@inproceedings{su-etal-2026-amadeus,
    title = "Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music",
    author = "Su, Hongju  and
      Li, Ke  and
      Yang, Lan  and
      Zhang, Honggang  and
      Song, Yi-Zhe",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1898/",
    pages = "40910--40928",
    ISBN = "979-8-89176-390-6",
    abstract = "Existing state-of-the-art symbolic music generation models represent symbolic music as a sequence of attribute tokens with fixed unidirectional dependencies. However, from the perspective of music theory, the attributes of a musical note are inherently a set rather than a sequence. Building on this insight, we propose Amadeus, a novel symbolic music generation framework that adopts a two-level architecture: an autoregressive model for note sequences and a bidirectional discrete diffusion model for note attributes. This design enables flexible attribute control and adjustable decoding speed during inference. To further enhance sequential modeling, we introduce the Conditional Information Enhancement Module (CIEM). We also constructed AMD (Amadeus MIDI Dataset){---}the largest open-source symbolic music dataset to date{---}supporting both pre-training and fine-tuning. We trained two models of different scales, Amadeus and Amadeus-M, and conducted extensive experiments, demonstrating substantial improvements over state-of-the-art methods across both objective and subjective metrics."
}