arXiv:2605.02859v1 Announce Type: cross Abstract: Scientists increasingly rely on sensor-based data, yet transforming raw streams into insights across the edge-to-cloud continuum remains difficult. Provisioning heterogeneous infrastructure and managing execution on emerging platforms like Data Processing Units typically requires cross-domain expertise, creating significant barriers to rapid prototyping. This paper introduces an experience-driven methodology for the rapid […]
Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era
arXiv:2604.00187v2 Announce Type: replace-cross Abstract: Explainable Artificial Intelligence (XAI) is critical for ensuring trust and accountability, yet its development remains predominantly visual. For blind and low-vision (BLV) users, the lack of accessible explanations creates a fundamental barrier to the independent use of AI-driven assistive technologies. This problem intensifies as AI systems shift from single-query tools […]
The Productivity-Reliability Paradox: Specification-Driven Governance for AI-Augmented Software Development
arXiv:2605.01160v1 Announce Type: cross Abstract: Since 2022, AI-powered coding assistants have produced contradictory evidence: controlled studies report 20-56% productivity gains on well-scoped tasks, while the most rigorous RCT documents a 19% slowdown for experienced developers, and telemetry across 10,000+ developers shows 98% more pull requests but 91% longer review times with flat delivery metrics. This […]
Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals
arXiv:2605.00895v1 Announce Type: cross Abstract: The second-order harmonic (2f) component generated by twin-rotary compressor is a dominant low-frequency noise source of variable refrigerant flow (VRF) outdoor units, yet its amplitude fluctuates strongly with environmental thermal load and valve opening, making it difficult to assess accurately using conventional mechanism-based models. This paper proposes an unsupervised transfer […]
Fusing Urban Structure and Semantics: A Conditional Diffusion Model for Cross-City OD Matrix Generation
arXiv:2605.00938v1 Announce Type: cross Abstract: Accurate modeling of commuting flows is important for urban governance, traffic planning, and resource allocation. However, the combined influence of individual intentions, geographic constraints, and social dynamics leads to considerable heterogeneity in commuting patterns, making it difficult to develop generation models that generalize across cities. To address this issue, we […]
BIM Information Extraction Through LLM-based Adaptive Exploration
arXiv:2605.01698v1 Announce Type: cross Abstract: BIM models provide structured representations of building geometry, semantics, and topology, yet extracting specific information from them remains remarkably difficult. Current approaches translate natural language into structured queries by assuming a fixed data organization (static approach), which BIM heterogeneity eventually invalidates. We address this with a new paradigm, adaptive exploration, […]
Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
arXiv:2605.00857v1 Announce Type: cross Abstract: Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled target domains without accessing source data. However, existing SFDA methods rely solely on the limited internal knowledge of source-pretrained models, leading to inferior cross-domain generalization and unreliable pseudo-labels. Although EEG Foundation Models […]
Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning
arXiv:2605.01424v1 Announce Type: cross Abstract: Multimodal learning leverages the integration of diverse data modalities to enhance performance in complex tasks. Yet, it frequently encounters incomplete or redundant modality data in real-world scenarios. This paper presents a fine-grained theoretical analysis of the generalization properties of multimodal metric learning models, addressing critical gaps in understanding the relationship […]
KG-First, LLM-Fallback: A Hybrid Microservice for Grounded Skill Search and Explanation
arXiv:2605.01582v1 Announce Type: cross Abstract: Authoritative competency frameworks such as ESCO, ROME, and O*NET are essential for aligning education with labor market needs, yet their technical complexity and structural heterogeneity hinder practical adoption by educators. This paper introduces SkillGraph-Service, an interoperable microservice designed to bridge this gap by unifying these resources into a provenance-preserving Knowledge […]
Selector-Guided Autonomous Curriculum for One-Shot Reinforcement Learning from Verifiable Rewards
arXiv:2605.01823v1 Announce Type: cross Abstract: Recently, Reinforcement Learning from Verifiable Rewards (RLVR) has been established as a highly effective technique for augmenting the math reasoning skills of Large Language Models (LLMs) based on a single instance. Current state-of-the-art 1-shot RLVR models adopt heuristics for selecting instances, mostly based on historical variance in rewards, which we […]
Visual Chart Representations for Cryptocurrency Regime Prediction: A Systematic Deep Learning Study
arXiv:2605.00875v1 Announce Type: cross Abstract: Technical traders have long relied on visual analysis of candlestick charts to identify market patterns and predict price movements. While deep learning has achieved remarkable success in image classification, its application to financial chart images remains underexplored. This paper presents a systematic study comparing different visual representations for cryptocurrency regime […]
Rethink MAE with Linear Time-Invariant Dynamics
arXiv:2605.00915v1 Announce Type: cross Abstract: Standard representation probing for visual models relies on mathematically permutation-invariant operations like Global Average Pooling (GAP) or CLS tokens, treating patch representations as an unstructured bag-of-words. We challenge this paradigm by demonstrating that token order is a critical, exploitable dimension in frozen visual representations (e.g., MAE, BEiT, DINOv2, and ViT […]