arXiv:2603.21045v3 Announce Type: replace-cross Abstract: Diffusion-based image super-resolution (SR), which aims to reconstruct high-resolution (HR) images from corresponding low-resolution (LR) observations, faces a fundamental trade-off between inference efficiency and reconstruction quality. The state-of-the-art residual-shifting diffusion framework achieves efficient 4-step inference, yet suffers from severe performance degradation in compact sampling trajectories. This is mainly attributed to […]
Pseudo Label NCF for Sparse OHC Recommendation: Dual Representation Learning and the Separability Accuracy Trade off
arXiv:2603.24750v2 Announce Type: replace-cross Abstract: Online Health Communities connect patients for peer support, but users face a discovery challenge when they have minimal prior interactions to guide personalization. We study recommendation under extreme interaction sparsity in a survey driven setting where each user provides a 16 dimensional intake vector and each support group has a […]
Exploring the Impact of Skin Color on Skin Lesion Segmentation
arXiv:2603.29694v1 Announce Type: cross Abstract: Skin cancer, particularly melanoma, remains a major cause of morbidity and mortality, making early detection critical. AI-driven dermatology systems often rely on skin lesion segmentation as a preprocessing step to delineate the lesion from surrounding skin and support downstream analysis. While fairness concerns regarding skin tone have been widely studied […]
Multimodal Machine Learning for Early Prediction of Metastasis in a Swedish Multi-Cancer Cohort
arXiv:2603.29793v1 Announce Type: cross Abstract: Multimodal Machine Learning offers a holistic view of a patient’s status, integrating structured and unstructured data from electronic health records (EHR). We propose a framework to predict metastasis risk one month prior to diagnosis, using six months of clinical history from EHR data. Data from four cancer cohorts collected at […]
GENIE: Gram-Eigenmode INR Editing with Closed-Form Geometry Updates
arXiv:2603.29860v1 Announce Type: cross Abstract: Implicit Neural Representations (INRs) provide compact models of geometry, but it is unclear when their learned shapes can be edited without retraining. We show that the Gram operator induced by the INR’s penultimate features admits deformation eigenmodes that parameterize a family of realizable edits of the SDF zero level set. […]
UniRank: End-to-End Domain-Specific Reranking of Hybrid Text-Image Candidates
arXiv:2603.29897v1 Announce Type: cross Abstract: Reranking is a critical component in many information retrieval pipelines. Despite remarkable progress in text-only settings, multimodal reranking remains challenging, particularly when the candidate set contains hybrid text and image items. A key difficulty is the modality gap: a text reranker is intrinsically closer to text candidates than to image […]
End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines
arXiv:2603.29927v1 Announce Type: cross Abstract: Transferring large volumes of high-resolution images during wind turbine inspections introduces a bottleneck in assessing and detecting severe defects. Efficient coding must preserve high fidelity in blade regions while aggressively compressing the background. In this work, we propose an end-to-end deep learning framework that jointly performs segmentation and dual-mode (lossy […]
WAter: A Workload-Adaptive Knob Tuning System based on Workload Compression
arXiv:2603.28809v1 Announce Type: cross Abstract: Selecting appropriate values for the configurable parameters of Database Management Systems (DBMS) to improve performance is a significant challenge. Recent machine learning (ML)-based tuning systems have shown strong potential, but their practical adoption is often limited by the high tuning cost. This cost arises from two main factors: (1) the […]
Training for Technology: Adoption and Productive Use of Generative AI in Legal Analysis
arXiv:2603.04982v2 Announce Type: replace-cross Abstract: Can targeted user training unlock the productive potential of generative artificial intelligence in professional settings? We study this question using a randomized experiment in which 164 law students completed an issue-spotting examination under one of three conditions: no GenAI access, optional access to a large language model (LLM), or LLM […]
The impact of multi-agent debate protocols on debate quality: a controlled case study
arXiv:2603.28813v1 Announce Type: cross Abstract: In multi-agent debate (MAD) systems, performance gains are often reported; however, because the debate protocol (e.g., number of agents, rounds, and aggregation rule) is typically held fixed while model-related factors vary, it is difficult to disentangle protocol effects from model effects. To isolate these effects, we compare three main protocols, […]
KARMA: Knowledge-Action Regularized Multimodal Alignment for Personalized Search at Taobao
arXiv:2603.22779v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are equipped with profound semantic knowledge, making them a natural choice for injecting semantic generalization into personalized search systems. However, in practice we find that directly fine-tuning LLMs on industrial personalized tasks (e.g. next item prediction) often yields suboptimal results. We attribute this bottleneck to a […]
SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents
arXiv:2603.29139v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled agentic systems that translate natural language intent into executable scientific visualization (SciVis) tasks. Despite rapid progress, the community lacks a principled and reproducible benchmark for evaluating these emerging SciVis agents in realistic, multi-step analysis settings. We present SciVisAgentBench, a comprehensive and […]