Arjuna Badger Press

The de-LLM loop — hunting the machine tells

Part of the technology exposé. The closed editorial loop that drives prose toward "no obvious craft issues or LLM tells" — and the duplicate-tell eliminators at its core.

A model's prose has a fingerprint: the spaced em-dash, "almost smiled," the thesis stated on a loop, the flat even rhythm where every sentence weighs the same. Readers feel it as "this was written by a machine" without being able to name it. The de-LLM loop's only job is to find those tells and kill them — and to learn each one permanently, so the prose quality ratchets up instead of drifting.

flowchart LR
    R[Cold-read + craft audit<br/>find craft issues / LLM tells] --> X[Re-incorporate into engine<br/>prompts · style guide · tic scanners]
    X --> F[Surgical edit pass<br/>human in the loop]
    F --> S{Tic scanners<br/>+ scorers}
    S -->|tells remain| R
    S -->|only creative changes left| DONE([ship])

Three components chasing each other

A brutal cold-read agent (sentence layer) and a structural craft audit (the layer above —

voice homogenisation, gravitas inflation, over-polished action, reveal/reaction order) find the problems model prose falls into.

Each finding is re-incorporated into the engine — the prompts, the style guide, and a set of

deterministic tic scanners that count the specific machine-tells against falling targets.

A surgical, human-in-the-loop prose pass fixes them. Then the loop runs again.

The duplicate-tell eliminators (deterministic, free)

The "duplicate eliminator" is really a family of scanners, each killing a different layer of repeated machine pattern. They are pure pattern-counters — no model, no cost — and they run on every build:

Scanner	The tell it eliminates
`prose_tics`	the sentence-layer tells — the spaced em-dash, "almost smiled," the stock gesture-beats a model reaches for again and again
`prose_thesis`	the semantic tell — the book's theme stated and re-stated on a loop, as if the reader might miss it
`prose_evenness`	the deepest and subtlest tell — even register, where every sentence carries the same weight and rhythm, so nothing lands. The cold-read names this the hardest to grep; it's made measurable here.
`prose_cadence`	rhythm and sentence-length variance — the music under the prose
`voice_audit`	character-voice homogenisation — when everyone in the cast starts to sound the same

Each one turns a vague "this feels like AI" into a number against a target, so a revision can be judged on whether it actually removed the tell or just moved the words around.

Why the loop matters

The system learns from its own failures. A tell found once becomes a guardrail that catches it forever after. That's the whole trick: the prose doesn't depend on the model getting better — it depends on the scanners getting more complete, which they do every time the cold-read finds something new.

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