Anchored Cyclic Generation: A Novel Paradigm for Long-Sequence Symbolic Music Generation
Generating long sequences with structural coherence remains a fundamental challenge for autoregressive models across sequential generation tasks. In symbolic music generation, this challenge is particularly pronounced, as existing methods are constrained by the inherent severe error accumulation problem of autoregressive models, leading to poor performance in music quality and structural integrity. In this paper, we propose the Anchored Cyclic Generation (ACG) paradigm, which relies on anchor features from already identified music to guide subsequent generation during the autoregressive process, effectively mitigating error accumulation in autoregressive methods. Based on the ACG paradigm...
| Authors | Boyu Cao, Lekai Qian, Dehan Li et al. |
|---|---|
| Primary category | cs.SD |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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