📰 Reads

arXiv:2604.24938v1 Announce Type: cross Abstract: Depth pruning improves the inference efficiency of large language models by removing Transformer blocks. Prior

arXiv:2604.24940v2 Announce Type: cross Abstract: Word embeddings are fundamental to natural language processing, yet traditional approaches represent each word

arXiv:2604.24952v1 Announce Type: cross Abstract: Human visual preferences are inherently multi-dimensional, encompassing aesthetics, detail fidelity, and seman

arXiv:2604.24953v2 Announce Type: cross Abstract: While preference optimization is crucial for improving visual generative models, how to effectively scale this

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 nativel

arXiv:2604.24955v1 Announce Type: cross Abstract: As benchmarks grow in complexity, many apparent agent failures are not failures of the agent at all - they are

arXiv:2604.24957v1 Announce Type: cross Abstract: Scaling test-time compute has emerged as a powerful mechanism for enhancing Large Language Model (LLM) perform

arXiv:2604.24999v1 Announce Type: cross Abstract: Thoracic Computed Tomography (CT) scans offer detailed insights into the intricate branching network of the ai

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

arXiv:2604.25018v1 Announce Type: cross Abstract: The Internet of Everything (IoE) represents an evolution of the Internet of Things (IoT) by integrating people

arXiv:2604.25031v1 Announce Type: cross Abstract: When an LLM formalizes natural language, how do we know the output is faithful? We propose a roundtrip verific

arXiv:2604.25039v1 Announce Type: cross Abstract: Large Language Models (LLMs) solve many reasoning tasks via chain-of-thought (CoT) prompting, but smaller mode

arXiv:2604.25047v1 Announce Type: cross Abstract: This study examined the barriers and enablers of online instruction in hospitality education. A sequential exp

arXiv:2604.25053v1 Announce Type: cross Abstract: While large language models (LLMs) are increasingly being explored for mental health applications, recent stud

arXiv:2604.25067v2 Announce Type: cross Abstract: Forecasting when AI systems will become capable of meaningfully accelerating AI research is a central challeng

arXiv:2604.25071v1 Announce Type: cross Abstract: The prevalence of biometric authentication has been on the rise due to its ease of use and elimination of weak

arXiv:2604.25085v1 Announce Type: cross Abstract: Fraud can pose a challenge in many resource allocation domains, including social service delivery and credit p

arXiv:2604.25109v1 Announce Type: cross Abstract: Agent Skills package SKILL.md files, scripts, reference documents, and repository context into reusable capabi

arXiv:2604.25110v1 Announce Type: cross Abstract: This position paper argues that knowledge distillation must account for what it loses: student models should b

arXiv:2604.25122v1 Announce Type: cross Abstract: We present M$^3$-VQA, a novel knowledge-based Visual Question Answering (VQA) benchmark, to enhance the evalua

arXiv:2604.25131v2 Announce Type: cross Abstract: Recent self-supervised pre-training methods for electroencephalogram (EEG) have shown promising results. Howev

arXiv:2604.25136v1 Announce Type: cross Abstract: We propose Frictive Policy Optimization (FPO), a framework for learning language model policies that regulate

arXiv:2604.25142v1 Announce Type: cross Abstract: Unsupervised domain adaptation generalizes neural retrievers to an unseen domain by generating pseudo queries

arXiv:2604.25143v1 Announce Type: cross Abstract: We show that replacing the rolling SVD of AdamW updates with a rolling SVD of loss gradients changes the diagn