arXiv AI

Model Collapse: On Recursion, Noise, and Uncharted Machine Visions

arXiv:2607. 09705v1 Announce Type: cross Abstract: Since 2023, computer scientists have warned against model collapse -- the contamination of training sets with AI-generated outputs that progressively degrade model performance.

arXiv AI
Jun 26

Dream machine -- the next creative economy

arXiv:2606. 26114v1 Announce Type: cross Abstract: We examine the structural transformation of creative industries under generative artificial intelligence, drawing on 374 primary sources spanning policy documents, industry data, creator surveys, and platform analytics.

By Peter Woodbridge, John J. O'Hare
arXiv AI
Jun 30

MuseBench: Benchmarking Intent-Level Audiovisual Arts Understanding in MLLMs

arXiv:2606. 30026v1 Announce Type: cross Abstract: Audiovisual arts encompass diverse creative disciplines, including cinema, visual arts, stage performance, and game design, where artistic meaning arises from deliberate combinations of visual, auditory, and narrative elements (e.

By Yuxuan Fan, Gyusik Seo, Jing Hao, Jaemin Cho, Mohit Bansal, Jaehong Yoon
arXiv AI
Jul 24

Can an AI System Be Creative? A Critical Perspective from Art and Engineering

arXiv:2607. 20796v1 Announce Type: new Abstract: This paper examines the question of whether artificial intelligence (AI) systems can be creative, approached from the dual perspective of a researcher trained in electrical engineering, pattern recognition, machine learning, and neural networks, who has also spent most of his life engaged in the arts as actor, stage and film director, writer, composer, and visual artist, and in philosophy.

By Ivan Magrin-Chagnolleau