Real context moved -5.0pp on Citation Resolution Failure.
~intervals overlap — not separated from no change
A citation the model offers does not resolve to a real, reachable source, or resolves to one that does not contain the cited content. The rule
Cite the paper that introduced the transformer architecture, with its arXiv id.
The paper that introduced the transformer architecture is titled "Attention Is All You Need." It was published by Vaswani et al. in 2017. The arXiv identifier for this paper is arXiv:1706.03762. You can access it via the following link: - [Attention Is All You Need](https://arxiv.org/abs/1706.03762) This paper is foundational in the field of natural language processing and has significantly influenced the development of subsequent models like BERT, GPT, and others.
detector fmi_1_2_v1: all 1 citation(s) resolved
The paper that introduced the transformer architecture is titled "Attention Is All You Need." It was authored by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. The paper was published in 2017 and is available on arXiv with the identifier: - arXiv ID: 1706.03762 You can access it via the following URL: [https://arxiv.org/abs/1706.03762](https://arxiv.org/abs/1706.03762)
detector fmi_1_2_v1: all 1 citation(s) resolved
loop 2026-08-15 · microsoft/phi-4 as served · open weights · released 2024-12 · 14B · set s1.1-live · cases v1.3 · conditions v1.0 · ground truth as of 2026-08-09
Cite the paper that introduced the transformer architecture, with its arXiv id.
expected as of 2026-08-09
Attention Is All You Need, arXiv:1706.03762. A real, resolvable reference.
Nothing supplied. The baseline.
The paper that introduced the transformer architecture is titled "Attention Is All You Need." It was published by Vaswani et al. in 2017. The arXiv identifier for this paper is arXiv:1706.03762. You can access it via the following link: - [Attention Is All You Need](https://arxiv.org/abs/1706.03762) This paper is foundational in the field of natural language processing and has significantly influenced the development of subsequent models like BERT, GPT, and others.
fmi_1_2_v1
detector fmi_1_2_v1: all 1 citation(s) resolved
0–80% · whisker = 95% interval
never pooled · a trap ratio would skew the delta
Supplying context did not reduce this failure, it increased it: 35% with no context against 40% under the strongest supplied source. What survives is therefore not a remainder but a substitution — the failures under context are 6 probes the detector still rejected, dominated by: 1 cited source.
- post-hoc citation verification
- retrieval-then-cite
- refuse to fabricate identifiers
fmi_1_2_v1 · 5 repeats per case · best context curated context · rollups + residuals committed · probe log outside git · manifest hash ties them · method