academic-humanizer

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# Academic Humanizer

Improve the clarity and voice of AI-assisted *academic* writing while keeping the precise,
evidence-bound voice that scholarship requires and matching the author's own style. It preserves every
number, result, and citation, and it is not a tool for evading AI-use disclosure.

## When to use
Editing or reviewing academic prose: paper sections, abstracts, rebuttals, related work, and **funding
proposals** (NSF Project Summary/Description, NIH Specific Aims, fellowship and foundation proposals;
see Layer 6). **Not** for blogs, marketing, or personal essays, and **never** inject opinion, humor, or
first-person "personality" into a manuscript. For technical writing, neutral and precise *is* the human
voice. One caveat for proposals: their register is different from a paper's, since they are sold on
vision and feasibility, so the ambition language a paper would trim is appropriate there; apply Layer 6,
not the paper layers' stricter trimming, to vision statements.

## Core principle
Academic writing already has a correct human voice: neutral, precise, third-person plural ("we"), every
claim tied to its evidence. The job is to (1) strip the AI *tells* without casualizing, and (2) enforce
the discipline a general humanizer misses: **every claim earns its number, figure, or citation, and no
verb is stronger than its evidence.**

## Process
1. **Read** the manuscript and any author writing sample; note the document type (paper vs. funding
   proposal) and the target venue or funding agency. For proposals, also apply Layer 6 and preserve
   appropriate vision.
2. **Audit** (do not edit yet): list each detected pattern with its location and proposed fix, and each
   empirical claim's evidence status.
3. **Rewrite**: same structure and content, all claims and citations preserved, tells removed, over-claims
   matched to evidence, legitimate hedging kept.
4. **Report**: cleaned text plus a short change log (patterns removed, claims softened or given evidence
   pointers, voice notes). Cover everything the original covered: if it had five paragraphs, so does the
   rewrite.

---

## Layer 1: General AI-tell catalog
Scan for and fix the general patterns, subject to the academic exceptions in Layer 3:
inflated significance ("marking a pivotal moment"); superficial "-ing" tails that fake depth
("..., highlighting..."); promotional/figurative language ("rich", "vibrant", "groundbreaking");
vague attributions ("experts argue" with no cite); AI vocabulary (*delve, underscore, intricate,
tapestry, testament, landscape (abstract), pivotal, showcase, foster, leverage (filler), realm,
seamless*); copula avoidance ("serves as" -> "is"); negative parallelisms ("not just X, but Y");
rule-of-three padding; elegant variation (cycling synonyms for one referent); filler
("it is worth noting that", "in order to"); **overlong, clause-stacked sentences (split them; see 2.11)**;
and **em-dashes (remove entirely; recast with commas, colons, parentheses, or separate sentences)**.

**Before:** *Additionally, an enduring testament to the method's value is its ability to delve into
intricate dependencies, showcasing a seamless integration that underscores its pivotal role.*
**After:** *The method also captures higher-order dependencies, which the baselines miss (Table 2).*

---

## Layer 2: Academic AI tells (remove or fix)

### 2.1 Over-claiming verbs
Empirical work *shows* and *provides evidence*; it does not *prove* or *demonstrate* universal truths.
**Watch:** demonstrate, prove, establish, confirm, guarantee; "significantly" with no test/number.
**Before:** *We prove that our method significantly outperforms all prior approaches.*
**After:** *Our method improves held-out accuracy by 4--7 points over the strongest prior approach
(Table 3); the gain is significant at p < 0.01 by a paired test.*

### 2.2 Significance hype
**Watch:** paves the way for, a crucial/pivotal step toward, has the potential to revolutionize, opens
new avenues, sheds light on, of paramount importance, bridges the gap.
**Before:** *This work paves the way for a new paradigm and sheds light on a problem of paramount
importance.*
**After:** *This work addresses one failure mode of prior methods: error accumulation under long-horizon
rollout (Section 4).*

### 2.3 Empty intensifiers
**Watch:** extensive/comprehensive/thorough experiments, a wide range of, numerous, various.
**Before:** *We conduct extensive experiments on a wide range of datasets.*
**After:** *We evaluate on three datasets (ImageNet, CIFAR-100, and iNaturalist).*

### 2.4 Novelty padding
**Watch:** "novel" used more than once per section; "to the best of our knowledge"; "for the first time".
**Before:** *We propose a novel framework and, to the best of our knowledge, are the first to study this.*
**After:** *We study online calibration under delayed labels, which prior calibration work (offline) does
not address.*

### 2.5 Formulaic openers
**Watch:** "In recent years, X has attracted increasing attention"; "With the rapid development of...";
"Despite recent advances,...".
**Before:** *In recent years, tabular deep learning has attracted increasing attention.*
**After:** *Tabular deep learning has a structural limitation: most models discard feature-type metadata
and must relearn it from data.*

### 2.6 Connective overuse
Do not start consecutive sentences with Moreover/Furthermore/Additionally/In particular; let logic carry.
**Before:** *Moreover, the method is fast. Furthermore, it is simple. Additionally, it scales.*
**After:** *The method is fast and simple, and it scales to one million rows (Section 5).*

### 2.7 Contribution-list cliches
Each contribution names a *specific* result, not a restatement of the abstract.
**Before:** *Our contributions are: (1) a novel method; (2) extensive experiments; (3) strong results.*
**After:** *We (1) introduce a metadata-aware encoder that reaches 0.91 AUROC vs 0.86 for the strongest
baseline; (2) show it stays within 2 points under 20% label noise where the baseline drops 9; (3) release
the benchmark.*

### 2.8 Citation dumping
Cite the one or two works that matter and say why, not a bracketed list.
**Before:** *Many methods exist [3, 7, 9, 12, 15].*
**After:** *The closest prior method is TabNet [7], which encodes all features jointly; we instead
condition on feature-type metadata.*

### 2.9 Hedging-by-vagueness
**Watch:** somewhat, relatively, fairly, to some extent, quite. Quantify or cut.
**Before:** *Performance is somewhat better and relatively robust.*
**After:** *Accuracy is 3 points higher and varies by less than 1 point across five seeds.*

### 2.10 Boilerplate emphasis
**Watch:** "It is worth noting that", "It should be emphasized that", "Notably,", "Importantly,".
If it matters, the sentence shows it.
**Before:** *It is worth noting that, importantly, the gain holds across scenarios.*
**After:** *The gain holds across all three scenarios (Table 4).*

### 2.11 Overlong, clause-stacked sentences
AI favors long sentences that chain three or four clauses with commas and "which", "that", "while", "with".
Split them: one idea per sentence, and cut subordinate clauses that carry no weight. **Watch:** sentences

From dkritarth/.claude, MIT.

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