Beyond Reconstruction: Understanding Discrete Representations for Autoregressive Wearable Sensor Data Generation

Discrete tokenization provides an interface between continuous wearable sensor data and autoregressive sequence models. Existing wearable sensor tokenizers are commonly optimized and evaluated for reconstruction, yet whether reconstruction quality is a reliable indicator of downstream autoregressive generation performance remains unclear. We systematically investigate this question using three vector-quantized tokenizers and T5-based autoregressive models. We find that reconstruction fidelity does not consistently reflect downstream generation quality. We further analyze token prediction errors and find that larger target–prediction codeword distances are consistently associated with larger signal-level distortions after decoding. Motivated by this finding, we propose HierCode, a coarse-to-fine generation framework that groups nearby codewords of a pretrained tokenizer into clusters. HierCode first autoregressively generates cluster tokens and then recovers fine-grained tokens in parallel conditioned on the generated cluster sequence. To improve robustness to imperfect cluster predictions, the recovery model is trained with perturbed cluster sequences and incorporates the coarse model's predictive distribution as a soft cluster prior at inference. Across five wearable sensing benchmarks and three tokenizer architectures, HierCode improves over direct fine-token autoregressive generation in the majority of evaluated settings.