Pith Number

pith:SNWUXK5X

pith:2026:SNWUXK5XLHWKECHTP6XQ7RCCE7

not attested not anchored not stored refs resolved

ARA: Agentic Reproducibility Assessment For Scalable Support Of Scientific Peer-Review

Anastasios Kouvelas, Andres L. Marin, Fan Wu, Georgios Fontaras, Kevin Riehl, Michail A. Makridis, Nikofors Zacharof, Patrick Langer, Robert Jakob

ARA extracts directed workflow graphs from papers to evaluate reproducibility at scale.

arxiv:2605.02651 v2 · 2026-05-04 · cs.DL · cs.LG

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\usepackage{pith}
\pithnumber{SNWUXK5XLHWKECHTP6XQ7RCCE7}

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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

Experiments on 213 ReScience C articles demonstrate ARA's generalizability and consistent workflow reconstruction and assessment across LLMs, model temperatures, and scientific domains. ARA achieves ~61% accuracy on three benchmarks, and the highest accuracy reported on ReproBench (60.71% vs. 36.84%) and GoldStandardDB (61.68% vs. 43.56%).

C2weakest assumption

That the directed workflow graphs extracted by the agentic system accurately capture the paper's experimental dependencies, data flows, and result-generating procedures in a way that correlates with actual human-validated reproducibility.

C3one line summary

ARA uses LLMs to build workflow graphs linking sources, methods, and outputs in papers, then scores reproducibility, reaching ~61% accuracy on 213 ReScience C articles and outperforming priors on ReproBench and GoldStandardDB.

References

62 extracted · 62 resolved · 2 Pith anchors

[1] Publish or perish 2000 · doi:10.2307/1290335

[2] Science in an exponential world 2006 · doi:10.1038/440413a

[3] Distinguishing fact from fiction: A benchmark dataset for identifying machine-generated scientific papers in the llm era 2023 · doi:10.18653/v1/2023.trustnlp-1.17

[4] Have ai-generated texts from llm infiltrated the realm of sci- entific writing? a large-scale analysis of preprint platforms 2024 · doi:10.1101/2024.03.25.586710

[5] Is LLM a reliable reviewer? a comprehensive evaluation of LLM on automatic paper reviewing tasks, 2024

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:40.405373Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

936d4babb759eca208f37faf0fc44227c8fe885a1b37182d1779789c266bff9b

Aliases

arxiv: 2605.02651 · arxiv_version: 2605.02651v2 · doi: 10.48550/arxiv.2605.02651 · pith_short_12: SNWUXK5XLHWK · pith_short_16: SNWUXK5XLHWKECHT · pith_short_8: SNWUXK5X

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/SNWUXK5XLHWKECHTP6XQ7RCCE7 \
  | 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: 936d4babb759eca208f37faf0fc44227c8fe885a1b37182d1779789c266bff9b

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c9d6d8e7d866c0884e17b18808aaac4ba6367dadc6f3e6da904104a9e3f316c1",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.DL",
    "submitted_at": "2026-05-04T14:34:36Z",
    "title_canon_sha256": "1530913cd0ae2ca7c7b7034cd6d1daeb1e2835f61cbfff88c2190e62d7dafae0"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.02651",
    "kind": "arxiv",
    "version": 2
  }
}