Compact Latent Manifold Translation: A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis

2026-05-13 · Source: Takara TLDR - Daily AI Papers · Field: Health & Wellbeing — Medical Devices & Health Technology, Clinical Care & Medical Practice, Health & Medical Research · Depth: Expert, medium

Summary

Compact Latent Manifold Translation (CLMT) is a parameter-efficient foundation model, with only 0.09B parameters, designed to synthesize physiological signals across different modalities and frequencies. It addresses challenges like modality entanglement and high computational costs in existing models by employing a two-stage discrete translation paradigm. First, a Universal Tokenizer uses Hierarchical Residual Vector Quantization (RVQ) to convert heterogeneous signals into isolated discrete latent manifolds, preventing inter-modality interference. Second, a Context-Prompted Latent Translator maps these discrete tokens across modalities, incorporating static physiological priors to reframe signal synthesis as a latent sequence translation task. CLMT significantly outperforms larger baselines, improving clinical R-peak detection F1-score from 0.37 to 0.83 in PPG-to-ECG synthesis and achieving a 0.9956 Pearson correlation in 25Hz to 100Hz cross-frequency super-resolution.

Key takeaway

For AI Scientists and Machine Learning Engineers developing medical foundation models, CLMT demonstrates that highly parameter-efficient architectures can achieve superior cross-modal and cross-frequency physiological signal synthesis. You should consider discrete latent manifold translation and hierarchical vector quantization to overcome modality entanglement and enable edge-device deployment, potentially reducing computational footprints while enhancing diagnostic accuracy.

Key insights

CLMT uses a two-stage discrete translation to synthesize physiological signals across modalities and frequencies efficiently.

Principles

• Decouple heterogeneous signals into discrete latent manifolds.
• Reframe signal synthesis as latent sequence translation.

Method

CLMT employs a Universal Tokenizer with Hierarchical RVQ for signal decoupling, followed by a Context-Prompted Latent Translator that maps discrete tokens across modalities using physiological priors.

In practice

• Improve R-peak detection in PPG-to-ECG synthesis.
• Recover high-frequency diagnostic landmarks in super-resolution.

Topics

• Compact Latent Manifold Translation
• Physiological Signal Synthesis
• Cross-Modal Signal Translation
• Cross-Frequency Super-Resolution
• Hierarchical Residual Vector Quantization

Code references

• Vchitect/TACA

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.