Pith Number

pith:BTJRS7L5

pith:2026:BTJRS7L5U2ZLKWGLBRIZQSBPAK

not attested not anchored not stored refs resolved

Logical Grammar Induction via Graph Kolmogorov Complexity: A Neuro-Symbolic Framework for Self-Healing Clinical Data Integrity

Abolfazl Zarghani, Amir Malekesfandiari

Clinical records form a logical grammar whose violations expand graph Kolmogorov complexity and reveal data corruption.

arxiv:2605.15242 v1 · 2026-05-14 · cs.LG

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{BTJRS7L5U2ZLKWGLBRIZQSBPAK}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

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

Logic-GNN achieves an F1-score of 0.94, outperforming state-of-the-art baselines by 12% in distinguishing between life-threatening medical outliers and data corruption on the Sina System dataset (2M+ records).

C2weakest assumption

Clinical records can be productively modeled as a structured 'private language' governed by latent logical games whose violations are reliably detected by expansion in graph Kolmogorov complexity / MDL (stated in the abstract as the basis for anomaly definition).

C3one line summary

Logic-GNN induces symbolic grammars from clinical graphs via TGNN and Graph Kolmogorov Complexity, defining anomalies as MDL-expanding grammatical violations and reporting 0.94 F1 on the Sina dataset.

References

13 extracted · 13 resolved · 0 Pith anchors

[4] A Novel Anomaly Detection Using Autoencoders on Contaminated Data, 2024

[5] L. Wu, P . Cui, J. Pei, and L. Zhao,Graph Neural Networks: Founda- tions, Frontiers, and Applications, Springer, 2022 2022

[6] Interpretability in Graph Neural Networks, 2022

[7] M. Li and P . Vit ´anyi,An Introduction to Kolmogorov Complexity and Its Applications, 3rd ed., Springer, 2008 2008

[8] Adaptive Sliding Window Optimization for Multi- Dimensional Data Streams Using Reinforcement Learning, 2024

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:48.088724Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0cd3197d7da6b2b558cb0c5198482f02afa83aed808ea44c392d96880ed679a7

Aliases

arxiv: 2605.15242 · arxiv_version: 2605.15242v1 · doi: 10.48550/arxiv.2605.15242 · pith_short_12: BTJRS7L5U2ZL · pith_short_16: BTJRS7L5U2ZLKWGL · pith_short_8: BTJRS7L5

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/BTJRS7L5U2ZLKWGLBRIZQSBPAK \
  | 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: 0cd3197d7da6b2b558cb0c5198482f02afa83aed808ea44c392d96880ed679a7

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7e089236436c01cb4544a472bead391cadca6728fc09ca44a4f93c77b700e4e1",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T06:19:43Z",
    "title_canon_sha256": "296768a486d8e0d0ae5964a9eda4bc284ab2d80a983edaadea500c7525239624"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15242",
    "kind": "arxiv",
    "version": 1
  }
}