arXiv:2603.17974v1 Announce Type: cross Abstract: Software vulnerabilities continue to grow in volume and remain difficult to detect in practice. Although learning-based vulnerability detection has progressed, existing benchmarks are largely function-centric and fail to capture realistic, executable, interprocedural settings. Recent repo-level security benchmarks demonstrate the importance of realistic environments, but their manual curation limits scale. This […]
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 […]
Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics
arXiv:2603.17633v1 Announce Type: new Abstract: Understanding the dynamic behavior of biomolecules is fundamental to elucidating biological function and facilitating drug discovery. While Molecular Dynamics (MD) simulations provide a rigorous physical basis for studying these dynamics, they remain computationally expensive for long timescales. Conversely, recent deep generative models accelerate conformation generation but are typically either failing […]
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 […]
See, Think, Act: Teaching Multimodal Agents to Effectively Interact with GUI by Identifying Toggles
arXiv:2509.13615v5 Announce Type: replace Abstract: The advent of multimodal agents facilitates effective interaction within graphical user interface (GUI), especially in ubiquitous GUI control. However, their inability to reliably execute toggle control instructions remains a key bottleneck. To investigate this, we construct a state control benchmark with binary toggle instructions derived from public datasets. Evaluation results […]
Edit-As-Act: Goal-Regressive Planning for Open-Vocabulary 3D Indoor Scene Editing
arXiv:2603.17583v1 Announce Type: cross Abstract: Editing a 3D indoor scene from natural language is conceptually straightforward but technically challenging. Existing open-vocabulary systems often regenerate large portions of a scene or rely on image-space edits that disrupt spatial structure, resulting in unintended global changes or physically inconsistent layouts. These limitations stem from treating editing primarily as […]
Scalable Energy-Based Models via Adversarial Training: Unifying Discrimination and Generation
arXiv:2510.13872v4 Announce Type: replace-cross Abstract: Simultaneously achieving robust classification and high-fidelity generative modeling within a single framework presents a significant challenge. Hybrid approaches, such as Joint Energy-Based Models (JEM), interpret classifiers as EBMs but are often limited by the instability and poor sample quality inherent in training based on Stochastic Gradient Langevin Dynamics (SGLD). We […]
Multimodal Emotion Recognition via Bi-directional Cross-Attention and Temporal Modeling
arXiv:2603.11971v2 Announce Type: replace-cross Abstract: Expression recognition in in-the-wild video data remains challenging due to substantial variations in facial appearance, background conditions, audio noise, and the inherently dynamic nature of human affect. Relying on a single modality, such as facial expressions or speech, is often insufficient for capturing these complex emotional cues. To address this […]
APEX-SWE
arXiv:2601.08806v2 Announce Type: replace-cross Abstract: We introduce the AI Productivity Index for Software Engineering (APEX-SWE), a benchmark for assessing whether frontier AI models can execute economically valuable software engineering work. Unlike existing evaluations that focus on narrow, well-defined tasks, APEX-SWE assesses two novel task types that reflect real-world software engineering: (1) Integration tasks (n=100), which […]
Identifying Latent Actions and Dynamics from Offline Data via Demonstrator Diversity
arXiv:2603.17577v1 Announce Type: cross Abstract: Can latent actions and environment dynamics be recovered from offline trajectories when actions are never observed? We study this question in a setting where trajectories are action-free but tagged with demonstrator identity. We assume that each demonstrator follows a distinct policy, while the environment dynamics are shared across demonstrators and […]