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

[2602.15772] Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models

This paper explores the optimization dilemma in multimodal models, where enhancing generative capabilities often compromises understandin...

[2510.00565] Toward Safer Diffusion Language Models: Discovery and Mitigation of Priming Vulnerability

This paper explores vulnerabilities in diffusion language models (DLMs) related to priming attacks and proposes a novel safety alignment ...

[2602.15758] ChartEditBench: Evaluating Grounded Multi-Turn Chart Editing in Multimodal Language Models

The paper presents ChartEditBench, a benchmark for evaluating multi-turn chart editing in multimodal language models, highlighting challe...

[2509.16779] Improving User Interface Generation Models from Designer Feedback

This paper explores enhancing user interface (UI) generation models by incorporating designer feedback, demonstrating improved performanc...

[2508.20373] NPG-Muse: Scaling Long Chain-of-Thought Reasoning with NP-Hard Graph Problems

The paper presents NPG-Muse, a novel approach to enhance long chain-of-thought reasoning in large language models using NP-hard graph pro...

[2602.15698] How to Disclose? Strategic AI Disclosure in Crowdfunding

The article examines the impact of strategic AI disclosure in crowdfunding, revealing that mandatory disclosure can significantly reduce ...

[2507.08333] Token-Based Audio Inpainting via Discrete Diffusion

This article presents a novel method for audio inpainting using discrete diffusion techniques to restore missing segments in audio record...

[2602.15689] A Content-Based Framework for Cybersecurity Refusal Decisions in Large Language Models

This paper presents a content-based framework for cybersecurity refusal decisions in large language models, emphasizing the need for expl...

[2506.19923] Prover Agent: An Agent-Based Framework for Formal Mathematical Proofs

The paper introduces Prover Agent, an AI framework that combines large language models with formal proof assistants to enhance automated ...

[2602.15678] Revisiting Northrop Frye's Four Myths Theory with Large Language Models

This paper explores Northrop Frye's Four Myths Theory through the lens of Large Language Models (LLMs), proposing a character function fr...

[2505.05736] Multimodal Integrated Knowledge Transfer to Large Language Models through Preference Optimization with Biomedical Applications

The paper introduces MINT, a framework for optimizing large language models (LLMs) using multimodal biomedical data to enhance predictive...

[2602.15620] STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens

The paper presents STAPO, a novel approach to stabilize reinforcement learning in large language models by silencing rare spurious tokens...

[2602.15539] Dynamic Training-Free Fusion of Subject and Style LoRAs

The paper presents a novel dynamic training-free fusion framework for combining subject and style LoRAs in generative models, enhancing c...

[2602.08032] Horizon Imagination: Efficient On-Policy Rollout in Diffusion World Models

The paper presents Horizon Imagination (HI), an innovative on-policy imagination process for reinforcement learning using diffusion-based...

[2602.15513] Improving MLLMs in Embodied Exploration and Question Answering with Human-Inspired Memory Modeling

This paper presents a novel non-parametric memory framework for improving Multimodal Large Language Models (MLLMs) in embodied exploratio...

[2601.11440] GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance

The paper presents GenDA, a generative data assimilation framework for reconstructing urban wind fields from sparse sensor data, enhancin...

[2601.02799] Stratified Hazard Sampling: Minimal-Variance Event Scheduling for CTMC/DTMC Discrete Diffusion and Flow Models

This article presents Stratified Hazard Sampling (SHS), a novel method for improving event scheduling in discrete diffusion and flow mode...

[2602.15377] Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework

This paper presents an orchestration-free framework for customer service automation, utilizing Task-Oriented Flowcharts (TOFs) to enhance...

[2602.15373] Far Out: Evaluating Language Models on Slang in Australian and Indian English

This paper evaluates the performance of language models on slang in Australian and Indian English, revealing significant gaps in understa...

[2512.19057] Efficient Personalization of Generative Models via Optimal Experimental Design

This paper presents a novel method for efficiently personalizing generative models using optimal experimental design to select preference...