arXiv:2604.25297v1 Announce Type: cross Abstract: In recent years, the rapid proliferation of open-source large language models (LLMs) has spurred efforts to turn general-purpose models into domain specialists. However, many domain-specialized LLMs are developed using datasets and training protocols that are not aligned with the nuanced requirements of real-world applications. In the legal domain, where precision […]
A Faceted Proposal for Transparent Attribution of AI-Assisted Text Production
arXiv:2604.25346v1 Announce Type: cross Abstract: Artificial intelligence systems are increasingly integrated into writing processes, challenging traditional notions of authorship, responsibility, and intellectual contribution. Current disclosure practices usually indicate whether AI was used, but rarely explain how it was used, where it intervened, or how its output was reviewed. This paper proposes a faceted model for […]
Co-Writing with AI: An Empirical Study of Diverse Academic Writing Workflows
arXiv:2604.25389v1 Announce Type: cross Abstract: Despite AI tools becoming increasingly embedded in academic practice, little is known about how university students integrate them into their writing processes. We examine how students engage with AI across different writing tasks, and how this engagement is shaped by individual factors including AI literacy, writing confidence, trust, authorship concerns, […]
Benchmarking bandgap prediction in semiconductors under experimental and realistic evaluation settings
arXiv:2604.25568v1 Announce Type: cross Abstract: Accurate bandgap prediction is crucial for semiconductor applications, yet machine learning models trained on computational data often struggle to generalize to experimental bandgap measurements. Challenges related to data fidelity, domain generalization, and model interpretability remain insufficiently addressed in existing evaluation frameworks. To bridge this gap, we introduce RealMat-BaG, a benchmark […]
CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG
arXiv:2604.25676v1 Announce Type: cross Abstract: Multilingual retrieval-augmented generation (mRAG) is often implemented within a fixed retrieval space, typically via query or document translation or multilingual embedding vector representations. However, this approach may be inadequate for culturally grounded queries, in which retrieval-condition misalignment may occur. Even strong retrievers and generators may struggle to produce culturally relevant […]
Investigation into In-Context Learning Capabilities of Transformers
arXiv:2604.25858v1 Announce Type: cross Abstract: Transformers have demonstrated a strong ability for in-context learning (ICL), enabling models to solve previously unseen tasks using only example input output pairs provided at inference time. While prior theoretical work has established conditions under which transformers can perform linear classification in-context, the empirical scaling behavior governing when this mechanism […]
Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
arXiv:2604.24954v1 Announce Type: cross Abstract: We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 Nano Omni delivers consistent accuracy improvements over its predecessor, Nemotron Nano V2 VL, across all modalities, enabled by advances in architecture, […]
Evaluating Risks in Weak-to-Strong Alignment: A Bias-Variance Perspective
arXiv:2604.25077v1 Announce Type: new Abstract: Weak-to-strong alignment offers a promising route to scalable supervision, but it can fail when a strong model becomes confidently wrong on examples that lie in the weak teacher’s blind spots. Understanding such failures requires going beyond aggregate accuracy, since weak-to-strong errors depend not only on whether the strong model disagrees […]
EVT-Based Generative AI for Tail-Aware Channel Estimation
arXiv:2604.25008v1 Announce Type: cross Abstract: Ultra-reliable and low-latency communication (URLLC) will play a key role in fifth-generation (5G) and beyond networks, enabling mission-critical applications. Meeting the stringent URLLC requirements, characterized by extremely low packet error rates and minimal latency, calls for advanced statistical modeling to accurately capture rare events in wireless channels. Traditional methods, such […]
GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts
arXiv:2601.05110v3 Announce Type: replace Abstract: Large Reasoning Models (LRMs) achieve remarkable performance by explicitly generating multi-step chains of thought, but this capability incurs substantial inference latency and computational cost. Collaborative inference offers a promising solution by selectively allocating work between lightweight and large models, yet a fundamental challenge remains: determining when a reasoning step requires […]
Barriers and Enablers of Online Instruction in Hospitality Education in the Philippines: An Exploratory Study
arXiv:2604.25047v1 Announce Type: cross Abstract: This study examined the barriers and enablers of online instruction in hospitality education. A sequential exploratory design was implemented with hospitality teachers from both public and private higher educational institutions in the Philippines. Thematic analysis of interviews identified four key themes: technological barriers, pedagogical challenges, institutional and personal support, and […]
Agentic Architect: An Agentic AI Framework for Architecture Design Exploration and Optimization
arXiv:2604.25083v1 Announce Type: new Abstract: Rapid advances in Large Language Models (LLMs) create new opportunities by enabling efficient exploration of broad, complex design spaces. This is particularly valuable in computer architecture, where performance depends on microarchitectural designs and policies drawn from vast combinatorial spaces. We introduce Agentic Architect, an agentic AI framework for computer architecture […]