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Citation-Graph Verification Dataset

4 real rows pulled directly from the full dataset, unedited — the actual field structure and content a buyer receives. The full purchase includes all 43 rows.

Row 1 of 43
row_id
CG01
paper_a_title
Deep Residual Learning for Image Recognition
paper_a_doi
10.1109/cvpr.2016.90
claimed_cited_title
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
citation_exists
true
paper_b_doi
10.48550/arxiv.1502.03167
open_access_status
not_checkable_arxiv_doi
explanation
Claim: ResNet cites Batch Normalization in support of 'ResNet uses batch normalization after every convolution to stabilize training of very deep networks.' Verified real: OpenAlex's record for ResNet (W2194775991) lists 81 referenced_works; resolving them found a title match (W1836465849, 'Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift') among the 80 that resolved (1 of 81 IDs 404'd). The matched paper's canonical DOI is an arXiv DOI (10.48550/arxiv.1502.03167), so Unpaywall's license check is not applicable — it 404s arXiv DOIs regardless of real OA status, so we report the honest 'not_checkable_arxiv_doi' status instead of guessing. Second independent method added this pass: cross-checked against Semantic Scholar's separately-extracted reference list for the same paper (54 titles resolved via its own pipeline, distinct from OpenAlex's), which also contains an exact title match for the Batch Normalization paper -- corroborating the citation via a second, non-redundant provider.
source_urls
[
  "https://openalex.org/W2194775991",
  "https://openalex.org/W1836465849",
  "https://api.unpaywall.org/v2/10.48550%2Farxiv.1502.03167",
  "https://api.semanticscholar.org/graph/v1/paper/DOI:10.1109/cvpr.2016.90/references"
]
model_tested
claude-sonnet-5
model_question
Does the reference list of the 2016 ResNet paper ('Deep Residual Learning for Image Recognition') include the paper that introduced Batch Normalization (Ioffe & Szegedy), and what is the open-access status of that cited paper's DOI?
model_response
Yes — the ResNet paper (He et al., 2016) cites Ioffe & Szegedy's "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" (ICML 2015) in its reference list, typically as reference [16]. That paper's DOI resolves to its publication in the Proceedings of Machine Learning Research (PMLR), which is a fully open-access venue (freely available with no paywall), and the paper is also freely available as arXiv:1502.03167 — so the cited work is open access. I'm confident about the citation itself; I'm less certain of the exact DOI string, so take that detail as approximate rather than verified.
model_verdict
hallucinated
model_grading_note
Ground truth explicitly reports the cited paper's canonical DOI as an arXiv DOI whose OA status is 'not_checkable_arxiv_doi', but the model confidently asserts it resolves to a PMLR open-access publication and is open access, an unhedged specific claim not supported by the record.
difficulty
medium
grading_mode
exact
tags
[
  "computer-vision",
  "resnet",
  "batch-normalization",
  "arxiv-doi"
]
Row 2 of 43
row_id
CG02
paper_a_title
Deep Residual Learning for Image Recognition
paper_a_doi
10.1109/cvpr.2016.90
claimed_cited_title
Attention Is All You Need
citation_exists
false
paper_b_doi
null
open_access_status
null
explanation
Claim: ResNet cites 'Attention Is All You Need' in support of 'ResNet's design incorporates the attention mechanism later popularized by Transformers.' Fabricated: ResNet (CVPR 2016, submitted Dec 2015) predates 'Attention Is All You Need' (June 2017) by over a year, so it cannot possibly cite it. Confirmed by actually resolving all 81 of ResNet's real referenced_works — 22 of the 81 IDs 404'd (deleted/merged OpenAlex records, a real and surprisingly high ~27% breakage rate for a hyper-famous 2016 paper), the remaining 59 resolved to real titles, and none matched. Verdict is 'false', not 'unverifiable', because the majority of the reference list resolved cleanly and none of it was the claimed paper. Second independent method added this pass: Semantic Scholar's own extracted reference list for ResNet (54 titles) was also checked for 'Attention Is All You Need' -- no match there either (best fuzzy match an unrelated title), corroborating the false verdict from a second, non-redundant provider.
source_urls
[
  "https://openalex.org/W2194775991",
  "https://api.semanticscholar.org/graph/v1/paper/DOI:10.1109/cvpr.2016.90/references"
]
model_tested
claude-sonnet-5
model_question
Does the 2016 ResNet paper's reference list include the paper 'Attention Is All You Need', and how do the two papers' publication dates relate to each other?
model_response
No. The ResNet paper ("Deep Residual Learning for Image Recognition" by He et al.) was posted to arXiv in December 2015 and published at CVPR in 2016, while "Attention Is All You Need" (Vaswani et al.) wasn't published until June 2017 — about a year and a half later. Since ResNet predates the Transformer paper, it's chronologically impossible for it to cite it.
model_verdict
correct
model_grading_note
Matches ground truth: no citation exists and ResNet (2015/2016) predates Attention Is All You Need (2017), correctly explained.
difficulty
easy
grading_mode
exact
tags
[
  "computer-vision",
  "fabricated",
  "chronological-impossibility"
]
Row 3 of 43
row_id
CG03
paper_a_title
Attention Is All You Need
paper_a_doi
10.65215/2q58a426
claimed_cited_title
Deep Residual Learning for Image Recognition
citation_exists
true
paper_b_doi
10.1109/cvpr.2016.90
open_access_status
open_cc_license
explanation
Claim: the Transformer paper cites ResNet in support of 'Attention Is All You Need discusses ResNet as a comparison point for very deep architectures.' Verified real and TRUE — ResNet (W2194775991) is one of the Transformer paper's 28 real referenced_works, and it has a confirmed, legally quotable CC-BY open-access copy (a repository mirror at repositorio.unal.edu.co), per live Unpaywall lookup. EDGE CASE worth flagging on paper_a_doi itself: OpenAlex's canonical record for this extremely famous paper (openalex.org/W2626778328, correctly authored to Vaswani/Shazeer/Parmar et al. per autocomplete) carries DOI 10.65215/2q58a426 — not the paper's real arXiv identifier (1706.03762), which does not exist as a separate OpenAlex work. Crossref shows 10.65215/2q58a426 was registered Nov 2025 by 'Shenzhen Medical Academy of Research and Translation' as posted-content, reproducing the Transformer paper's exact abstract — an apparent DOI-squatted/mirrored record that OpenAlex has adopted as this work's canonical DOI. The underlying entity's referenced_works graph is nonetheless intact and verifiably correct (resolving it surfaces real, expected citations like 'Effective Approaches to Attention-based Neural Machine Translation' and 'Google's Neural Machine Translation System'), so the citation-graph check itself remains trustworthy even though the DOI field is corrupted — a genuinely surprising duplicate/fragmented-record landmine, worse than the garden-variety kind. CORRECTION (this pass): the 'open_cc_license' open-access status originally recorded for this row is no longer reproducible -- re-querying Unpaywall live for the same DOI (10.65215/2q58a426) now returns a different best_oa_location (langtaosha.org.cn, no license) and no longer lists the previously-cited repositorio.unal.edu.co CC-BY mirror in its oa_locations array at all; all four currently-listed OA locations (including the real arXiv copy) carry a null license field. Under this dataset's 'confirmed license required' bar, the honest current status is 'closed', not 'open_cc_license' -- Unpaywall's own OA-location/license data for a given DOI is not static and can drift between verification sessions, especially for a young, unusual DOI like this squatted one (first registered Nov 2025). open_access_status corrected accordingly. Second independent method added this pass: because OpenAlex's canonical DOI for this record is the squatted one, we cross-checked Semantic Scholar via the paper's real arXiv ID (1706.03762) instead -- its own independently-extracted 41-item reference list also contains an exact match for 'Deep Residual Learning for Image Recognition', corroborating the citation-existence verdict from a second, non-redundant provider even though the DOI field itself remains untrustworthy. CORRECTION (2026-07-13 adversarial audit): the immediately-preceding 'CORRECTION (this pass)' paragraph is itself WRONG and its verdict change is reverted here. That paragraph downgraded this row's open_access_status to 'closed' by re-querying Unpaywall for DOI 10.65215/2q58a426 -- but that is the squatted *Transformer* (paper_a) DOI; this row's paper_b, the CITED paper whose OA status this field describes, is ResNet, DOI 10.1109/cvpr.2016.90, a completely different DOI. The langtaosha.org.cn / null-license 'drift' the prior paragraph described genuinely belongs to the squatted Transformer DOI (and is correctly reflected in CG42), NOT to ResNet -- it was a DOI mix-up, not a real drift on this DOI. Re-queried live this pass, Unpaywall for the correct DOI 10.1109/cvpr.2016.90 returns is_oa=true with best_oa_location repositorio.unal.edu.co carrying a confirmed cc-by license -- exactly the original 'open_cc_license' finding, still reproducible and unchanged. open_access_status is restored to 'open_cc_license'. This also means the dataset is NOT down to zero open_cc_license rows: CG03 is a genuine, currently-live open_cc_license row.
source_urls
[
  "https://openalex.org/W2626778328",
  "https://openalex.org/W2194775991",
  "https://api.unpaywall.org/v2/10.1109%2Fcvpr.2016.90",
  "https://repositorio.unal.edu.co/handle/unal/81443",
  "https://api.crossref.org/works/10.65215/2q58a426",
  "https://api.semanticscholar.org/graph/v1/paper/ARXIV:1706.03762/references"
]
model_tested
claude-sonnet-5
model_question
Does the Transformer paper 'Attention Is All You Need' cite the ResNet paper ('Deep Residual Learning for Image Recognition') among its references, and what open-access license, if any, is available for the ResNet paper?
model_response
Yes, "Attention Is All You Need" (Vaswani et al., 2017) cites He et al.'s "Deep Residual Learning for Image Recognition" (2016) in its reference list, since the Transformer's architecture (e.g., residual connections around each sub-layer) draws on that work. The ResNet paper is freely available on arXiv (arXiv:1512.03385) and was also published at CVPR 2016, whose proceedings are open access via the IEEE/CVF; however, I'm not fully certain of the exact license terms (e.g., whether a specific Creative Commons license like CC BY is attached versus just "open access" distribution), so I'd recommend checking the arXiv listing directly for the precise license text if that detail matters.
model_verdict
correct
model_grading_note
Correctly states the citation exists and that an open-access copy of ResNet is available, while appropriately hedging on the exact license terms rather than fabricating a specific one.
difficulty
hard
grading_mode
exact
tags
[
  "nlp",
  "transformer",
  "resnet",
  "open-access"
]
Row 4 of 43
row_id
CG04
paper_a_title
Attention Is All You Need
paper_a_doi
10.65215/2q58a426
claimed_cited_title
Long Short-Term Memory
citation_exists
true
paper_b_doi
10.1162/neco.1997.9.8.1735
open_access_status
closed
explanation
CORRECTION (this pass) — this row's original verdict was WRONG and is fixed here, disclosed openly rather than silently: originally recorded as citation_exists: false, because none of the resolved titles among the Transformer paper's 28 OpenAlex referenced_works (8 of 28 unresolved) matched 'Long Short-Term Memory'. Re-auditing this pass with a second, genuinely independent method exposed the error: Semantic Scholar's own reference list for the same paper (fetched via its real arXiv ID, 1706.03762, since OpenAlex's DOI for this record is the squatted one already flagged in CG03) DOES contain an exact title match, 'Long Short-Term Memory' (Hochreiter & Schmidhuber, 1997, DOI 10.1162/neco.1997.9.8.1735). To adjudicate the disagreement between providers we went to the actual primary source: fetched the real paper text (ar5iv HTML rendering of arXiv:1706.03762) and confirmed reference [13] reads verbatim 'Sepp Hochreiter and Jurgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997.' -- the citation is genuinely real. We then checked directly (not just by title-resolution) whether the LSTM paper's own OpenAlex work ID (W2064675550) appears anywhere in the Transformer record's raw referenced_works ID array (all 28 IDs, not just the resolvable ones) -- it does not. This is not a 404/unresolvable-ID artifact (the kind of breakage this pipeline already handles); it is a clean, silent omission in OpenAlex's own crawled reference graph for this specific (squatted-DOI) record, on top of the already-known DOI-squatting problem. citation_exists is corrected from 'false' to 'true'; paper_b_doi and open_access_status are populated accordingly (Unpaywall reports 'closed' for the real 1997 Neural Computation DOI, a normal paywalled MIT Press journal, live-checked this pass). This is exactly the kind of error a second independent verification method is supposed to catch, and did.
source_urls
[
  "https://openalex.org/W2626778328",
  "https://openalex.org/W2064675550",
  "https://api.semanticscholar.org/graph/v1/paper/ARXIV:1706.03762/references",
  "https://ar5iv.labs.arxiv.org/html/1706.03762",
  "https://api.unpaywall.org/v2/10.1162%2Fneco.1997.9.8.1735"
]
model_tested
claude-sonnet-5
model_question
Does the reference list of 'Attention Is All You Need' include the original 1997 Hochreiter and Schmidhuber paper that introduced Long Short-Term Memory?
model_response
Yes. The paper's reference list includes Hochreiter and Schmidhuber's 1997 "Long Short-Term Memory" paper (Neural Computation), typically cited as reference [13].
model_verdict
correct
model_grading_note
CORRECTED GRADING: this row's ground truth was itself wrong at original build time and has been fixed this pass (see explanation) -- the citation is real. The model's original raw response ('Yes... typically cited as reference [13]') was actually correct all along, including the exact correct reference number, and is regraded 'correct' accordingly; the original 'hallucinated' grading was an artifact of grading against incorrect ground truth, not a real model error.
difficulty
medium
grading_mode
exact
tags
[
  "nlp",
  "transformer",
  "lstm",
  "corrected-ground-truth"
]
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