collaborators

15 papers

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.LG2026

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…

cs.SD2026

WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning

Gagan Mundada, Yash Vishe, Amit Namburi +4

Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various vision-language tasks. However, their reasoning abilities in th…