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arXiv:2602.05847v1 Announce Type: new Abstract: While humans perceive the world through diverse modalities that operate synergistically to support a holistic understanding of their surroundings, existing...
arXiv:2602.05848v1 Announce Type: new Abstract: DARWIN is an evolutionary GPT model, utilizing a genetic-algorithm like optimization structure with several independent GPT agents being trained individually...
arXiv:2602.05849v1 Announce Type: new Abstract: Training a neural network requires navigating a high-dimensional, non-convex loss surface to find parameters that minimize this loss. In many...
arXiv:2602.05850v1 Announce Type: new Abstract: We use the theory of algebraic effects to give a complete equational axiomatization for dynamic threads. Our method is based...
arXiv:2602.05852v1 Announce Type: new Abstract: Community detection in networks is a fundamental problem in machine learning and statistical inference, with applications in social networks, biological...
arXiv:2602.05853v1 Announce Type: new Abstract: The quadratic complexity of attention mechanisms poses a critical bottleneck for large language models processing long contexts. While dynamic sparse...
arXiv:2602.05854v1 Announce Type: new Abstract: AI has been increasingly integrated into screenwriting practice. In refinement, screenwriters expect AI to provide feedback that supports reflection across...
arXiv:2602.05855v1 Announce Type: new Abstract: Reliable terrain perception is a critical prerequisite for the deployment of humanoid robots in unstructured, human-centric environments. While traditional systems...
arXiv:2602.05856v1 Announce Type: new Abstract: Standardized patients (SPs) play a central role in clinical communication training but are costly, difficult to scale, and inconsistent. Large...
arXiv:2602.05857v1 Announce Type: new Abstract: The rapid evolution of large language models (LLMs) has expanded their capabilities from basic dialogue to advanced scientific reasoning. However,...
arXiv:2602.05859v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have become a standard tool for mechanistic interpretability in autoregressive large language models (LLMs), enabling researchers to...
arXiv:2602.05861v1 Announce Type: new Abstract: Graph-structured data is ubiquitous and powerful in representing complex relationships in many online platforms. While graph neural networks (GNNs) are...
arXiv:2602.05863v1 Announce Type: new Abstract: While Group Relative Policy Optimization (GRPO) has emerged as a scalable framework for critic-free policy learning, extending it to settings...
arXiv:2602.05864v1 Announce Type: new Abstract: We present an LLM-mediated role-playing game that supports reflection on socialization, moral responsibility, and educational role positioning. Grounded in socialization...
arXiv:2602.05867v1 Announce Type: new Abstract: Mysterious citations are routinely appearing in peer-reviewed publications throughout the scientific community. In this paper, we developed an automated pipeline...
arXiv:2602.05868v1 Announce Type: new Abstract: Existing literature heavily relies on static analysis tools to evaluate LLMs for secure code generation and vulnerability detection. We reviewed...
arXiv:2602.05871v1 Announce Type: new Abstract: Distilled autoregressive diffusion models facilitate real-time short video synthesis but suffer from severe error accumulation during long-sequence generation. While existing...
arXiv:2602.05873v1 Announce Type: new Abstract: Bayesian (deep) neural networks (BNN) are often more attractive than the mainstream point-estimate vanilla deep learning in various aspects including...
arXiv:2602.05874v1 Announce Type: new Abstract: Hate speech detection is commonly framed as a direct binary classification problem despite being a composite concept defined through multiple...
arXiv:2602.05875v1 Announce Type: new Abstract: We introduce the Hierarchical Seating Allocation Problem (HSAP) which addresses the optimal assignment of hierarchically structured organizational teams to physical...
arXiv:2602.05877v1 Announce Type: new Abstract: The integration of Large Language Model (LLM)-based conversational agents into vehicles creates novel security challenges at the intersection of agentic...
arXiv:2602.05880v1 Announce Type: new Abstract: Boundary detection of irregular and translucent objects is an important problem with applications in medical imaging, environmental monitoring and manufacturing,...
arXiv:2602.05882v1 Announce Type: new Abstract: Being a cornerstone of temporal analysis, change detection has been playing a pivotal role in modern earth observation. Existing change...
arXiv:2602.05883v1 Announce Type: new Abstract: Large language models (LLMs) have rapidly become familiar tools to researchers and practitioners. Concepts such as prompting, temperature, or few-shot...