AI Infrastructure

Integrate Physical AI Capabilities into Existing Apps with NVIDIA Omniverse Libraries

Physical AI—AI systems that perceive, reason, and act in physically grounded simulated environments—is changing how teams design and vali...

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

Dell and HIVE partner to deploy Nvidia’s next-generation AI chips

[2602.16690] Synthetic-Powered Multiple Testing with FDR Control

The paper presents SynthBH, a novel method for multiple hypothesis testing that integrates synthetic data to enhance statistical inferenc...

[2502.01160] Scalable Precise Computation of Shannon Entropy

This paper presents a scalable tool, PSE, for precise computation of Shannon entropy, optimizing the process to enhance efficiency in qua...

[2602.16660] Align Once, Benefit Multilingually: Enforcing Multilingual Consistency for LLM Safety Alignment

The paper presents a method for enhancing multilingual safety alignment in large language models (LLMs) using a resource-efficient Multi-...

[2602.16612] Causal and Compositional Abstraction

The paper presents a formal framework for causal and compositional abstraction, emphasizing its significance in AI and scientific practic...

[2602.16603] FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving

The paper presents FlowPrefill, a novel system designed to optimize large language model (LLM) serving by decoupling preemption from sche...

[2602.16585] DataJoint 2.0: A Computational Substrate for Agentic Scientific Workflows

DataJoint 2.0 introduces a relational workflow model designed to enhance collaboration in scientific data pipelines, ensuring data integr...

[2602.16320] RefineFormer3D: Efficient 3D Medical Image Segmentation via Adaptive Multi-Scale Transformer with Cross Attention Fusion

RefineFormer3D presents a lightweight transformer architecture for 3D medical image segmentation, achieving high accuracy with significan...

[2602.16233] DistributedEstimator: Distributed Training of Quantum Neural Networks via Circuit Cutting

The paper presents a novel approach to distributed training of quantum neural networks using circuit cutting, addressing overheads and pe...

[2602.16430] Designing Production-Scale OCR for India: Multilingual and Domain-Specific Systems

This article discusses the development of production-scale Optical Character Recognition (OCR) systems tailored for India's multilingual ...

[2602.16148] Local adapt-then-combine algorithms for distributed nonsmooth optimization: Achieving provable communication acceleration

This paper introduces FlexATC, a communication-efficient framework for distributed nonsmooth optimization, achieving notable convergence ...

[2602.16132] CHAI: CacHe Attention Inference for text2video

The paper presents CHAI, a novel approach to enhance text-to-video generation by utilizing Cache Attention for efficient inference, achie...

[2602.16113] Evolutionary Context Search for Automated Skill Acquisition

The paper presents Evolutionary Context Search (ECS), a novel method for automated skill acquisition in large language models, enhancing ...

[2602.16086] LGQ: Learning Discretization Geometry for Scalable and Stable Image Tokenization

The paper presents LGQ, a novel image tokenizer that learns discretization geometry to enhance scalability and stability in visual genera...

[2602.16054] CLAA: Cross-Layer Attention Aggregation for Accelerating LLM Prefill

The paper introduces Cross-Layer Attention Aggregation (CLAA) to enhance the efficiency of long-context LLM inference by addressing token...

[2602.16174] Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocation

The paper presents Federated Split Decision Transformers (FSDT) for optimizing resource allocation in mobile edge computing for the metav...

[2602.15996] Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation

This paper explores saddle point reformulation in Vertical Federated Learning (VFL), presenting methods for efficient model training acro...

[2602.16136] Retrieval Collapses When AI Pollutes the Web

The paper discusses the phenomenon of 'Retrieval Collapse,' where AI-generated content dominates search results, leading to a decline in ...

[2602.16124] Rethinking ANN-based Retrieval: Multifaceted Learnable Index for Large-scale Recommendation System

The paper presents a novel approach called MultiFaceted Learnable Index (MFLI) for enhancing ANN-based retrieval in large-scale recommend...

[2602.15894] Quality-constrained Entropy Maximization Policy Optimization for LLM Diversity

This paper presents Quality-constrained Entropy Maximization Policy Optimization (QEMPO), a method to enhance diversity in large language...

[2602.15891] Learning to Drive in New Cities Without Human Demonstrations

This paper presents NOMAD, a novel approach for training autonomous vehicles to navigate new cities without relying on human driving demo...