# Time-Shifted Token Scheduling for Symbolic Music Generation

arXiv: https://arxiv.org/abs/2509.23749 · HTML: https://mlsanity.com/paper/2509.23749/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs](https://mlsanity.com/paper/2101.02402/) (2021)
- [MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training](https://mlsanity.com/paper/2106.05630/) (2021)
- [The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measures](https://mlsanity.com/paper/2008.01307/) (2020)
- [EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation](https://mlsanity.com/paper/2108.01374/) (2021)
- [MMM : Exploring Conditional Multi-Track Music Generation with the Transformer](https://mlsanity.com/paper/2008.06048/) (2020)
- [MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAE](https://mlsanity.com/paper/2105.04090/) (2021)
- [Theme Transformer: Symbolic Music Generation with Theme-Conditioned Transformer](https://mlsanity.com/paper/2111.04093/) (2021)
- [MusPy: A Toolkit for Symbolic Music Generation](https://mlsanity.com/paper/2008.01951/) (2020)
- [Symphony Generation with Permutation Invariant Language Model](https://mlsanity.com/paper/2205.05448/) (2022)
- [Multitrack Music Transformer](https://mlsanity.com/paper/2207.06983/) (2022)
- [Compose & Embellish: Well-Structured Piano Performance Generation via A Two-Stage Approach](https://mlsanity.com/paper/2209.08212/) (2022)
- [Automatic Composition of Guitar Tabs by Transformers and Groove Modeling](https://mlsanity.com/paper/2008.01431/) (2020)
- [Variable-Length Music Score Infilling via XLNet and Musically Specialized Positional Encoding](https://mlsanity.com/paper/2108.05064/) (2021)
- [PiCoGen: Generate Piano Covers with a Two-stage Approach](https://mlsanity.com/paper/2407.20883/) (2024)
- [PiCoGen2: Piano cover generation with transfer learning approach and weakly aligned data](https://mlsanity.com/paper/2408.01551/) (2024)
- [Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation](https://mlsanity.com/paper/2407.20955/) (2024)
- [Nested Music Transformer: Sequentially Decoding Compound Tokens in Symbolic Music and Audio Generation](https://mlsanity.com/paper/2408.01180/) (2024)
- [METEOR: Melody-aware Texture-controllable Symbolic Orchestral Music Generation via Transformer VAE](https://mlsanity.com/paper/2409.11753/) (2024)

## Built on by (most cited first, 2 of 2)

- [DigitCode: Symbolic Tokenization of Hand Motion by Anatomical Units](https://mlsanity.com/paper/2608.03127/) (2026)
- [Equivariant Music Transformer](https://mlsanity.com/paper/2608.03920/) (2026)

## Abstract (arXiv)

Symbolic music generation faces a fundamental trade-off between efficiency and quality. Fine-grained tokenizations achieve strong coherence but incur long sequences and high complexity, while compact tokenizations improve efficiency at the expense of intra-token dependencies. To address this, we adapt a delay-based scheduling mechanism (DP) that expands compound-like tokens across decoding steps, enabling autoregressive modeling of intra-token dependencies while preserving efficiency. Notably, DP is a lightweight strategy that introduces no additional parameters and can be seamlessly integrated into existing representations. Experiments on symbolic orchestral MIDI datasets show that our method improves all metrics over standard compound tokenizations and narrows the gap to fine-grained tokenizations.