Academic Humanizer

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.

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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).

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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 past ~30 words, or with 3+ subordinate clauses. Before: Existing methods, though promising, are largely empirical, with unclear principles underpinning their behavior, which limits their reliability and further progress. After: Existing methods stay empirical. Their principles are unclear, which limits reliability and progress.

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Layer 3: Preserve these (do NOT over-correct)

A general humanizer flattens legitimate scholarly constructs. Keep them.

• Evidence-tied hedging is correct and required. Keep "suggests", "is consistent with", "we hypothesize that", "may indicate", "appears to" when the claim is genuinely uncertain. Wrong fix: turning "the results suggest X" into "the results prove X": this manufactures over-claiming. Keep the calibrated verb.
• Passive voice is fine when the actor is irrelevant: "Samples were normalized to total protein."
• First-person plural "we" is standard; do not rewrite to avoid it.
• Semicolons and an occasional triple are fine in moderation. Em-dashes are the exception: remove them entirely (Layer 1), recasting with commas, colons, parentheses, or separate sentences.
• Formal definitions, named methods/metrics, technical terms, equations, and symbols stay verbatim.
• Never invent, drop, or alter a number, equation, or citation. Same content; preserve every cite key.

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Layer 4: Claim-evidence discipline

For every empirical claim, check (a) is it backed by a number, figure, table, or citation in the text, and (b) does the verb match the strength of that evidence?

• Unbacked claim -> add the evidence pointer or soften. Before: Our method is more robust. After: Our method's accuracy drops by 2 points under distribution shift, versus 11 points for the baseline (Figure 3).
• Verb stronger than evidence -> downgrade. Before: This demonstrates that our method is universally superior. After: On these three datasets, our method matches or exceeds the strongest baseline (Table 2).
• Vague magnitude -> a number or RANGE, attributed. Before: a large improvement. After: a 2--6% improvement in balanced accuracy over the strongest baseline. Prefer ranges (e.g., "2--6%") over single averaged values unless the averaging method is stated, and attribute each number to its method, metric, and baseline. When comparing, lead with the comparison against the strongest competitor, not the trivial baseline.

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