arXiv:2601.10779v2 Announce Type: replace-cross Abstract: In multi-source transfer learning, a key challenge lies in how to appropriately differentiate and utilize heterogeneous source tasks. However, existing multi-source methods typically focus on optimizing either the source weights or the amount of transferred samples, largely neglecting their joint consideration. In this work, we propose a theoretical framework, Unified […]
GenGait: A Transformer-Based Model for Human Gait Anomaly Detection and Normative Twin Generation
arXiv:2604.01997v1 Announce Type: new Abstract: Gait analysis provides an objective characterization of locomotor function and is widely used to support diagnosis and rehabilitation monitoring across neurological and orthopedic disorders. Deep learning has been increasingly applied to this domain, yet most approaches rely on supervised classifiers trained on disease-labeled data, limiting generalization to heterogeneous pathological presentations. […]
Semantically Annotated Multimodal Dataset for RF Interpretation and Prediction
arXiv:2604.01433v1 Announce Type: cross Abstract: Current limitations in wireless modeling and radio frequency (RF)-based AI are primarily driven by a lack of high-quality, measurement-based datasets that connect RF signals to their physical environments. RF heatmaps, the typical form of such data, are high-dimensional and complex but lack the geometric and semantic context needed for interpretation, […]
SelfGrader: Stable Jailbreak Detection for Large Language Models using Token-Level Logits
arXiv:2604.01473v1 Announce Type: cross Abstract: Large Language Models (LLMs) are powerful tools for answering user queries, yet they remain highly vulnerable to jailbreak attacks. Existing guardrail methods typically rely on internal features or textual responses to detect malicious queries, which either introduce substantial latency or suffer from the randomness in text generation. To overcome these […]
The Overlooked Repetitive Lengthening Form in Sentiment Analysis
arXiv:2604.01268v1 Announce Type: cross Abstract: Individuals engaging in online communication frequently express personal opinions with informal styles (e.g., memes and emojis). While Language Models (LMs) with informal communications have been widely discussed, a unique and emphatic style, the Repetitive Lengthening Form (RLF), has been overlooked for years. In this paper, we explore answers to two […]
Cell Migration Boundary Motion in Drosophila Egg Chambers: A Combined Phase Field and Chemoattractant Model
arXiv:2604.01357v1 Announce Type: cross Abstract: In the Drosophila melanogaster egg chamber, the collective migration of border cells toward the oocyte is guided by spatial gradients of chemoattractants. While cellular responses to these cues are well characterized, the spatial distribution of chemoattractant within the tissue remains difficult to measure experimentally due to imaging limitations and extracellular […]
Lifting Unlabeled Internet-level Data for 3D Scene Understanding
arXiv:2604.01907v1 Announce Type: cross Abstract: Annotated 3D scene data is scarce and expensive to acquire, while abundant unlabeled videos are readily available on the internet. In this paper, we demonstrate that carefully designed data engines can leverage web-curated, unlabeled videos to automatically generate training data, to facilitate end-to-end models in 3D scene understanding alongside human-annotated […]
DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis
arXiv:2604.01236v1 Announce Type: cross Abstract: Traditional network architectures suffer from severe protocol ossification and structural fragility due to their reliance on static, human-defined rules that fail to adapt to the emergent edge cases and probabilistic reasoning of modern autonomous agents. To address these limitations, this paper proposes DarwinNet, a bio-inspired, self-evolving network architecture that transitions […]
Bridging Large-Model Reasoning and Real-Time Control via Agentic Fast-Slow Planning
arXiv:2604.01681v1 Announce Type: cross Abstract: Large foundation models enable powerful reasoning for autonomous systems, but mapping semantic intent to reliable real-time control remains challenging. Existing approaches either (i) let Large Language Models (LLMs) generate trajectories directly – brittle, hard to verify, and latency-prone – or (ii) adjust Model Predictive Control (MPC) objectives online – mixing […]
Unified Optimization of Source Weights and Transfer Quantities in Multi-Source Transfer Learning: An Asymptotic Framework
arXiv:2601.10779v2 Announce Type: replace-cross Abstract: In multi-source transfer learning, a key challenge lies in how to appropriately differentiate and utilize heterogeneous source tasks. However, existing multi-source methods typically focus on optimizing either the source weights or the amount of transferred samples, largely neglecting their joint consideration. In this work, we propose a theoretical framework, Unified […]
Generative AI Spotlights the Human Core of Data Science: Implications for Education
arXiv:2604.02238v1 Announce Type: cross Abstract: Generative AI (GAI) reveals an irreducible human core at the center of data science: advances in GAI should sharpen, rather than diminish, the focus on human reasoning in data science education. GAI can now execute many routine data science workflows, including cleaning, summarizing, visualizing, modeling, and drafting reports. Yet the […]
APEX: Agent Payment Execution with Policy for Autonomous Agent API Access
arXiv:2604.02023v1 Announce Type: cross Abstract: Autonomous agents are moving beyond simple retrieval tasks to become economic actors that invoke APIs, sequence workflows, and make real-time decisions. As this shift accelerates, API providers need request-level monetization with programmatic spend governance. The HTTP 402 protocol addresses this by treating payment as a first-class protocol event, but most […]