Pith Number

pith:M25E5YKU

pith:2026:M25E5YKUYOURCUVA7PZBTZEBAH

not attested not anchored not stored refs pending

A Comparative Study in Surgical AI: Potential and Limitations of Data, Compute, and Scaling

Daniel A. Donoho, Eric Fithian, Jack Cook, John Zhu, Kirill Skobelev, Margaux Masson-Forsythe, Neeraj Mainkar, Sandeep Angara, Shauna Otto, X.Y. Han, Yegor Baranovski, Zhuang-Fang Yi

Even large Vision Language Models fail at basic surgical tool detection in neurosurgery, with scaling producing only diminishing gains.

arxiv:2603.27341 v3 · 2026-03-28 · cs.AI · cs.CV · 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{M25E5YKUYOURCUVA7PZBTZEBAH}

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

even with multi-billion parameter models and extensive training, current Vision Language Models fall short in the seemingly simple task of tool detection in neurosurgery. scaling experiments indicating that increasing model size and training time only leads to diminishing improvements in relevant performance metrics.

C2weakest assumption

The chosen tool-detection task and neurosurgery datasets are representative of broader surgical AI challenges, and that the tested models represent the best possible application of 2026-era methods without unstated domain adaptations.

C3one line summary

Current vision-language models underperform on surgical tool detection in neurosurgery, with scaling model size and training time producing only diminishing returns.

Receipt and verification

First computed	2026-05-20T00:04:30.262408Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

66ba4ee154c3a91152a0fbf219e48101e76d6876da3f00eb41b4ff79464c481c

Aliases

arxiv: 2603.27341 · arxiv_version: 2603.27341v3 · doi: 10.48550/arxiv.2603.27341 · pith_short_12: M25E5YKUYOUR · pith_short_16: M25E5YKUYOURCUVA · pith_short_8: M25E5YKU

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/M25E5YKUYOURCUVA7PZBTZEBAH \
  | 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: 66ba4ee154c3a91152a0fbf219e48101e76d6876da3f00eb41b4ff79464c481c

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "344333161085507a4619e14f6df8b6b72740a63bbf36a5589fe6c244a350d7cc",
    "cross_cats_sorted": [
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-03-28T17:18:40Z",
    "title_canon_sha256": "34f3b66d8745edfb1d919332f6f34ae484712b3d8f9958177206f18c49af163a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.27341",
    "kind": "arxiv",
    "version": 3
  }
}