Pith Number

pith:CSQJGVMC

pith:2026:CSQJGVMCKINNW2GPG2Q6EO2WB5

not attested not anchored not stored refs resolved

Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics

Andrew Campbell, Lanshuang Zhang, Md Mofijul Islam, Peter Kotanko, Rakesh Malhotra, Siwei Zhao, Subhasis Dasgupta, Tauhidur Rahman, Xi Chen, Yuliang Chen, Yunfei Luo

NormWear-2 models physiological signals and interventions as joint latent dynamics for multi-scale forecasting.

arxiv:2605.15465 v1 · 2026-05-14 · cs.LG · eess.SP

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\usepackage{pith}
\pithnumber{CSQJGVMCKINNW2GPG2Q6EO2WB5}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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

NormWear-2 achieves the best overall forecasting performance across time, frequency, and latent representation domains, with significant improvements over state-of-the-art time series foundation models, while maintaining competitive downstream representation quality.

C2weakest assumption

That chaos-theoretic balancing of dynamical regime diversity during pretraining produces more robust latent representations that generalize across heterogeneous temporal resolutions, intervention regimes, and real-world datasets from daily life to clinical settings.

C3one line summary

NormWear-2 encodes physiological signals and interventions into a shared latent space, models their joint evolution as a dynamical system, and uses chaos-theoretic balancing during pretraining to achieve superior multi-scale forecasting on diverse real-world datasets.

References

18 extracted · 18 resolved · 3 Pith anchors

[1] Chronos-2: From Univariate to Universal Forecasting · arXiv:2510.15821

[2] Tirex: Zero-shot forecasting across long and short horizons with enhanced in-context learning

[3] T., Jiang, J., Jayaraman, P., Parekh, A., Nadkarni, G 2025

[4] Masked Autoencoders Are Scalable Vision Learners · arXiv:2111.06377

[5] arXiv preprint arXiv:2312.05230 , year=

Receipt and verification

First computed	2026-05-20T00:01:00.035861Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

14a0935582521adb68cf36a1e23b560f5c4fad3d142b87eec7dd18675345fc38

Aliases

arxiv: 2605.15465 · arxiv_version: 2605.15465v1 · doi: 10.48550/arxiv.2605.15465 · pith_short_12: CSQJGVMCKINN · pith_short_16: CSQJGVMCKINNW2GP · pith_short_8: CSQJGVMC

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/CSQJGVMCKINNW2GPG2Q6EO2WB5 \
  | 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: 14a0935582521adb68cf36a1e23b560f5c4fad3d142b87eec7dd18675345fc38

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "72c6e809f12a280cd0ab6603f06416b08185f9563b029615c502f0790d790432",
    "cross_cats_sorted": [
      "eess.SP"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T23:06:15Z",
    "title_canon_sha256": "fa48e22992582b1b7e5f2f7d482542b7599d7c6db5efcd62eb8a47c23aaf7d1d"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15465",
    "kind": "arxiv",
    "version": 1
  }
}