Generative AI

Will Generative AI apps remain a revenue powerhouse in 2026?

[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...

[2512.04552] RRPO: Robust Reward Policy Optimization for LLM-based Emotional TTS

The paper presents Robust Reward Policy Optimization (RRPO), a novel framework designed to enhance emotional text-to-speech (TTS) systems...

[2509.20928] Conditionally Whitened Generative Models for Probabilistic Time Series Forecasting

The paper introduces Conditionally Whitened Generative Models (CW-Gen) for probabilistic time series forecasting, addressing challenges l...

[2510.22876] Batch Speculative Decoding Done Right

The paper presents a novel framework for batch speculative decoding, addressing critical failures in existing methods and achieving signi...

[2507.07139] Image Can Bring Your Memory Back: A Novel Multi-Modal Guided Attack against Image Generation Model Unlearning

The paper presents Recall, a novel adversarial framework that targets the robustness of image generation model unlearning, revealing vuln...

[2510.04398] SECA: Semantically Equivalent and Coherent Attacks for Eliciting LLM Hallucinations

The paper presents SECA, a method for eliciting hallucinations in large language models (LLMs) through semantically equivalent and cohere...

[2510.02356] Measuring Physical-World Privacy Awareness of Large Language Models: An Evaluation Benchmark

This article presents EAPrivacy, a benchmark for evaluating the physical-world privacy awareness of large language models (LLMs), reveali...

[2510.00232] BiasFreeBench: a Benchmark for Mitigating Bias in Large Language Model Responses

The paper introduces BiasFreeBench, a benchmark designed to evaluate bias mitigation techniques in large language models (LLMs) by provid...

[2509.18776] AECBench: A Hierarchical Benchmark for Knowledge Evaluation of Large Language Models in the AEC Field

The paper introduces AECBench, a benchmark for evaluating large language models (LLMs) in the Architecture, Engineering, and Construction...

[2503.10522] AudioX: A Unified Framework for Anything-to-Audio Generation

AudioX presents a unified framework for generating audio from various multimodal inputs, enhancing the quality and flexibility of audio g...

[2411.01629] Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity

This paper explores denoising diffusions using optimal transport, focusing on localization, curvature, and multi-scale complexity in gene...

[2508.21285] A Financial Brain Scan of the LLM

This article presents a novel approach to analyzing large language models (LLMs) in finance, enabling researchers to identify and manipul...

[2508.18210] Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation

This article presents a diagnostic framework for evaluating synthetic dialogue generation in contact centers, highlighting the limitation...

[2307.14397] A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot

This survey explores generative modeling under constraints of limited data, few shots, and zero shots, presenting challenges and methodol...

[2507.04704] SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes

The paper introduces SPATIA, a novel multimodal model for predicting spatial cell phenotypes by integrating cellular morphology, gene exp...

[2602.06801] On the Non-Identifiability of Steering Vectors in Large Language Models

This paper explores the non-identifiability of steering vectors in large language models (LLMs), revealing that these vectors cannot be u...

[2506.04051] High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning

The paper presents HALT, a method for finetuning large language models (LLMs) to enhance reliability by generating responses only when co...

[2602.05319] Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective

This paper introduces Accelerated Sequential Flow Matching, a Bayesian filtering framework that enhances real-time inference in stochasti...

[2506.03407] Multi-Spectral Gaussian Splatting with Neural Color Representation

The paper presents MS-Splatting, a novel multi-spectral 3D Gaussian Splatting framework that generates consistent views from images captu...

[2602.00628] From Associations to Activations: Comparing Behavioral and Hidden-State Semantic Geometry in LLMs

This paper examines the relationship between behavioral and hidden-state semantic geometry in large language models (LLMs) through psycho...

[2505.07861] Scalable LLM Reasoning Acceleration with Low-rank Distillation

The paper presents Caprese, a low-rank distillation method designed to enhance reasoning capabilities in large language models (LLMs) whi...