Pith Number

pith:PHRTZ7Z7

pith:2026:PHRTZ7Z75U6DBKD4KFMYPZVUWC

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

B.J.F. van Beijnum, Bo Cui, Monique Tabak, Shunzhe Zhang, Xiaowen Song, Yaowen Zhang, Ying Wang

A 0.09B model maps discrete latent manifolds to translate PPG into ECG with 0.83 R-peak F1 and super-resolve frequencies to 0.9956 correlation.

arxiv:2605.13248 v1 · 2026-05-13 · eess.SP · cs.AI

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\usepackage{pith}
\pithnumber{PHRTZ7Z75U6DBKD4KFMYPZVUWC}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Our 0.09B model significantly outperforms massive baselines. In cross-modal PPG-to-ECG synthesis, it resolves temporal phase drift and dramatically improves the clinical R-peak detection F1-score from 0.37 (baseline) to 0.83. Furthermore, in extreme cross-frequency super-resolution (25Hz to 100Hz), it successfully recovers high-frequency diagnostic landmarks, achieving an unprecedented Pearson correlation of 0.9956.

C2weakest assumption

That the Hierarchical Residual Vector Quantization produces truly isolated discrete latent manifolds that preserve all clinically relevant information without modality-specific loss, and that the reported baseline comparisons use equivalent training regimes and data.

C3one line summary

A compact 0.09B model using hierarchical discrete tokenization and prompted latent translation outperforms larger baselines in cross-modal PPG-to-ECG synthesis and cross-frequency super-resolution.

References

29 extracted · 29 resolved · 1 Pith anchors

[1] Personalized Medicine , volume= 2018

[2] npj Digital Medicine , volume= 2020

[3] Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

[4] Advances in Neural Information Processing Systems (NeurIPS) , year=

[5] Multi-scale masked autoencoder for electrocardiogram anomaly detection.arXiv preprint arXiv:2502.05494

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T02:44:49.451593Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

79e33cff3fed3c30a87c515987e6b4b08d87663a44159e5d9e0dd1794f215af8

Aliases

arxiv: 2605.13248 · arxiv_version: 2605.13248v1 · doi: 10.48550/arxiv.2605.13248 · pith_short_12: PHRTZ7Z75U6D · pith_short_16: PHRTZ7Z75U6DBKD4 · pith_short_8: PHRTZ7Z7

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/PHRTZ7Z75U6DBKD4KFMYPZVUWC \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 79e33cff3fed3c30a87c515987e6b4b08d87663a44159e5d9e0dd1794f215af8

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2a862463322e1910057ee95027cc00306794fcc73c9e17315a17f51680b30d1f",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-13T09:31:36Z",
    "title_canon_sha256": "ddc6466b9f7dca9cb2768f5926a7ff35c890ce0c4d69b3f7d037b13e4ab47b7b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13248",
    "kind": "arxiv",
    "version": 1
  }
}