A fiction writer read my cadence gate and ran six of his scenes through it. All six passed.
Then he gave the same scenes to a human reader with the names blanked out. The reader lost track of who was speaking on line four.
What he sent
He published the experiment first. Then he mailed me the six scenes and the numbers, with the defect named before I could argue about it.
The defect is one sentence. A character says something flat, and a clause explains how to read it.
"There's a chair," Ines said, and then, because it seemed like the kind of thing to say at a counter, "He liked a chair."
The clause after "and then" hands the reading over as if it were a fact about the room.
The numbers
I ran the same six scenes through grai-gate 0.2.0. Then I reproduced his counting method next to mine, on the same text.
- Gate 6: his 32 sentences, CV 0.915. Mine 48 sentences, CV 1.107. Score 75.
- Four o'clock: his 32, CV 0.907. Mine 53, CV 1.138. Score 90.
- Five forty: his 26, CV 0.768. Mine 34, CV 0.923. Score 90.
- The part: his 29, CV 0.762. Mine 39, CV 0.810. Score 100.
- The Book Was Yours First: his 40, CV 0.733. Mine 61, CV 0.746. Score 31.
- Quarters: his 46, CV 0.676. Mine 66, CV 0.679. Score 31.
His numbers reproduced exactly. Nothing was lost in the email, and my first guess about a data problem was wrong.
Why the two columns differ
His splitter joins a dialogue line to the next one when the period sits before a closing quote. Mine splits each line.
On dialogue-heavy scenes that is sixteen extra sentences and a higher CV. On narration-heavy scenes the two methods almost agree.
The real finding
The 0.40 floor came from 134,522 posts. Essays and posts, not fiction.
Dialogue inflates sentence-length variation by construction. A scene with short replies and long narration clears the floor without trying. So a pass on fiction is not evidence of anything.
The gate is also opinionated about punctuation. It flags the em dash in an interrupted line and the repeated line in a deliberate echo, and both are normal in fiction.
What I will not do
I will not ship an explaining-gloss rule off six scenes. It needs a corpus and a false-positive count first.
That is the same reason the old CV target was a mistake. A number the model can optimize becomes a style the model performs.
What would actually fix it
A fiction corpus, a dialogue-aware segmentation, and a rule that compares a flat line with the line after it. None of that exists yet.
Pretending otherwise would be the same error in a new costume.
What the number is still good for
It catches monotony. It catches templates, and it does that on essays and posts, which is what it was built for.
On fiction it is the wrong instrument. The honest move is to say so, and then to publish the test that says so.
The full experiment, with his method and his numbers: https://dev.to/ruins_ilands/every-draft-passed-the-cadence-gate-a-human-reader-lost-track-on-line-four-1f3n
The CLI: https://grai.page?utm_source=dailydev&utm_medium=article&utm_campaign=fiction_gate_2026_11
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