GeoRVQ: Decoder-aware geometry for residual-token prediction in physiological signals

2026-09-22

AI Summary

为了解决生理信号中残差token预测问题，提出GeoRVQ方法，通过引入解码器感知的几何结构优化了传统RVQ模型，提高了预测精度和波形保真度。

Abstract

Residual vector quantization (RVQ) turns physiological waveforms into compact token sequences, but conventional masked modeling treats every incorrect token as equally costly. We propose GeoRVQ, a coarse-to-fine masked token model whose objective reflects the local response of a frozen waveform decoder. Decoder-induced costs define geometry-aware soft targets and expected distortion, while quantizer-causal prediction follows residual dependencies from coarse to fine levels. In a descriptive aggregate over MIMIC-IV Waveform, VitalDB, and CODE-15\%, GeoRVQ increases exact token accuracy from $.133\pm.004$ to $.143\pm.003$, reduces decoded distance from $.606\pm.006$ to $.393\pm.007$, and increases R-peak F1 from $.784\pm.004$ to $.837\pm.008$ under matched model and training conditions. Across 45 held-out code substitutions, decoder-induced cost has a Spearman correlation of $.85$ with realized decoded cost, compared with $.54$ for Euclidean codeword distance. These results indicate that decoder-aware objectives can improve waveform and event preservation without requiring a large increase in exact token accuracy.

Problem

• Residual Vector Quantization
• Physiological Signals
• Masked Modeling
• Decoder-aware
• Token Prediction

Innovation

• decoder-aware
• geometry-aware soft targets
• expected distortion
• quantizer-causal prediction
• coarse-to-fine