pathways in Text-to-Image Personalization

arXiv:2607.00766v1 Announce Type: new Abstract: Text-to-image personalization aims to generate a user-provided subject in novel scenes described by text. However, most existing methods encode subject

Deep Learning-Driven Black-Box Doherty Power Amplifier with Pixelated Output Combiner and Extended Efficiency Range

arXiv:2603.16565v2 Announce Type: replace-cross Abstract: This article presents a deep learning-driven inverse design methodology for Doherty power amplifiers (PA) with multi-port pixelated output com

Deep Multitask Learning for Mixed-Type Outcomes with Shared Sparsity

arXiv:2607.00995v1 Announce Type: cross Abstract: Most existing multitask learning approaches are limited by their reliance on task-specific loss functions tailored to the scale and type of each outco

Destination-Labeled Self-Looping Systems with Dwell: Intrinsic Characterization, Realization Cost, and Recognition

arXiv:2607.00044v1 Announce Type: cross Abstract: We study a finite-state symbolic controller for systems in which the admissible visible transitions are fixed in advance and each visible state carrie

Device Passport: Enabling Spatio-Temporal Pretrained Models to Generalize Across Input Layouts

arXiv:2607.00249v1 Announce Type: new Abstract: New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout. Biosignal foundation models offer a p

DeWorldSG: Depth-Aware 3D Semantic Scene Graph Generation via World-Model Priors

arXiv:2607.00889v1 Announce Type: cross Abstract: We present DeWorldSG, a novel framework that generates spatio-temporally robust 3D Semantic Scene Graphs from RGB-D sequences. Existing methods often

Diffusion-Based Multi-Class Normality for OOD Detection: An Application to CDP Authentication

arXiv:2607.00609v1 Announce Type: new Abstract: Reconstruction-based generative models offer a natural framework for unsupervised out-of-distribution (OOD) detection, but multi-class normality modelli

Disentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages

arXiv:2607.01161v1 Announce Type: cross Abstract: Cross-lingual speaker verification (SV) systems typically exhibit performance degradation when enrollment and test utterances are spoken in different

researcharxiv-cs-cl

Distributed Online Bandit Submodular Maximization with Bounded Sampling Violations

arXiv:2607.00680v1 Announce Type: new Abstract: We study distributed online submodular maximization under partition matroid constraints, in which multiple agents select a limited number of actions fro

Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecasting

arXiv:2606.18367v2 Announce Type: replace Abstract: Standard benchmarks evaluate time series foundation models (TSFMs) using aggregate metrics, but these can mask severe failures in critical operating

'Don't Say It!': Constraints, Compliance, and Communication when Language Models Play Taboo

arXiv:2607.00601v1 Announce Type: new Abstract: The game of Taboo requires describing a target word without using a set of forbidden words, so that other players can guess it. This deceptively simple

researcharxiv-cs-cl

DroneIQA-VLE: Multi-Task Drone Image Quality Assessment via Vision-Language Ensemble

arXiv:2607.00416v1 Announce Type: new Abstract: We present DroneIQA-VLE, our solution to the ICME 2026 Drone-IQA Grand Challenge on Target-aware Image Quality Assessment for Low-altitude UAV Images. T

E-TIDE: Fast, Structure-Preserving Motion Forecasting from Event Sequences

arXiv:2603.27757v2 Announce Type: replace Abstract: Event-based cameras capture visual information as asynchronous streams of per-pixel brightness changes, generating sparse, temporally precise data.

Efficient Multilingual Reasoning Transfer via Progressive Code-Switching

arXiv:2607.00485v1 Announce Type: new Abstract: Large reasoning models (LRMs) have achieved strong reasoning capabilities in English, yet their performance degrades significantly when required to reas

researcharxiv-cs-cl

Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing

arXiv:2602.02157v2 Announce Type: replace Abstract: Neural Controlled Differential Equations (Neural CDEs) provide a powerful continuous-time framework for sequence modeling, yet the roughness of the

Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution

arXiv:2508.11664v2 Announce Type: replace-cross Abstract: Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia. However, conventional methods like

Enhancing Flow Matching with A Unified Guidance Framework for Efficient and Robust Speech Synthesis

arXiv:2607.00363v1 Announce Type: cross Abstract: Flow Matching (FM) has emerged as a powerful paradigm for speech generation but remains constrained by high inference latency and timbre leakage. To a

Enhancing Oracle Bone Inscription Recognition via Multi-Scale Layer Attention

arXiv:2607.00057v1 Announce Type: cross Abstract: Oracle Bone Inscriptions (OBIs) recognition plays a crucial role in understanding ancient Chinese culture. However, accurately recognizing OBIs remain

Enhancing Robustness in Robot-Environment Interactions through Passive Compliant Degrees of Freedom: A Hybrid Position-Force Control Approach with Feedback Linearization

arXiv:2607.00571v1 Announce Type: new Abstract: Robot-environment interactions in dynamic or unstructured settings are often degraded by impact shocks, vibrations, and uncertainties in contact geometr

researcharxiv-cs-ro

EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction

arXiv:2607.00417v1 Announce Type: cross Abstract: In the era of satellite constellations, multi-view optical satellite imagery is pivotal for Earth Observation (EO) and high-quality Digital Surface Mo

Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition

arXiv:2607.00777v1 Announce Type: cross Abstract: Recognizing jazz standards from audio is a challenging form of tune-level music retrieval: different performances of the same standard may vary in tem

Explainability in mulimodal deep transformation models for stroke outcome prediction

arXiv:2504.06299v2 Announce Type: replace-cross Abstract: Multimodal prediction models based on imaging and clinical data are increasingly used for clinical decision support, yet their interpretabilit

Explainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions

arXiv:2607.00931v1 Announce Type: new Abstract: Predicting cancer drug response from transcriptomic profiles is a cornerstone of precision oncology, yet the scientific value of machine learning models

Faithful by Definition: Emotion Analysis via Natural Semantic Metalanguage Explications

arXiv:2607.00661v1 Announce Type: cross Abstract: Explanations for emotion classifiers are usually produced post hoc, with no guarantee that they reflect the computation behind the label. We present a

FCL-COD: Weakly Supervised Camouflaged Object Detection with Frequency-aware and Contrastive Learning

arXiv:2603.22969v2 Announce Type: replace Abstract: Existing camouflage object detection (COD) methods typically rely on fully-supervised learning guided by mask annotations. However, obtaining mask a

Finite-Time Queue Peak Laws in Stochastic Networks: Logarithmic Scaling After Geometric Thresholds

arXiv:2606.18218v2 Announce Type: replace-cross Abstract: We study finite-horizon queue peaks in generalized switches, a standard stochastic-network model in which many queues share constrained servic

FLAT: Revealing Hidden Latent-Conditioned Backdoor Failures in Federated Learning

arXiv:2508.04064v2 Announce Type: replace-cross Abstract: Horizontal federated learning (HFL) backdoor audits often summarize model behavior through clean accuracy (CA), mean attack success rate (ASR)

Flow-Map GRPO: Reinforcement Learning for Few-Step Flow-Map Generators via Anchored Stochastic Composition

arXiv:2607.00535v1 Announce Type: cross Abstract: Few-step flow-map generators, such as consistency models and MeanFlow, accelerate sampling by directly learning long-range transport maps between nois

Flow-Through Tensors: A Unified Computational Graph Architecture for Multi-Layer Transportation Network Optimization

arXiv:2507.02961v2 Announce Type: replace-cross Abstract: Modern transportation network modeling increasingly involves the integration of diverse methodologies including sensor-based forecasting, rein

Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence

arXiv:2607.00763v1 Announce Type: cross Abstract: Digital forensic investigations of network intrusions require analytical outputs that are traceable, reproducible, and court-defensible - requirements