Generative AI

[D] USQL Joins Were Cool, But Now I Want to Join the GenAI Party

Hi Experts, I have 1.5 years of experience in Data Engineering, and now I want to start learning AI, ML, and Generative AI. I already hav...

Report says Minnesota workers face highest generative AI exposure in the Midwest

A report from North Star Policy Action says Minnesota workers have the highest generative AI exposure in the Midwest and the 10th-highest...

Navigating Recent Developments in Generative AI and Trade Secret Protection

[2512.01389] Syndrome-Flow Consistency Model Achieves One-step Denoising Error Correction Codes

The paper presents the Error Correction Syndrome-Flow Consistency Model (ECCFM), which enhances one-step denoising error correction codes...

[2511.19797] Terminal Velocity Matching

The paper introduces Terminal Velocity Matching (TVM), a novel approach to generative modeling that enhances performance in one- and few-...

[2602.15350] Fine-Tuning LLMs to Generate Economical and Reliable Actions for the Power Grid

This paper discusses a method for fine-tuning large language models (LLMs) to generate effective corrective actions for power grid manage...

[2602.15318] Sparrow: Text-Anchored Window Attention with Visual-Semantic Glimpsing for Speculative Decoding in Video LLMs

The paper introduces Sparrow, a novel framework designed to enhance speculative decoding in Video Large Language Models (Vid-LLMs) by opt...

[2507.01761] Enhanced Generative Model Evaluation with Clipped Density and Coverage

This article presents novel metrics, Clipped Density and Clipped Coverage, aimed at improving the evaluation of generative models by enha...

[2505.18883] Partition Generative Modeling: Masked Modeling Without Masks

The paper introduces Partition Generative Models (PGMs), a novel approach to generative modeling that eliminates mask tokens, improving t...

[2602.15278] Visual Persuasion: What Influences Decisions of Vision-Language Models?

This article explores how visual-language models (VLMs) make decisions based on image inputs, introducing a framework to analyze their pr...

[2602.15241] GenAI for Systems: Recurring Challenges and Design Principles from Software to Silicon

This paper explores the integration of Generative AI in computing systems, identifying recurring challenges and design principles across ...

[2602.15082] S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantization

The paper presents S-PRESSO, a novel sound effect compression model that achieves ultra-low bitrate audio compression using diffusion aut...

[2602.15074] Structure-Aware Piano Accompaniment via Style Planning and Dataset-Aligned Pattern Retrieval

This paper presents a structure-aware method for generating piano accompaniments using a transformer model for style planning and dataset...

[2602.15727] Spanning the Visual Analogy Space with a Weight Basis of LoRAs

The paper presents LoRWeB, a novel approach to visual analogy learning that enhances image manipulation by dynamically selecting and weig...

[2602.15592] Uni-Flow: a unified autoregressive-diffusion model for complex multiscale flows

Uni-Flow presents a novel autoregressive-diffusion model that effectively simulates complex multiscale flows, enhancing the accuracy and ...

[2602.15552] Latent Regularization in Generative Test Input Generation

This paper explores the effects of latent space regularization on the quality of generative test inputs for deep learning classifiers, de...

[2602.15538] Functional Central Limit Theorem for Stochastic Gradient Descent

This paper presents a functional central limit theorem for the trajectory of the stochastic gradient descent (SGD) algorithm applied to c...

[2602.15521] ExpertWeaver: Unlocking the Inherent MoE in Dense LLMs with GLU Activation Patterns

The paper presents ExpertWeaver, a framework that enhances the conversion of dense LLMs into sparse Mixture-of-Experts (MoE) models using...

[2602.15037] CircuChain: Disentangling Competence and Compliance in LLM Circuit Analysis

The paper introduces CircuChain, a benchmark for evaluating large language models (LLMs) in electrical circuit analysis, focusing on thei...

[2602.15451] Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

This article presents a novel framework for optimizing deep generative models in molecular design using quantum annealing, significantly ...

[2602.15449] TAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Models

The TAROT framework enhances code generation in large language models by implementing a test-driven, capability-adaptive reinforcement fi...

[2602.15423] GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search

GaiaFlow presents a novel framework for carbon-efficient search, employing semantic-guided diffusion tuning to balance retrieval accuracy...

[2602.15791] Enhancing Building Semantics Preservation in AI Model Training with Large Language Model Encodings

This article presents a novel approach to enhance building semantics preservation in AI model training using large language model encodin...