Pith Number

pith:HQISPW25

pith:2026:HQISPW25N3F3ZCUUD4BULSRVC2

not attested not anchored not stored refs pending

Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging

Bernhard Kainz, Cosmin Bercea, Johanna P Mueller, Matthew Baugh

Wasserstein distances let vision-language models pick moderately similar normal anatomy references to localize anomalies in medical images.

arxiv:2605.05161 v2 · 2026-05-06 · cs.CV

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

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{HQISPW25N3F3ZCUUD4BULSRVC2}

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

On the NOVA brain MRI benchmark, WALDO with Qwen2.5-VL-72B achieves 43.5±1.6% mAP@30 (95% CI: [40.4, 46.7]), representing a 19% relative improvement over zero-shot baselines. Cross-model evaluation shows consistent gains: GPT-4o achieves 32.0±6.5% and Qwen3-VL-32B achieves 32.0±6.6% mAP@30. Paired McNemar tests confirm statistical significance (p<0.01).

C2weakest assumption

That reference images drawn from DINOv2 patch distributions of normal anatomy, selected via moderate similarity in the Goldilocks zone, provide a reliable bias-variance trade-off for comparative visual reasoning in VLMs, and that the non-monotonic relationship between reference similarity and localisation accuracy holds across the tested models and data.

C3one line summary

WALDO improves zero-shot anomaly localization in medical imaging by selecting reference distributions via entropy-weighted Sliced Wasserstein distances and Goldilocks zone sampling, yielding a 19% relative gain on brain MRI benchmarks.

Receipt and verification

First computed	2026-06-23T02:12:49.712750Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

3c1127db5d6ecbbc8a941f0345ca35169c8cb3bfd5f359c169aec439d9573369

Aliases

arxiv: 2605.05161 · arxiv_version: 2605.05161v2 · doi: 10.48550/arxiv.2605.05161 · pith_short_12: HQISPW25N3F3 · pith_short_16: HQISPW25N3F3ZCUU · pith_short_8: HQISPW25

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/HQISPW25N3F3ZCUUD4BULSRVC2 \
  | 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: 3c1127db5d6ecbbc8a941f0345ca35169c8cb3bfd5f359c169aec439d9573369

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "d2fbaab073d37a9a86a573783ef8087d937173b80d66454c7b1a6e4c9af1e460",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-06T17:32:34Z",
    "title_canon_sha256": "26e9a12eb9cc83771084c519162e381b037bcbb7cad4afeeeb6da2a099555a84"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.05161",
    "kind": "arxiv",
    "version": 2
  }
}