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20242026
most citedSteering Autoregressive Music Generation with Recursive Feature Machines

1 citations · 1 across the 6 of their papers we have counts for

collaborators

20 papers

cs.SD2026

MusPyExpress: Extending MusPy with Enhanced Expression Text Support

Phillip Long, Hao-Wen Dong, Julian McAuley +1

Current work in modeling symbolic music primarily relies on representations extracted from MIDI-like data. While such formats allow for modeling symbolic music as sequences of note…

cs.SD2026

Local Multimodal Music Alignment from Global Supervision

Irmak Bukey, Zachary Novack, Jongmin Jung +2

Understanding music requires understanding localized relationships across data modalities, e.g., how time in performance audio maps onto position in a score image. Yet supervision…

cs.LG2026

Parcae: Scaling Laws For Stable Looped Language Models

Hayden Prairie, Zachary Novack, Taylor Berg-Kirkpatrick +1

Traditional fixed-depth architectures scale quality by increasing training FLOPs, typically through increased parameterization, at the expense of a higher memory footprint, or data…

cs.LG20261 cited

Steering Autoregressive Music Generation with Recursive Feature Machines

Daniel Zhao, Daniel Beaglehole, Taylor Berg-Kirkpatrick +2

Controllable music generation remains a significant challenge, with existing methods often requiring model retraining or introducing audible artifacts. We introduce MusicRFM, a fra…

cs.AI2026

Zephyrus: An Agentic Framework for Weather Science

Sumanth Varambally, Marshall Fisher, Jas Thakker +14

Foundation models for weather science are pre-trained on vast amounts of structured numerical data and outperform traditional weather forecasting systems. However, these models lac…

cs.SD2026

MusiCRS: Benchmarking Audio-Centric Conversational Recommendation

Rohan Surana, Amit Namburi, Gagan Mundada +4

Conversational recommendation has advanced rapidly with large language models (LLMs), yet music remains a uniquely challenging domain in which effective recommendations require rea…