Whole-Piece Training for Symbolic Music Language Models via Full-Horizon Compressed Recurrence

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, we further propose the Hierarchical Anchored Cyclic Generation (Hi-ACG) framework, which employs a systematic global-to-local generation strategy and is highly compatible with our specifically designed piano token, an efficient musical representation. The experimental results demonstrate that compared to traditional autoregressive models, the ACG paradigm achieves reduces cosine distance by an average of 34.7% between predicted feature vectors and ground-truth semantic vectors. In long-sequence symbolic music generation tasks, the Hi-ACG framework significantly outperforms existing mainstream methods in both subjective and objective evaluations. Furthermore, the framework exhibits excellent task generalization capabilities, achieving superior performance in related tasks such as music completion.

We investigate how predictive performance depends on the maximum context available to an autoregressive model of symbolic music, and what information long-context models exploit. A small causal Transformer is trained separately at each context length T in {6, 18, 48, 96, 192, 336} and evaluated on the same target positions, both over raw tokens and over non-overlapping binary latent codes. On Nottingham folk tunes, the token predictor's test NLL decreases by 71% (0.986 bits per token) between 6 and 336 tokens, and by 71% on the O'Neill's tunes long enough for the same sweep (129 of 302 test tunes). Most of the long-context gain is explained by exact copying: the improvement appears when an earlier occurrence of the target's 16-token history enters the available context; overwriting that occurrence removes the gain, whereas equally large unrelated corruption does not; and a simple copy baseline recovers 94% of the reduction. A trained 336-token model likewise loses most of this gain when its history is restricted to recent tokens at test time. Because many repeats arise from written repeat signs expanded in the score, this result pertains specifically to these rendered score representations. By contrast, on MAESTRO performances and MusicNet scores, where exact repeats are substantially less frequent, the reduction is smaller (9% and 18%) and is largely attained by 96 tokens. Finally, an audit of an earlier draft that reported saturation at 16 tokens identified split leakage, overlapping latent receptive fields, an averaging predictor, and a per-file tempo grid; we present these as methodological checks for context-length measurements.

Symbolic music generation is a challenging task in multimedia generation, involving long sequences with hierarchical temporal structures, long-range dependencies, and fine-grained local details. Though recent diffusion-based models produce high quality generations, they tend to suffer from high training and inference costs with long symbolic sequences due to iterative denoising and sequence-length-related costs. To deal with such problem, we put forth a diffusing strategy named SMDIM to combine efficient global structure construction and light local refinement. SMDIM uses structured state space models to capture long range musical context at near linear cost, and selectively refines local musical details via a hybrid refinement scheme. Experiments performed on a wide range of symbolic music datasets which encompass various Western classical music, popular music and traditional folk music show that the SMDIM model outperforms the other state-of-the-art approaches on both the generation quality and the computational efficiency, and it has robust generalization to underexplored musical styles. These results show that SMDIM offers a principled solution for long-sequence symbolic music generation, including associated attributes that accompany the sequences. We provide a project webpage with audio examples and supplementary materials at https://3328702107.github.io/smdim-music/.