Pith Number

pith:23UZLL5X

pith:2026:23UZLL5XCQAEODQ5GPICBNXWFH

not attested not anchored not stored refs resolved

To Trust or Not to Trust: Authors' Response to AI-based Reviews

C\'esar Leblanc, Lukas Picek

Authors at CS venues found AI-based reviews useful enough to use in revisions, though they trusted them less than human reviews.

arxiv:2605.16623 v1 · 2026-05-15 · cs.CY · cs.AI

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Add to your LaTeX paper

\usepackage{pith}
\pithnumber{23UZLL5XCQAEODQ5GPICBNXWFH}

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

Most respondents (83.9%) considered the AI-based review useful, 80.4% reported that it identified issues not mentioned by human reviewers, and 82.1% reported using at least some AI feedback in their camera-ready version.

C2weakest assumption

The 56 responses from a self-selected subset of authors at two specific CS venues are representative enough to support general statements about author perceptions of AI reviews; the paper relies on this without reporting response rate or testing for non-response bias.

C3one line summary

Survey of 56 authors at two CS venues finds 84% viewed AI reviews as useful and 82% used some feedback in revisions, yet trusted them less than human reviews and preferred supervised deployment.

References

51 extracted · 51 resolved · 0 Pith anchors

[1] https://aaai 2025

[2] Proceedings of the National Academy of Sciences 122(5):e2401232121 2025

[3] Frontiers in Artificial Intelligence 8:1622292 2025

[4] Bhavsar D, Duffy L, Jo H, et al (2025) Policies on artificial intelligence chatbots among academicpublishers:across-sectionalaudit.ResearchIntegrityandPeerReview10(1):1 2025

[5] Qualitative research in psychology 3(2):77–101 2006

Receipt and verification

First computed	2026-05-20T00:02:33.007113Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d6e995afb71400470e1d33d020b6f629fd0e36fa775617186de07bd29a5c87f3

Aliases

arxiv: 2605.16623 · arxiv_version: 2605.16623v1 · doi: 10.48550/arxiv.2605.16623 · pith_short_12: 23UZLL5XCQAE · pith_short_16: 23UZLL5XCQAEODQ5 · pith_short_8: 23UZLL5X

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/23UZLL5XCQAEODQ5GPICBNXWFH \
  | 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: d6e995afb71400470e1d33d020b6f629fd0e36fa775617186de07bd29a5c87f3

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a52be7e8a3decf144916de20bcd76b4027074922e61c372e88a7e06a91fd7842",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CY",
    "submitted_at": "2026-05-15T20:43:55Z",
    "title_canon_sha256": "8496db0c1dcf350e9a9c0f1ef153347feee1985f1b8676e5b6631eb03c66a773"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16623",
    "kind": "arxiv",
    "version": 1
  }
}