arXiv:2603.17673v1 Announce Type: cross Abstract: LLM agents are increasingly relevant to research domains such as vulnerability discovery. Yet, the strongest systems remain closed and cloud-only, making them resource-intensive, difficult to reproduce, and unsuitable for work involving proprietary code or sensitive data. Consequently, there is an urgent need for small, local models that can perform security […]
rSDNet: Unified Robust Neural Learning against Label Noise and Adversarial Attacks
arXiv:2603.17628v1 Announce Type: cross Abstract: Neural networks are central to modern artificial intelligence, yet their training remains highly sensitive to data contamination. Standard neural classifiers are trained by minimizing the categorical cross-entropy loss, corresponding to maximum likelihood estimation under a multinomial model. While statistically efficient under ideal conditions, this approach is highly vulnerable to contaminated […]
ChopGrad: Pixel-Wise Losses for Latent Video Diffusion via Truncated Backpropagation
arXiv:2603.17812v1 Announce Type: cross Abstract: Recent video diffusion models achieve high-quality generation through recurrent frame processing where each frame generation depends on previous frames. However, this recurrent mechanism means that training such models in the pixel domain incurs prohibitive memory costs, as activations accumulate across the entire video sequence. This fundamental limitation also makes fine-tuning […]
“I’m Not Reading All of That”: Understanding Software Engineers’ Level of Cognitive Engagement with Agentic Coding Assistants
arXiv:2603.14225v2 Announce Type: replace-cross Abstract: Over-reliance on AI systems can undermine users’ critical thinking and promote complacency, a risk intensified by the emergence of agentic AI systems that operate with minimal human involvement. In software engineering, agentic coding assistants (ACAs) are rapidly becoming embedded in everyday development workflows. Since software engineers (SEs) create systems deployed […]
VideoAtlas: Navigating Long-Form Video in Logarithmic Compute
arXiv:2603.17948v1 Announce Type: cross Abstract: Extending language models to video introduces two challenges: representation, where existing methods rely on lossy approximations, and long-context, where caption- or agent-based pipelines collapse video into text and lose visual fidelity. To overcome this, we introduce textbfVideoAtlas, a task-agnostic environment to represent video as a hierarchical grid that is simultaneously […]
An optimal control approach to nonlinear wave speed selection in reaction-diffusion equations
arXiv:2603.17601v1 Announce Type: cross Abstract: Travelling wave solutions of reaction-diffusion equations are widely used to model the spatial spread of populations and other phenomena in biology and physics. In this article, we reinterpret the classical variational principle approach through an optimal control formulation, in order to obtain a lower bound on the invasion speed of […]
SAATT Nav: a Socially Aware Autonomous Transparent Transportation Navigation Framework for Wheelchairs
arXiv:2603.13698v2 Announce Type: replace-cross Abstract: While powered wheelchairs reduce physical fatigue as opposed to manual wheelchairs for individuals with mobility impairment, they demand high cognitive workload due to information processing, decision making and motor coordination. Current autonomous systems lack social awareness in navigation and transparency in decision-making, leading to decreased perceived safety and trust from […]
Theoretical Foundations of delta-margin Majority Voting
arXiv:2111.06390v4 Announce Type: replace-cross Abstract: In high-stakes ML applications such as fraud detection, medical diagnostics, and content moderation, practitioners rely on consensus-based approaches to control prediction quality. A particularly valuable technique — deltadelta delta-margin majority voting — collects votes sequentially until one label exceeds alternatives by a threshold deltadelta delta, offering stronger confidence than simple […]
A Contextual Help Browser Extension to Assist Digital Illiterate Internet Users
arXiv:2603.17592v1 Announce Type: cross Abstract: This paper describes the design, implementation, and evaluation of a browser extension that provides contextual help to users who hover over technological acronyms and abbreviations on web pages. The extension combines a curated technical dictionary with OpenAI’s large language model (LLM) to deliver on-demand definitions through lightweight tooltip overlays. A […]
Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models
arXiv:2504.00638v3 Announce Type: replace-cross Abstract: The accuracy and robustness of machine learning models against adversarial attacks are significantly influenced by factors such as training data quality, model architecture, the training process, and the deployment environment. In recent years, duplicated data in training sets, especially in language models, has attracted considerable attention. It has been shown […]
Ethical Fairness without Demographics in Human-Centered AI
arXiv:2603.13373v2 Announce Type: replace-cross Abstract: Computational models are increasingly embedded in human-centered domains such as healthcare, education, workplace analytics, and digital well-being, where their predictions directly influence individual outcomes and collective welfare. In such contexts, achieving high accuracy alone is insufficient; models must also act ethically and equitably across diverse populations. However, fair AI approaches […]
Role-Augmented Intent-Driven Generative Search Engine Optimization
arXiv:2508.11158v2 Announce Type: replace-cross Abstract: Generative Search Engines (GSEs), powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), are reshaping information retrieval. While commercial systems (e.g., BingChat, Perplexity.ai) demonstrate impressive semantic synthesis capabilities, their black-box nature fundamentally undermines established Search Engine Optimization (SEO) practices. Content creators face a critical challenge: their optimization strategies, effective […]