Real-Time Language Model Jamming: A Case Study for Live Music Accompaniment Generation

research work·research paper·active

Research work examining automatic accompaniment.

Recorded facts

Official site	https://arxiv.org/abs/2606.11886 ↗
Geography	Global
arxiv	2606.11886
venue	Accepted to RTAS 2026. 14 pages, 5 figures, 3 tables
authors	Bowen Zheng; Andrew H. Yang; Jiaqi Ruan; Jia He; Xinyue Li; Yuan-Hsin Chen; Ziyu Wang; Xiaosong Ma
methods	language modeling
code urls	unknown
demo urls	https://stream-muse-webpage.vercel.app/#audio-library
exact title	Real-Time Language Model Jamming: A Case Study for Live Music Accompaniment Generation
project urls	https://stream-muse-webpage.vercel.app/#audio-library
original title	unknown
citation counts	unknown
research topics	automatic accompaniment
peer review status	venue or acceptance claim recorded in arXiv comment
disclosed conflicts	unknown
stated contribution	Presents or evaluates the system, method, benchmark, or analysis identified in the paper title.
us market scope basis	Included as a materially relevant public research artifact in the US-facing AI-music ecosystem; direct affiliation varies.
funding acknowledgements	unknown
affiliations at publication	unknown
publication or preprint date	2026-06-10
abstract level neutral summary	The work studies automatic accompaniment; methods and evaluation details are in the official abstract.
datasets benchmarks models tools used	unknown
correction withdrawal retraction status	No withdrawal marker observed in captured arXiv metadata.