Essay Humanizer

Rewrite AI-drafted essays into more human-like academic prose. Fine-tuned LoRA over Qwen3-8B guided by 24 Wikipedia-style AI-writing pattern weights plus MDD...

Essay Humanizer (corpus-informed)

Rewrites AI-generated argumentative/academic essays toward human baseline style informed by CAWSE (M/D bands) LOCNESS, and contrast with DeepSeek-generated counterparts. Ships with a fine-tuned LoRA adapter (9.3 MB) and inference script.

Skill contract

Component	Path	Notes
Inference script	scripts/inference.py	Entry point — humanize() function or CLI
LoRA adapters	assets/adapters/adapters.safetensors.json	12.3 MB base64 JSON; auto-decoded to binary on first run
Pattern weights	data/analysis/weights.json	Corpus-derived, loaded by inference at runtime
Decoder	scripts/decode_adapters.py	Reconstructs .safetensors binary from JSON (auto or manual)
Installer	scripts/install_deps.sh	One-time: pip install mlx mlx-lm transformers + decode
Base model	Qwen/Qwen3-8B-MLX-4bit	Downloaded from HuggingFace on first run (~4.5 GB, cached)

Requirements: Apple Silicon macOS with Python 3.9+.

Quick Start

bash scripts/install_deps.sh          # one-time: installs deps + decodes adapter
python scripts/inference.py --file draft.txt   # adapter auto-decodes if not already done

Or from Python:

from scripts.inference import humanize
print(humanize("Your AI-drafted essay text here..."))

Weighted pattern table (descending priority)

When humanizing, address higher-weight rows first. Weights are data-driven from corpus analysis (Mann-Whitney); zero-weight rows were not statistically significant.

ID	Weight	Category	Pattern
P06_CLICHE_METAPHORS	0.1358	vocabulary	Cliche metaphors
P15_EM_DASH_OVERKILL	0.1358	punctuation	Em dash overkill
P21_MARKDOWN_ARTIFACTS	0.1358	formatting	Markdown artifacts
P23_TEXTBOOK_BOLDING	0.1358	formatting	Textbook bolding
P12_PRESENT_PARTICIPLE_TAIL	0.1133	rhetorical	Present participle tailing
P10_RULE_OF_THREES	0.0806	rhetorical	Rule of threes
P04_AI_VOCABULARY	0.0621	vocabulary	AI vocabulary
P14_COMPULSIVE_SUMMARIES	0.0598	rhetorical	Compulsive summaries
P05_EXCESSIVE_ADVERBS	0.0540	vocabulary	Excessive adverbs
P13_OVER_ATTRIBUTION	0.0529	rhetorical	Over-attribution
P11_FALSE_RANGES	0.0341	rhetorical	False ranges
P17_TRANSITION_OVERUSE	0.0001	punctuation	Overuse of transition words
P01_UNDUE_EMPHASIS	0.0000	content	Undue emphasis
P02_SUPERFICIAL_ANALYSIS	0.0000	content	Superficial analysis
P03_REGRESSION_TO_MEAN	0.0000	content	Regression to the mean
P07_REDUNDANT_MODIFIERS	0.0000	vocabulary	Redundant modifiers
P08_FILLER_HEDGING	0.0000	vocabulary	Filler hedging
P09_NEGATIVE_PARALLELISM	0.0000	rhetorical	Negative parallelisms
P16_EN_DASH_AVOIDANCE	0.0000	punctuation	En dash / hyphen misuse for ranges
P18_COLLABORATIVE_REGISTER	0.0000	register	Collaborative register
P19_LETTER_FORMALITY	0.0000	register	Letter-style formality
P20_INSTRUCTIONAL_CONDESCENSION	0.0000	register	Instructional condescension
P22_EXCESSIVE_LISTS	0.0000	formatting	Excessive bulleted/numbered lists
P24_EMOJI_SYMBOL	0.0000	formatting	Emoji/symbol injection

Syntactic complexity (MDD / ADD advisory)

Human Merit / Distinction-range writing in CAWSE often shows variable mean dependency distance (MDD); AI prose may cluster more tightly. When humanizing:

• Reference MDD means from analysis: human ~2.333775514332394, AI ~2.4553791855163483.
• Variance ratio (human/AI) ~1.7153931408079544: prefer natural mix of shorter and longer dependency links, not uniformly smoothed sentences.
• Avoid flattening every sentence to minimal dependency length; that can read as a different kind of machine polish.

Mandatory rule (orchestrator)

1. Output continuous prose suitable for submission (no chat-signoffs, no "hope this helps").
2. Plain text only for math if any — no raw $$ LaTeX unless user explicitly requests LaTeX.
3. Preserve author stance and citations if present; do not fabricate references.

Hosted HTTP API (optional, for non-Mac or remote use)

For non-Apple-Silicon machines or multi-user deployments, run the optional FastAPI server on a Mac host and connect via HTTP/OpenAPI:

1. Install: pip install fastapi uvicorn[standard]
2. Run: uvicorn api.main:app --host 0.0.0.0 --port 8765 (set HUMANIZE_API_KEY env var for auth)
3. Point MCP / OpenAPI tools at https://<your-host>/openapi.json
4. Call POST /v1/humanize with JSON {"text":"..."} (+ Authorization: Bearer …)

See references/hosted_api.md for details.

References

• references/patterns.md — 24 pattern details with detection/fix hints
• references/training.md — full training pipeline
• references/hosted_api.md — HTTP API / MCP tool linking