Pith Number

pith:LLGV7HUY

pith:2026:LLGV7HUY36EI2H47AFQZXNWBDR

not attested not anchored not stored refs resolved

Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with 'nonconform'

Christine Preisach, Maximilian Kirsch, Oliver Hennh\"ofer

The nonconform package converts anomaly scores into calibrated p-values valid under data exchangeability.

arxiv:2605.13642 v1 · 2026-05-13 · stat.ML · cs.LG · stat.CO

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

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{LLGV7HUY36EI2H47AFQZXNWBDR}

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

Empirical results demonstrate that the implemented methods enable statistically principled anomaly detection.

C2weakest assumption

The p-values remain valid only under the assumption of data exchangeability stated in the abstract.

C3one line summary

nonconform is a Python package implementing conformal anomaly detection to replace heuristic thresholds with statistically calibrated p-values and FDR control.

References

38 extracted · 38 resolved · 0 Pith anchors

[1] Controlling the false discovery rate: A practical and powerful approach to multiple testing 1995 · doi:10.1111/j.2517-6161.1995.tb02031.x

[2] Simultaneous statistical inference , author =

[3] Algorithmic learning in a random world , author =

[4] 2024 IEEE International Conference on Knowledge Graph (ICKG) , publisher = 2024 · doi:10.1109/ickg63256.2024.00022

[5] Proceedings of the 34th International Conference on Neural Information Processing Systems , location =

Receipt and verification

First computed	2026-05-18T02:44:17.585115Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5acd5f9e98df888d1f9f01619bb6c11c67a91f53bf0fc41c06af161fda16a882

Aliases

arxiv: 2605.13642 · arxiv_version: 2605.13642v1 · doi: 10.48550/arxiv.2605.13642 · pith_short_12: LLGV7HUY36EI · pith_short_16: LLGV7HUY36EI2H47 · pith_short_8: LLGV7HUY

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/LLGV7HUY36EI2H47AFQZXNWBDR \
  | 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: 5acd5f9e98df888d1f9f01619bb6c11c67a91f53bf0fc41c06af161fda16a882

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "50f0f47c769e227d6b7c8a6fbfd412d0e8be921d7f409bea3911026232facccb",
    "cross_cats_sorted": [
      "cs.LG",
      "stat.CO"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.ML",
    "submitted_at": "2026-05-13T15:05:24Z",
    "title_canon_sha256": "fa09ec56bc0ba0b048615f6b1cf08f86db790efd83dc7678ddc85c88a0d952ef"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13642",
    "kind": "arxiv",
    "version": 1
  }
}