Pith Number

pith:YTTTLAVE

pith:2026:YTTTLAVEAUMS6F46SSEV4FDMHB

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Quantifying information flow along a stochastic trajectory

Euijoon Kwon, Yongjae Oh, Yongjoo Baek

Deep learning enables estimation of information flow along individual stochastic trajectories from time-series data.

arxiv:2605.13509 v1 · 2026-05-13 · cond-mat.stat-mech

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations 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

we propose a scalable deep-learning method for estimating the SIF from general time-series data

C2weakest assumption

That a neural network trained on simulated or limited data can accurately recover the true trajectory-level information flow for arbitrary unseen stochastic processes without model-specific tuning or overfitting.

C3one line summary

A scalable deep-learning estimator for trajectory-level stochastic information flow is proposed and tested on solvable models, oscillators, and motile cell trajectories.

References

75 extracted · 75 resolved · 0 Pith anchors

[1] Sekimoto, Langevin equation and thermodynamics, Prog 1998

[2] Seifert, Stochastic thermodynamics, fluctuation the- orems and molecular machines, Rep 2012

[3] D. J. Evans, E. G. D. Cohen, and G. P. Morriss, Proba- bility of second law violations in shearing steady states, Phys. Rev. Lett.71, 2401 (1993) 1993

[4] Jarzynski, Nonequilibrium equality for free energy dif- ferences, Phys 1997

[5] G. E. Crooks, Entropy production fluctuation theorem and the nonequilibrium work relation for free energy dif- ferences, Phys. Rev. E60, 2721 (1999) 1999

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-18T02:44:24.595151Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c4e73582a405192f179e94895e146c3874ce5a3c927235099462e43b3f7c2d3a

Aliases

arxiv: 2605.13509 · arxiv_version: 2605.13509v1 · doi: 10.48550/arxiv.2605.13509 · pith_short_12: YTTTLAVEAUMS · pith_short_16: YTTTLAVEAUMS6F46 · pith_short_8: YTTTLAVE

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YTTTLAVEAUMS6F46SSEV4FDMHB \
  | 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: c4e73582a405192f179e94895e146c3874ce5a3c927235099462e43b3f7c2d3a

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "ecb06f3d4c6c155ee97100e65d3e2c746a75f09ff5c2fc793de8c7d7d4650fc9",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cond-mat.stat-mech",
    "submitted_at": "2026-05-13T13:28:17Z",
    "title_canon_sha256": "8223486e87b0a27ab27fb4523ba1d3d8b4a528a2f6b6246c47fe853f093a073b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13509",
    "kind": "arxiv",
    "version": 1
  }
}