AI Infrastructure

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...

Siemens, NVIDIA hit chip verification milestone for AI

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

[2602.12207] VIRENA: Virtual Arena for Research, Education, and Democratic Innovation

VIRENA is a novel platform designed for controlled experimentation in social media environments, enabling researchers to study human-AI i...

[2602.06142] Protean Compiler: An Agile Framework to Drive Fine-grain Phase Ordering

The Protean Compiler introduces an agile framework for fine-grain phase ordering in compilers, enhancing LLVM's capabilities with machine...

[2502.20063] Strategic Hiring under Algorithmic Monoculture

The paper explores strategic hiring in labor markets dominated by algorithmic evaluation, highlighting the inefficiencies of naive hiring...

[2512.00036] Refined Bayesian Optimization for Efficient Beam Alignment in Intelligent Indoor Wireless Environments

This article presents a refined Bayesian optimization framework for efficient beam alignment in intelligent indoor wireless environments,...

[2602.11320] Efficient Analysis of the Distilled Neural Tangent Kernel

The paper presents a novel approach to reduce the computational complexity of Neural Tangent Kernel (NTK) methods through dataset distill...

[2602.02958] Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization

The paper presents Quant VideoGen, a framework for autoregressive long video generation that addresses the limitations of KV cache memory...

[2509.06085] Software Dependencies 2.0: An Empirical Study of Reuse and Integration of Pre-Trained Models in Open-Source Projects

This article investigates the integration and management of pre-trained models (PTMs) in open-source software projects, introducing the c...

[2601.19320] StableQAT: Stable Quantization-Aware Training at Ultra-Low Bitwidths

The paper presents StableQAT, a novel framework for quantization-aware training (QAT) that enhances stability and efficiency at ultra-low...

[2601.05378] Inverting Non-Injective Functions with Twin Neural Network Regression

This article presents a novel approach to inverting non-injective functions using Twin Neural Network Regression, focusing on locally inv...

[2512.18454] Out-of-Distribution Detection in Molecular Complexes via Diffusion Models for Irregular Graphs

This paper presents a novel framework for out-of-distribution (OOD) detection in molecular complexes using diffusion models tailored for ...

[2512.22623] Communication Compression for Distributed Learning with Aggregate and Server-Guided Feedback

This paper presents novel frameworks for communication compression in distributed learning, addressing bandwidth constraints in federated...

[2512.03363] Adaptive Aggregation with Two Gains in QFL

The paper presents A2G, a novel framework for adaptive aggregation in quantum federated learning, addressing performance issues due to cl...

[2506.08822] FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency

The paper presents FreqPolicy, a novel flow-based visuomotor policy that enhances efficiency in robotic manipulation by imposing frequenc...

[2511.14406] Watch Out for the Lifespan: Evaluating Backdoor Attacks Against Federated Model Adaptation

This paper evaluates backdoor attacks against federated learning model adaptation, focusing on the impact of Low-Rank Adaptation (LoRA) o...

[2505.15801] VerifyBench: Benchmarking Reference-based Reward Systems for Large Language Models

The paper introduces VerifyBench, a new benchmarking framework for evaluating reference-based reward systems in large language models, hi...

[2511.04485] Q3R: Quadratic Reweighted Rank Regularizer for Effective Low-Rank Training

The paper introduces Q3R, a novel Quadratic Reweighted Rank Regularizer designed to enhance low-rank training in deep learning models, ac...

[2504.08603] FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment

The paper presents FindAnything, a framework for open-vocabulary and object-centric mapping that enhances robot exploration in unknown en...

[2510.18478] Safe But Not Sorry: Reducing Over-Conservatism in Safety Critics via Uncertainty-Aware Modulation

This article presents the Uncertain Safety Critic (USC), a novel approach to enhance safety in reinforcement learning (RL) by balancing s...

[2509.15194] Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation

The paper presents EVOL-RL, a novel framework for evolving language models without labels, balancing majority-driven stability with novel...

[2508.12907] SNAP-UQ: Self-supervised Next-Activation Prediction for Single-Pass Uncertainty in TinyML

The paper presents SNAP-UQ, a novel method for single-pass uncertainty estimation in TinyML, enhancing reliability in on-device monitorin...