Editing Research/Fiction Writing/AI Prose Strengthen (section)

== Autonovel directives relevant to prose quality ==

These are extracted from <code>README.md</code>, <code>ANTI-SLOP.md</code>, <code>ANTI-PATTERNS.md</code>, <code>CRAFT.md</code>, <code>draft_chapter.py</code>, <code>evaluate.py</code>, <code>adversarial_edit.py</code>, and <code>voice_fingerprint.py</code>.

=== Word-level anti-slop findings ===

Autonovel’s <code>ANTI-SLOP.md</code> and <code>evaluate.py</code> flag words and phrases statistically or stylistically associated with unedited LLM output. The repository treats these as revision triggers, not absolute proof of authorship.

Commonly flagged categories:

* '''Grandiose or corporate diction:''' “delve,” “utilize,” “leverage,” “facilitate,” “elucidate,” “embark,” “endeavor,” “multifaceted,” “tapestry,” “paradigm,” “synergy,” “holistic,” “myriad,” “plethora.”
* '''Suspicious-in-clusters adjectives/verbs:''' “robust,” “comprehensive,” “seamless,” “cutting-edge,” “innovative,” “streamline,” “empower,” “foster,” “enhance,” “elevate,” “optimize,” “pivotal,” “profound,” “resonate,” “underscore,” “harness,” “cultivate.”
* '''Filler phrases:''' “It’s worth noting,” “It’s important to note,” “Let’s dive into,” “In conclusion,” “To summarize,” “Furthermore,” “Moreover,” “Additionally,” “In today’s fast-paced world,” “At the end of the day,” “When it comes to,” “One might argue.”
* '''Rhetorical crutches:''' especially “not just X, but Y.”

Quality takeaway: replace generic prestige diction with exact nouns, verbs, evidence, and images. If a phrase could fit any topic, it probably adds little.

=== Structural anti-patterns ===

Autonovel’s <code>ANTI-PATTERNS.md</code> argues that many AI tells are structural, not lexical:

* '''Over-explaining:''' the scene already shows fear, grief, or tension, then the narrator explains it.
* '''Triadic listing:''' repeated “X. Y. Z.” patterns or three-item sensory lists.
* '''Negative assertion repetition:''' repeated “He did not…” formulations.
* '''Cataloging by thinking:''' “He thought about X. He thought about Y…” instead of dramatized interiority.
* '''Simile crutch:''' repeated “the way X did Y.”
* '''Section-break crutch:''' using breaks to avoid transitions.
* '''Paragraph-length uniformity:''' middle sections flatten into similar 4–6 sentence paragraphs.
* '''Predictable emotional arcs:''' outline beats arrive too cleanly, with no sideways interruption.
* '''Repetitive chapter endings:''' same structural closing move reused.
* '''Balanced antithesis in dialogue:''' “I’m not saying X. I’m saying Y.”
* '''Dialogue as written prose:''' polished complete sentences, no interruptions, false starts, or wrong words.
* '''Scene-summary imbalance:''' too much narration compressing time instead of dramatized action/dialogue.

Quality takeaway: revise for asymmetry, scene-specific endings, imperfect speech, embodied interiority, and genuine surprise.

=== Fiction-specific “AI tell” patterns ===

<code>CRAFT.md</code> and <code>evaluate.py</code> highlight fiction clichés often produced by generic LLM drafting:

* “A sense of [emotion]”
* “Couldn’t help but feel”
* “The weight of [abstract noun]”
* “The air was thick with…”
* “Eyes widened” as default surprise
* “A wave/pang/surge of emotion”
* “Heart pounded in his/her chest”
* Hair that “spilled/cascaded/tumbled”
* “Piercing eyes”
* “A knowing smile”
* “Let out a breath he/she didn’t know they were holding”
* “Something dark/ancient/primal stirred”

Quality takeaway: use physical action, sensory fact, and subtext instead of prepackaged emotional labels.

=== Autonovel drafting constraints worth reusing ===

From <code>draft_chapter.py</code>:

* Write in a defined POV and tense.
* Follow a voice definition exactly.
* Hit every outline beat, but do not summarize or skip.
* Show sensory detail tied to the point-of-view character.
* Use character-specific speech patterns.
* Ban known slop phrases before drafting.
* Vary sentence length deliberately.
* Use metaphors from the character’s lived experience.
* Trust the reader; do not explain what scenes mean.
* Start in scene, not exposition.
* End on a moment, not a summary.
* Include at least one surprising moment per chapter.
* Keep most of the chapter in-scene rather than summarized.

=== Autonovel evaluation metrics worth reusing ===

From <code>evaluate.py</code> and <code>voice_fingerprint.py</code>:

* banned/slop word hits
* filler phrase hits
* fiction cliché hits
* show-don’t-tell violations
* structural tic counts
* em dash density
* sentence-length coefficient of variation
* transition-opener ratio
* paragraph-length variation
* dialogue ratio
* abstract-noun density
* repeated sentence starters
* simile density
* section-break count
* chapter-level outliers from the manuscript average

These metrics should not be treated as “AI detector evasion.” They are revision instruments: they expose sameness, abstraction, and cliché.

=== Adversarial editing as the strongest revision pattern ===

<code>adversarial_edit.py</code> asks a judge to identify 10–20 exact passages to cut or rewrite and classify them as:

* FAT — adds nothing
* REDUNDANT — restates what was already shown
* OVER-EXPLAIN — explains what the scene demonstrated
* GENERIC — could appear in any story
* TELL — names emotion/state instead of dramatizing it
* STRUCTURAL — disrupts pacing or rhythm

The key research finding: asking “what would you cut?” is more useful than asking for a general quality score. Absolute 1–10 scoring compresses; specific cut lists produce revision plans.