EditLens: Quantifying the Extent of AI Editing in Text
Authors: Katherine Thai
Bradley Emi
Elyas Masrour
Mohit Iyyer
Venue: ICLR 2026 Poster (2026)
Keywords: AI detection
authorship attribution
human-AI collaboration
Primary area: alignment, fairness, safety, privacy, and societal considerations
TL;DR: We develop a model that is able to quantify the extent of AI editing on a continuous spectrum.

Abstract: A significant proportion of queries to large language models ask them to edit user-provided text, rather than generate new text from scratch. While previous work focuses on detecting fully AI-generated text, we demonstrate that AI-edited text is distinguishable from human-written and AI-generated text. First, we propose using lightweight similarity metrics to quantify the magnitude of AI editing present in a text given the original human-written text and validate these metrics with human annotators. Using these similarity metrics as intermediate supervision, we then train EditLens, a regression model that predicts the amount of AI editing present within a text. Our model achieves state-of-the-art performance on both binary (F1=94.7%) and ternary (F1=90.4%) classification tasks in distinguishing human, AI, and mixed writing. Not only do we show that AI-edited text can be detected, but also that the degree of change made by AI to human writing can be detected, which has implications for authorship attribution, education, and policy. Finally, as a case study, we use our model to analyze the effects of AI-edits applied by Grammarly, a popular writing assistance tool. To encourage further research, we commit to publicly releasing our models and dataset.

Published as a conference paper at ICLR 2026
EDITLENS: QUANTIFYING THE EXTENT OF AI EDIT- ING IN TEXT
Katherine Thai'?
Bradley Emi'
Elyas Masrour'
Mohit Iyyer*
Pangram Labs
Uniiersiity of Massachusetts Amherst
Univeesityy of Maryland, College Park
ABSTRACT
A significant proportion of queries to large language models ask them to edit user- provided text, rather than generate new text from scratch. While previous work focuses on detecting fully Al-generated text, we demonstrate that Al-edited text is distinguishable from human-written and Al-generated text. First, we propose us- ing lightweight similarity metrics to quantify the magnitude of Al editing present in a text given the original human-writter text and validate these metrics with hu- man annotators. Using these similarity metrics as intermediate supervision, we then train EDITLENS, regression model that predicts the amount of AI editing present within text. Our model achieves state-of-the-ar art performance on both binary (F1=94.7%) and ternary (F1=90.4%) classification tasks in distinguishing human, AI, and mixed writing. Not only do we show that Al-edited text can be detected, but also that the degree of change made by Al human writing be detected, which has implications for authorship attribution, education, and policy. Finally, as case study, we use our model analyze the effects of AI-edits applied by Grammarly, a popular writing assistance tool. To encourage further research, we commit publicly releasing our dataset and models.
github. ht.hbbb..cc//peggrraaaaba//addd.
INTRODUCTION
Large language models (LLMs) generate text that is difficult to distinguish from human writing, enabling malicious applications such as academic plagiarism and fake review farms, thus motivating the need for accurate AI detection. While existing detectors frame the task as binary classification (fully human vs. fully Al-generated), mainstream LLM usage increasingly involves co-writing, where LLMs are used for editing and brainstorming via services like Grammarly! Sudowrite oor Google Docs' Gemini integration. In fact, a recent OpenAl study of over 1M ChatGPT conversations (Catterjii et al. 2025) shows that "about two- woo-thirds of all Writing messages ask ChatGPT to modify user text (editing, critiquing, translating, etc.) rather than creating new text from scratch." Binary Al detection systems not well-suited to detect such mixed-authorshhpp texts: for example, [Saha] & Feizi (2025) find that binary detectors often flag Al-polished text as Al-generated, limiting their utility in situations where light AI editing is acceptable but fully Al-generated text is not.
In this paper, we develop EDITLENS, the first AI detector that estimates the extent of Al editing in text as a continuous score. Previous work on detecting mixed Al and human text has treated the task as either a boundary detection problem (Kushnareva et al. 2024 Lei et al. 2025). sentence-wiss classification task (Wang et al. 2023), or ternary classification problem between human, AI, and mixed text (Abassy et al. 2204 Wang et al., 2025). However, modern collaborative editing involves layered revisions, suggestions, and refinements that blur traditional notions of authorship, making challenging to definitively attribute specific segments to either human AI authors and rendering boundary detection and sentence-level tasks ill-posed. Although the ternary classification approach does not require assigning direct authorship to discrete segments, it is unable quantify the degree or the magnitude AI editing: Was the text lightly edited for spelling and grammar, or completely
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Published as a conference paper at ICLR 2026
Fully Human
Fully Generated
From safety risks to social consequences, drivertess cars present dangers that outweigh any potential benefits and therefore should not be allowed.
There shouldnt be driveriess cars because its dangerous and elimates many thingg,
0.00
1.00
A Gel "Fix any mistakess" Prompt
f lid "Sound Fluent" Ecit Prompt
fi "Make it more descriptive" Prompt
There shouldn't driverless because it's too dangerous and eliminates many things.
We should think twice before embracing driverless cars, as they can be dangerous and away many valuable experiences.
The prospect of driverloss cars raises serious concerns, as they not only pose risks but cherished experiences and milastones.
0.07
0.29
0.36
Figure 1: Al edits exist continuous spectrum from fully human written fully Al generated. Here we show three versions of the same human-written text after different edits have been applied by an LLM alongside the cosine distance between the edited text and the fully human text. Texts have been truncated for space. "Fix any mistakes," the most mild edit according to cosine distance, results in text with only spelling and grammar errors corrected, while "Make more descriptive" closely adheres to the ideas in the human-written text while substantially rewriting it.
rewritten and restructured? Rather than classifying a text category, our model directly regresses score that indicates the degree of AT involvement in the production of the text whole.
Our contributions are the following:
1. We introduce comprehensive dataset spanning full taxonomy of Al-edits to human- written texts.
2. We quantify the amount of Al editing applied to each text via lightweight similarity met- rics, and validate that the similarity metrics correlate with the judgments of expert human annotators trained to detect AI writing styles.
3. We these similarity metries finetune regression head on an open-source large lan- guage model to detect the amount of Al-editing present given only the edited text.
4. When converted from a regression model to a binary or temary classification model, we show that model, EDITLENS, achieves state of the art performance, outperforming the best binary classifiers by 8%, and outperforming the best ternary classifiers by 16% (macro-F1).
5. We also show that unlike the discrete classifiers, the regression model is able to show nuance in progressively classifying more intense edits with higher scores, with case studies APT-Eval, Beemo, and Grammarly.
Our findings have wide-ranging implications for AI text detection policy. By enabling measurement for the level of Al involvement, more flexible policies acceptable usage of generative AI models can be consistently enforced. Furthermore, our work can help mitigate false positives, a critical limitation of existing binary text classifiers. With the ability to control the amount of Al editing allowed, a much lower false positive rate can be achieved under the policy cap framework suggested by Jabarian & Imas (2025) for implementation in high-stakes settings such academic integrity.
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Published as a conference paper at ICLR 2026
HETEROGENEOUS-
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Chickon Banh-Mi, which always comes out warm and fresh out of the oven.
Finish this review adding what got for dessert!
Make my review more descriptive!
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during work week, and never had bad experience, By farm, my favorite thing det the Chicken Danh-Mi, which always comes out warm and fresh out the And I'm still
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after, resist resist finishing meal with dessert the pancian wattles are fluffy and fragrant, parfectly paired with scoop of ice cream. It's the kind sweet endligg that makes the whole meal feel complete.
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My absolute tavorine order, every single time, is the Chickan Banh Mi, The bread arrives
Human
AI
Al Editec
Figure 2: Examples heterogeneous and homogeneous mixed authorship texts. heterogeneous mixed text, authorship of each token is clearly attributable. But in homogeneous mixed text, the human-originated ideas are clearly present in each rewritten sentence by the model, making it im- possible to assign binary labels of authorship any word sentence.
QUANTIFYING AI EDIT MAGNITUDE
2.1 HOMOGENEOUS vs. HETEROGENEOUS MIXED AUTHORSHIP
To better motivate our work, we first introduce the concepts of heterogeneous and homogeneous mixed authorship texts.
In the heterogeneous case, authorship of each segment of text can be directly attributed to a human AI. An example of this is situation where human writes paragraph and asks the Al to write the following paragraph. In cases like this, there exist one or more boundaries between human and AI segments. One can create token-level labels for heterogeneous mixed texts: every token was authored by either human or AI. Heterogeneous mixed text detection (also called fine-grained AI text detection) has been previously studied Kushnareva al. (2024) Wang al. (2023) and Lei et al. 20255)
In the homogeneous case, authorship is entangled by the editing process. An example of this is situation where human writes paragraph and asks an Al to paraphrase it. Even if Al Alpellac every word in the paragraph with a synonym, authorship is still mixed. As such, token-level binary labels are insufficient measures of authorship in this case, as both parties have provided input throughout the entire document. Despite increasing prevalence, homogeneous mixed AI text is understudied, and we focus the rest of the paper on detecting this kind of mixed text.
2.2 TASK DEFINITION: HOMOGENEOUS MIXED TEXT
In many practical scenarios, a human-written document is subsequently edited to yield new doc- ument y, where multiple sequential edits may have been performed by one or more agents (human or Al) in indistinguishable fashion to produce y. Unlike the heterogeneous mixed-text setting, where each segment is assumed to be authored wholly by either human or an LLM, here authorship is latent and entangled within the editing process. Our objective is not attribute authorship. but to predict the magnitude of change between x and y according to a similarity metric that agrees with expert judgments of the magnitude of AT writing style and semantics.
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Published as a conference paper at ICLR 2026
We model the edited text the image of an editing operator &x applied 2:
Ex;=;), ~~ >>p(2), À SEA. A,
where = denotes a (latent) sequence of micro-edits (insertions, deletions, substitutions, reorderings) possibly performed by mixture of editor types (humans or Als) and À summarizes an edit intensity. In the homogeneous setting, the editor identity within is unobserved and required at training or inference time. For simplicity, in this study, we focus on the case where human text is edited in one pass by single Al language model, but we also present results for multiple passes, and human-edited Al text as case studies in generalization.
Similarity driven target. Let sim X' X + [0,1] be fixed similarity functional. We define change magnitude functional A X' x x [0, 1] by monotone transformation of similarity (or distance):
A = Asim( (z, v)), e.g. -(s)=1-s
where sim nonnegative distance. A(x, y) for identical texts (no edits) and increases as heavier editing is applied to form We motivate the particular choice of sim below via agreement with expert annotators' perception of the amount of pervasiveness within a text, and it assumed known during training and evaluation.
Inference with edited text only. In most practical settings, only the edited document y is available at inference time. We therefore learn a single-ippul predictor that maps directly to change magnitude without reconstructing or retrieving a source æ:
føt X + [0,
Á(y) - 18 (2))
Training remains supervised using pairs {(x(9, y only to compute targets (0) ((x(1), ) the model never conditions on x at inference. Concretely, we optimize
N
min N
Σ (a A(x(), (()))
The Bayes-optimal predictor for this objective is the conditional expectation (v) B[A(X,y) Y u).
but crucially we do not estimate this expectation via reconstruction of æ. Instead, fa learns discrim- inatively from alone, absorbing the necessary inductive biases (e.g.. lexical volatility, style drift, fluency/consistencyy cues) to approximate f* from labeled examples.
For additional discussion of the precise differences between homogencous and heterogeneous mixed detection formulations, see the Appendix.
TRAINING A MODEL TO DETECT AI EDITS
3.1 CREATING HOMOGENEOUS MIXED TEXT DATASET
Because no dataset of homogencous mixed Al-generated text exists at scale, we create training set for this task.
We begin by collecting a source dataset of fully-human and fully-Al-generated texts. We select human-written texts from prior the release of large language models in 2022 from domains: re- views from Amazon (Zhang et al. 2015) and Google (Li al. 2022), creative writing samples from Reddit Writing Prompts (Fan et al.) 2018), general educational web articles from FineWeb-EDU [ozzkkv et al. (024). and news articles from XSum (Narayan et al. 2018) and CNN/DailyMaii See et al. 2017). As a holdout domain to measure out-o--distiibution performance, we also include the Enron email dataset (Cohen 2015).
Then, we generate an AI example corresponding to each human example following the synthetic mirroring procedure introduced in Emi & Spero (2024). We use GPT-4.1, Claude 4 Sonnet, and Gemini 2.5 Flash. We also include 3.3ma-3.3-70s Instruct-Tunt performance out-of distriiiiiit LLMs. Our final train, test, and val splits contain 60k, 6k, and 2.4k examples respectively. We estimate the cost of creating this dataset to be roughly $530. Addi- tional dataset summary statistics can be found in Tables 11
as a holdout LLM to measure
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Generation
Intermediate supervison
Bucketing
Training
& Fully Humar
Sample
Model
Sample
0.0
Edit CLS Head
Samsle
0.0
Sample
Al Edined
Edit
Sample 2
0.06
Prompts
Fully Human
Sample
Sample
0.13
Pr..P Paoz) Bucket Probs
Sample
Predicted Score
Synthetic
Mirroring
T Fully At Text
Samole
1.0
[011
Sample
1.0
Labels
Decoding
Figure 3: EDITLENS architecture. We generate fully AI and Al-edited versions of human source texts, then use lightweight similarity metries intermediate supervision. We partition the into buckets according to supervised score and experiment with training both a regression model and n-way classification models, then using weight-average decoding obtain a numerical score.
3.2 EDIT PROMPTS
We collected set of editing prompts by first prompting ChatGPT 4o, Claude Sonnet 4, and Gemini 2.5 Pro, then adding small number of prompts written by the authors. In total, collected 303 editing prompts. The full list of prompts and summary statistics about the categories and contributors be found in Tables 9 and TO While this list of prompts is exhaustive, it encompasses significant coverage of the different ways that people use Al to edit texts. We split this list of prompts into train, test, and validation splits so that the model cannot overfit to a particular set of prompts.
3.3 INTERMEDIATE SUPERVISION METRICS
We experiment with two methods labeling the "difference" A (xx, y) in text before and after AI editing. The first the cosine distance (I cosine similarity) between the Linq-Embed- Embed-Mistra (Choi cct al. 2024) embeddings of the source text and the Al-edited version. We chose this embedding due its strong all-around performance on the MTEB benchmark (Muennighoff et al.) 2023).
The second is precision based method similar to the embedding based ROUGE proposed by Ng & Abrecht (2015): given minimum (a) and maximum (b) sequence length, enumerate all phrases (including overlaps) of between a and words the source and edited texts. We compute the pairwise cosine similarity between phrases in the source and edited texts, then count the number of phrases in the edited text with cosine similarity above a threshold 7 for any phrase in the source text. This count is divided by the total number phrases the edited text, making a precision- based metric. We refer to this metric as the soft n-grams score throughout the paper. Soft n-grams reduces n-gram overlap between source and target when
We choose soft n-grams because it expresses similarity when the Al editor replaces phrase or word with semantically similar one rather than requiring exact matching. note that this supervision metric shortening-invare i.e, simply deleting text from the source still yields soft n-grams score of 1.
3.4 HUMAN AGREEMENT WITH INTERMEDIATE SUPERVISION METRICS
well these automatic metrics actually capture the extent of Al editing in text? To support choice of intermediate supervision metric, conduct study that
How

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Published as a conference paper at ICLR 2026
asks ings
human annotators to compare two Al-edited versions of the same text after find- humans are effective detectors of Al-authored text.
by
Russell et al. 2025 that
Extreme minor polish
1200
EditLens
Pangram
Task setup. Annotators are shown 3 texts side-by-side: hu- man written source text alongside Al-edited versions of the source text. The labeling interface can be seen in Figure 7 Be- tween the two Al texts, annotators are asked to select which text contains more Al edits. Annotators must select text, so no ties are allowed, but they are also asked for their confidence in their choice leave freeform comments on each task, but were not required to do so. We recruited 7 annotators with extensive daily exposure to both human writing and Al Al-generated texts. Each annotator completed all 200 tasks, and was compensated $400 (USD).
800
600
400
200
scale. Annotators have the option to
polish
1200
1000
Pangram
Task generation procedure. We randomly sample 200 human-wrine worunnnrrttennee texts of between 50 and 300 words from our test set. We then generate two Al-edited versions of each source text using randomly assigned prompts.
800
400
0
Agreement with metrics. We report Krippendorff's Krip pendorff (1980) for 6 annotator and each of our two supervi- sion metrics by treating each metric an eighth annotator by designating the higher scoring text as the metric's 's selection. a = 0.72, CI [0.66, 0.77] for cosine and = 0.71, CI [0.65, 0.76)
Slight major polish
700
EditLens Pangram
600
for soft n-grams (2,000 resampling units).
400
300
200
3.5 MODELING DETAILS
100
Using QLoRA (Dettmers et al. 2023). we finetune models of between 3 and 24B parameters from the Mistral and Llama families. We use QLoRA to sweep the widest possible range of sizes of base models on a single GPU. We leave other finetuning and modeling ar- chitecture choices future work. We experiment with directly training a regression head using MSE loss well as training an n-way classification model, then decoding the output score, using weighted-average decoding rather than traditional argmax decoding. Additional modeling details can be found in the Appendix.
Major poltsh
EditLens Pangram
backbone that fit in VRAM
600
500
400
300
200
100
0.6
pred score
4 RESULTS
Figure 4: Distributions for ED- ITLENS and Pangram the AI Polish dataset (Saha & Feizi, 2025). Pangram overwhelmingly tends to predict a score of either 0 1, while EDITLENS captures the increasing levels of Al polish applied to the texts.
EDITLENS demonstrates significantly more nuanced AI detec- tion than existing classifiers through both quantitative metrics and qualitative analysis. We report results for our best ED- ITI.NNS model according to ternary classification metrics (see Section 4.3 and Table 2 trained on soft n-grams and cosine scored data after hyperparameter sweep. Both models have a Mistral Small (24B) backbone and were trained on 4-way classification task
'The 7th annotator excluded because they had poor agreement with all other annotators, but agreement with hoth metrics still moderate when including the 6th annotator. Krippendorf's was 0.66, CI [0.60, 0.71] 1] for cosine score and 0.65, CI [0.59, 0.71] for soft n-grams (2,000 resampling units).
'Additional results for different model sizes, model families, and values of n for n-way classification he found Tables 16

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We compare EDITLENS with several open- and closed-source Al detection baselines. On the Al Polish dataset (APT-Eval) Saha & Feizi (2025). EDITLENS achieves substantially stronger cor- relations with edit magnitude metries compared binary detectors (correlation 0.606), markedly outperforming the best binary baseline Pangram (correlation 1. This quantitative superiority is complemented by clear qualitative differences: while binary classifiers like Pangram predict scores clustered near O or 1, EDITLENS produces a nuanced distribution that appropriately tracks increas- ing levels of AI polish from minor major edits. The model's regression-based approach enables it to achieve state- state-of-the-arr performance across evaluation paradigms, delivering 94.0% accuracy in binary classification (human vs. any AI) and 90.2% accuracy in temaay classification (human vs. Al-edited vs. Al-generated), substantially outperforming existing binary and ternary detection methods. Additionally, EDITLENS generalizes effectively outside its training distribution: to unseen prompts, LLMs, and domains, to human-edited Al text in the BEEMO dataset, and to Al-edited Al text as well as multi-edited AI text.
4.1 AI POLISH DATASET
We first compare the performance on the AI Polish dataset (APT-Eval) of EDITLENS against the best-performing binary AI classifier, Pangram. APT-Eval contains both degree- based AI-edited text, with 4 discrete categories (extreme minor, minor, slight major, and major polish levels), as well as percentage- based Al-edited text, where LLMs were asked to edit certain percentage of the text, varying from 1-75%
While there are no direct or exact labels, the score should generally monotonically increase as the amount of requested polish increases. In Figure 4 we qualitatively assess the distribution of the model prediction scores on the degree-based edits. We see clear difference between the be- havior of EDITLENS versus the behavior of Pangram. Pangram almost always predicts score very close to 0 or I, while EDITLENS is able to quantify the increasing levels of polish applied. We show the equivalent distributions for percentage-base based polishing in the Appendix.
Quantitatively, we also report the correlation value between the EDITLENS predicted score and the similarity metrics between source and target provided by APT--eval in Table For EDITLENS and all binary classification baselines, we measure the Pearson correlation coefficient (r) between the prediction scores and the semantic similarity (-0.606), Levenshtein distance (0.799), and Jaccard distance (0.781) metrics between the pre-As-polihhed and post-Al-polishee documents. Stronger correlation values mean that the model is able to faithfully track edit magnitude across examples and distances increase),
assign higher scores as semantic similarity decreases (and Levenshtein/Jacce and lower scores when the edited text remains close the source. EDITLEN: exhibits significant correlation between these similarity metrics and its scores, while the binary AI detectors correlate less strongly with these metrics.
4.2 PERFORMANCE AS BINARY CLASSIFIER
In the binary classification setting. how does EDITLENS treat mixed text? Different use cases may have different standards for what they consider an acceptable amount of Al-generated text-a profes- sor may allow the use of AI assistance for proofreading, but disallow fully Al-generated essays.
To measure the flexibility of our model and the baselines to be able to adjust to different sen- sitivity levels, we calibrate and compute the performance of each model on two settings: fully human-written vs. any Al-edited or Al-generated text, fully human-written and Al-edited text vs. Al-generated text. We describe the calibration procedure in the Appendix. Model accuracy and F1-scores can be found in Table [] Notably, EDITLENS outperforms our three binary baselines, FastDetcctGPT. Binoculars, and Pangram, on our test set consisting of fully human human-written, fully Al- Al-generated, and Al-edited texts.
4.3 PERFORMANCE AS TERNARY CLASSIFIER
To compare with categorical mixed AI detection models, evaluate each model on three classes: human, Al-generated, or Al-edited. To convert each binary classifier into a ternary classifier, we find two thresholds using the calibration procedure above held-out validation set, optimizing the F1
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(a) Human vs. Any Al
(b) Fully Al Al-Edited Human
Model (Threshold)
(%)
F1
Model (Threshol))
Acc. (%)
F1
DestDetcctGPT
69.1
80.5
FastDetec11PPT
90.6
84.4
Binoculars
68.6
81.4
Binoculars 0.601
31.4
47.7
Pangram 0.01.
80.7
83.7
Pangram 0.08
92.3
89.0
EDITLENS (Cosine) 0.399
93.8
95.4
EDITLENS (SNG) (0.988)
94.3
90.2
EDITLENS (SNG) (0.041)
94.0
95.6
EDITLENS (Cosine) (0.960)
96.4
94.1
Table 1: Accuracy and Fl-score on two binary classification tasks: (a) human vs. any Al generated or edited texts and (b) fully Al-generated texts vs. Al-edited and human texts. Thresholds were calibrated using the val set. "SNG" and "Cosine" denote EDITLENS trained with soft n-grams supervised data and cosine score supervised data, respectively.
score between the human/mixed and mixed/Al classes. The decoding procedures for GPTZero and DetectAIve are detailed in the Appendix.
Model
Type
Accuracy
Macro-Fl
Human F1
Al-Gen. F1
Al-Edited F1
Binoculars
Binary
48.5
50.1
54.7
58.4
37.4
FastDetectGPT
Binary
60.6
56.8
25.1
84.4
61.0
Pangram
Binary
73.0
69.5
76.4
89.0
43.2
DelectAlve
Ternary
57.8
52.5
79.9
16.1
61.5
GPTZero
Ternary
74.7
72.7
77.3
89.8
50.9
EDITLENS Soft N-Grams
Regression
89.7
89.9
89.6
94.1
86.1
EDITLENS Cosine
Regression
90.2
90.4
90.4
94.1
86.8
Table 2: Ternary classification performance across different models. Thresholds calibrated using the validation set. "Soft N-Grams" and "Cosine" denote EDITLENS trained with soft n-grams supervised data and cosine score supervised data, respectively.
4.4 NEGATIVE CONTROL: HUMAN-EDITED HUMAN TEXT
In order to ensure that EDITLENS is specifically detecting Al-created edits and not any edits, we run negative control on human-edited human text. We use the ArgRewrite V.2 (Kashefi et al. 2022) dataset, a corpus of annotated argumentative revisions, in order to evaluate the effect on the EDITLENS score of human-editing of human-written text.
We find that the average EDITLENS score (on a 0-1 scale) on human-edited human text is 0.012. The average EDITLENS score of human text 0.009, and the average EDITLENS score of Al-edited human text on the same dataset is 0.86. We conclude from these results that EditLens does not predict human-edited human text as Al-edited. Human-edited human text predicted effectively the same as human text by EditLens.
This result is due to the fact that numan-ediied human text implicitly present the human-lait llbeld portion of our dataset (e.g, news articles go through multiple rounds of human editing before publi- cation).
4.5 POSITIVE CONTROL: AL-EDITED AI TEXT
We also evaluate our detector's mean score difference on Al-edited, Al-generated text, to make sure that this kind of text is always predicted as fully Al-generated. We take synthetic mirrors of our original human dataset. considered to be 'Al-generated documents' and edit them using our held- out prompt set. On dataset size of n=412, the mean score difference for single edit pass originally human text 0.38. The mean score difference for a single edit pass on an originally AI text is -0.05.
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4.6 GENERALIZATION TO HUMAN-EDITED AI TEXT (BEEMO)
While the majority of our studies focus on Al-edited human writing, we also evaluate the perfor- mance of EDITLENS on human-edited Al text using the BEEMO (Artemova et al. 2024) dataset, which includes human expert-edited versions of Al model outputs. We find that the model ade- quately generalizes human-edited Al text. The average decrease score from the model output to the human-edited model output is 0.33 + 0.30, with the score decreasing after human-editing in 88.9% of the documents, More details are presented the Appendix.
4.7 CASE STUDY: GRAMMARLY EDIT DATASET
Fix any mistakes Paraphrase
Improve
Simplify it
Sound fluent
Shorten it
Make more descriptive
Make it more detailed
Summarize
0.0
0.4
0.6
1.0
EditLens Score
Figure 5: Distribution of EDITLENS scores dataset from Grammarly by edit instruction.
Grammarly a popular subscription- AI writing assistant that allows users to edit text using both pre-filled rrelfilled and custom prompts within native word processor. We manually collect dataset of 1768 samples using 9 of the default prompts offered by Grammarly to simulate typical user queries editing by sampling 197 hurten-written source texts and applying each of the edits to them in the Grammarly web interface. In Figure 5] we present the distributions of EDITLENS scores on examples from each editing instruction sorted by the median. Perhaps counterintuitively. EDITLENS considers "Fix any mistakes" the most minor of all edits, while "Summarize this" and "Make it more detailed" are the most invasive edits. Figures [I and 10 we show this is also true according to both the cosine and soft n-grams scores of the examples.
RELATED WORK
Binary AI-Generated Text Detectors. Several works have explored the binary setting of distin- guishing fully-human from fully-Al-generated text. DetectGPT (Mitchell al. (022)). FastDetect- GPT (Bao et al. 2024), DNA-GPT (Yang & Cheng 2024), and Binoculars (Hans et al. 2024 are training-free approaches that leverage statistical properties of Al-generated text perform binary detection. Ghostbusters (Verma et al. 2024) is an open-weight classifier trained on simple features from the text, while closed-source classifiers such GPTZero (Tian & Cuil 2023) and Pangram (Emi & Spero (0244) have more recently emerged as accurate Al text classifiers. All of the above methods operate in the post-hoc setting, contrast to work on watermarking (Kirchenbauer al.) 2024) in which the model's decoding algorithm is modified to enable detection.
Heterogeneous Mixed Text Detection. As described above, previous work on mixed AI and hu- man text detection focuses on the heterogeneous case: where distinct boundaries can drawn between fully Al-generated and fully human-written segments. Examples of these works include AI Boundary Detection with RoFT (Kushnareva et al. 2024), SeqXGPT (Wang et al. 2023), HaCo-Det
(Su et al. 2025), and PALD (Lei et al. 2025).
httpp:: /www.wwwoo grammaa r y com/
OOcassionally, Grammarly would abstain, leaving with fewer than 197 samples.

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Categorical Mixed Text Detection. Alternatively, some previous work has instead focused mixed text as an additional category or categories in addition to human and AI. DetectAlve (Abassy) et al. 2024), HERO (Wang et al., 2025), and GPTZero (Tian & Cui 2023) all examples where mixed categories have been added. We find the limitation of this approach is that the amount of editing cannot be quantified: all mixed text is treated the same.
Human-Edited AI Text. In addition to our problem setting of Al-edited human written text, there also studies and datasets focusing on human-editeds Al-generated text. Beemo (Artemova al. 2024) is a benchmark focusing on expert-edited Al text. LAMP (Chakrabarty et al.] (0255) ) is corpus of ----errrrtteed paragraphs that have been improved by professional writers according a defined taxonomy.
Paraphrasers and Humanizers. Several previous works have studied the effects of automated
paraphrasers (Krishna et al. [2033 Russell et al. 2025; Sadasivan et al.) (0255) Cheng et al. (2025)
and "humanizers" (Masrour et al. 2025) how they degrade Al-generated text. We explore the
effect of AI rewriting of AI outputs as it relates to our model in the results.
DISCUSSION AND LIMITATIONS
While EDITLENS can quantify the degree of editing as measured by distance metries from embed- dings commonly used for retrieval, classification, etc., the limitation of EDITLENS is that it cannot say how the text was edited or produced. Quantifying edits on a one-dimensional scale is reduc- tive: there are multiple modes of editing such as tone adjustment versus adding original content that are important to differentiate, and EDITLENS treats all types of editing on the same footing. Future work could quantify the extent of editing on these different qualitative axes. Finally, the embeddings themselves could be further optimized to align better with human perception of AI editing or other metrics, such as factual integrity of the original source semantic similarity, rather than using simple off- the shelf embedding for retrieval. The embeddings themselves could also be optimized directly for the AI detection task. We leave these experiments for future work.
7 CONCLUSION
In this study, we introduce the task of continuous fine- tme-ggrined Al edit prediction, and show that ED- ITLENS, based on simple embedding- baased supervision finetuned language model, significantly outperforms existing Al detection approaches. By moving beyond binary or categorical detection frameworks, our method provides more nuanced view of mixed-authorship text, quantifying the magnitude of Al editing rather than simply flagging the presence of Al-generated text. This capa- bility enables more flexible policy decisions around the use of generative AI. We release our dataset and models to encourage future research in this area.
ETHICS STATEMENT
Our research involved using 3 human subjects annotate the degree of Al-editing present text. We obtained informed consent from the subjects and fairly compensated them for their labor. We commit to maintaining their privacy.
Inaccurate Al detection software can cause harm as false accusations of Al misconduct can result in serious consequences, including emotional trauma, reputation damage, and undue punishments for academic misconduct. We acknowledge that our model has non-zero error rate and its errors may result in such harms. We commit to continuing to engage with the academic community to educate others appropriately contextualizing and communicating the results of AI detection software. We also commit to releasing the model for non-commercial use only and responsibly vetting access to researchers and educators.
We intend for our contribution to the research on AI detection to ultimately mitigate harm by provid- ing more nuanced picture of AI usage than binary AI detection classifiers. The ability to calibrate the sensitivity level of the regression model is also a step towards mitigating the false positive rate and lowering the overall number of false accusations of AI misconduct.
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9 REPRODUCIBILITY STATEMENT
We release the dataset, source code, and model weights at https://github.. com/ pangraml abs abs//dii//e/esen
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A DIFFERENCES WITH THE HETEROGENEOUS MIXED TEXT DETECTION TASK
The PaLD (Lei al. 2025) formulation considers a text segmented I Di ....... In where each segment 2; is assumed to originate from either a human or an LLM, i.e., 2; ~ Phuman or z; PLLM- The learning objective is infer latent per-segment authorship la- bels d1:n E (human, LLM) (and optionally segment boundaries), estimating po(a1.n x) and predicting &i.. arg max po (0in. x).. In contrast, our homogeneous mixed text pre- diction task dispenses with provenance as supervision and regresses authorship-agnostic edit magnitude aligned to similarity metric. Given pre/post pairs only to derive targets, infer- ence relies on single-input predictor (y) that maps the edited text y directly to Á(y) e [0, 1], without segment labels, boundary inference, reconstruction of the source. This framing changes (i) the assumptions (binary authorship mixture vs. latent, entangled edits), (ii) the outputs (label sequence d1: scalar/regional magnitudes A), (iii) the super- vision (segment-level authorship vs. metric- aligned change signals), and (iv) the evalua- tion (classification metrics such accuracy/F1 correlation and error against A, plus cali- bration).
0.65 0.19
EditLens Prediction
0.58 0.21
0.52 0.24
0.6
0.39
0.25
0.4
B PERFORMANCE ON MULTI-EDITED AI TEXT
Mean Trojectory
We also examine the case where multiple AI- edits have been applied to a single piece of text. We test our model on this case by applying a series of 5 edits to piece of human-written written text and measuring the EDITLENS score after each subsequent edit. In Figure 6 we show that for each edit, the mean increases monotonically.
Number of Edits
Figure 6: "Trajectory" of EDITLENS scores after
subsequent AI edits to single text.
C OUT-OF-DOMAIN PERFORMANCE
During dataset creation, we hold out both model and a domain to test the ability our model generalize out-of-distribution texts. We created an OOD model test set of 3k examples with
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Raw scores
4 buckets
5 buckets
6 buckets
Cosine Distance
0.67 0.06
0,50 : 0.05
0.52 : 0.05
0.55 : 0.05
Soft N-Grams
0.50 0.05
0.52 0.05
Table 3: Agreement (Krippendorff"s a with bootstrap SE) between human annotators and proposed intermediate supervision metries under different bucketing schemes for scores.
Llama-3,3-70B 3,3-70B-Instruct-TB generated and edited texts as well as an OOD domain test set using the Enron email dataset (Cohen 2015) source texts, and measure the degradation in macro-Fl score our best model, EDITLENS with cosine supervision. On the OOD domain dataset, macro- F1 the ternary classification task decreases from 0.904 to 0.866 (-0.038). On the OOD LLM dataset, macro-F1 on the ternary classification task decreases from 0.904 to 0.850 (-0.054).
D HUMAN AGREEMENT WITH INTERMEDIATE SUPERVISION METRICS
compute the score for each pair source and Al-edited texts, then assign each Al-edited text to one of buckets according to the bucketing scheme described in Section G All a values are reported in Table
E ABLATION: USING A DIFFERENT EMBEDDING MODEL FOR SUPERVISION
We ran an additional ablation study with Qwen3 (Zhang et al. 2025) embeddings as supervision.
BINARY CLASSIFICATION
Best Human F1: 0.96
Best AI F1: 0.93
TERNARY CLASSIFICATION
Accuracy: 0.902
Macro F1: 0.903
Class
F1 Score
Human
0.891
AI
0.959
Al-Edited
0.861
Table 4: Per-class Fl scores for ternary classification.
We observe no significant difference results between models trained on cosine distance different embeddings.
F CALIBRATION PROCEDURE
We calibrate the threshold for the model for both the binary and ternary settings. We first segregate the data into three categories: human, AI, or mixed. Mixed text is human text that has any amount of AI editing applied. Our goal is then to find two thresholds, lower threshold and upper threshold. The lower threshold separates the [Human] class from the union of [AI, Mixed] classes. The upper threshold separates the [AI] class from the union of [Human, Mixed] classes. Both thresholds are found by maximizing the Fl score on a separate held out validation set for these two labeling schemas.
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G MORE MODELING DETAILS
We use QLoRA to sweep both Llama and Mistral families of backbones between 3B and 24B pa- rameters. We experiment with both direct regression head and N-way classification head with weighted-average decoding.
Determining thresholds for fully human and fully Al texts Some edits are too small to be detectable, such as adding single comma, correcting typo, We choose minimum threshold of 0.03 for cosine distance threshold and 0.06 for soft n-grams in order to supervise it as Al-edited, chosen through manual inspection and validation of edits we would consider small enough such that the authorship is still entirely human.
Additionally, there are cases the other end of the spectrum where Al so pervasive in text that essentially rewrote the entire document and it became fully Al-generated. To measure the upper threshold where would consider between the sources and their corresponding fully Al-generated synthetic mirrors. We selected thresholds that best separate fully Al-generated synthetic mirrors from the heaviest AL-edited text, which were 0.15 for cosine distance and 0.72 for soft n-grams.
fully Al-generated, we analyzed the similarity metries
G.1 REGRESSION FORMULATION
Let denote the raw similarity score and Now and Thigh be the low and high thresholds, respectively. We define the scaled similarity score as:
0.0
if S Tlow
1.0
if 2 Thigh otherwise
(I)
8- Die Thigh Tax
The regression model directly predicts the scaled similarity score using mean squared error loss:
1 Σ (5. - 3,)²
MSS
(2)
1=1
where n is the number of training examples.
G.2 CLASSIFICATION FORMULATION
For the classification approach, we discretize the similarity scores into N buckets, where N E
(4, 5, 6). Given minimum and maximum thresholds Tiin and Tmaxs define the bucket assignment function:
b(8) min N 1,
Tmin
N
Tiaxx Tiin
The midpoint of bucket j is given by:
(j + 0.5) (Tmax Tmin)
m; = Tmin +
(4)
N
We train the classification model using cross-entropy loss:
LCF =-1 Σ logp(b(s))la))
(5)
where p(flar)) is the predicted probability for bucket given input 2;-
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During inference, decode the final similarity score using weighted average strategy:
$ = Σ p(j my
(6)
where p(j(z) is the predicted probability for bucket j and my is the corresponding bucket midpoint.
G.3 ARCHITECTURE AND OPTIMIZATION
train model for epoch with AdamW using a batch size of 24 and a constant learning rate of 3e-5. We initialize the model with pretrained weights from the base model and target all linear use a LayerNorm and single
layers: self-attention QK output, and linear layers the MLP. linear layer the head for both prompt classification and edit heads and supervise both jointly in a multi-task learning routine. On A100 GPUs, this takes approximately 8 hours for the largest model.
H TERNARY CLASSIFIE DECODING
GPTZero reports probabilities of three classes: "human", "AI", and "mixed," so we simply use argmax decoding select the highest probability class. DetectAIve reports probabilities of four classes: "human", "AI", "Al Polished", and "AI humanized". We attempted to group "AI human- ized" predictions with both the "Al" and the "AI Polished" categories for ternary classification, and found that grouping with "Al" produced a higher Fl score. Therefore, we group "AI Humanized" and "Al" into single category for purposes of comparison.
TERNARY CLASSIFICATION CONFUSION MATRICES
Analyzing the confusion matrix, we see that EDITLENS exhibits much stronger performance on the Al-edited text category than the strongest ternary classifier, GPTZero. While both EDITLENS and GPTZero are nearly perfect at distinguishing fully Al-generated text from fully human-written text, EDITLENS is the only model able also consistently detect Al-edited text as a distinct category from fully human and fully AI.
GPTZero
EditLens (Ours)
Human
Mix Human
1881
0
1770
111
True Label
True Label
Mix
1101
786
351
265
1945
28
AI
1
63
1817
AI
0
186
1695
Human
Mix
Al
Human
Mix
Al
Predicted Label
Predicted Label
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Model
r. somantic sim. +
r, Levenshtein f
Jaccard 1
FastDetectGPT
-0.287
0.331
0.280
Binoculars
-0.29
0.326
0.274
Pangram
-0.491
0.615
0.556
EDITLENS (Ours)
-0.606
0.799
0.781
Table 5: Pearson correlation coefficients by model.
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corlesed
Figure 7: screenshot of our data annotation interface.
J CORRELATION BETWEEN EDITLENS PREDICTIONS AND AI POLISH SIMILARITY METRICS
K DATA GENERATION MODELS
Checkpoint Name
Provider
# Params
Open Source
claude-soomett-42222500000 202 Anthropic 2025
Anthropic API
?
No
meta-1lama/Llamm-33 3-700- Instruct Turbo
Meta
2024c
Togetherai API
70B
Yes
gpt-4.1-2025-04-14 OpenAl 2025
OpenAI API
?
No
comini-2.5-fal 5lsshh Google 2025
Gemini API
? No
Table 6: Models used for dataset generation
L EMBEDDING MODELS
Checkpoint Name
# Params Dim. Size
Lingall--eeerrrr//hhngghhhhhaaar Choi et al. 2024)
4096
senterocettaassffomme///11111111 Reimers al. 2021
22.7M
384
Table 7: Embedding models used for supervision
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M
BASE MODELS
Checkpoint Name
Params
Open Source
mistralal/Mistral -emo Base
2407
Mistral
2024
12B
Yes
nittrralai/MMittrra--iiaaa------
Mistral
2025
24B
Yes
meta meta-Llama/Lllma--33.. 8B Mcta
2024a
8B
Yes
meta- mata-1lama/tlamma
Meta 2024b
3B
Yes
Table 8: Base models used for training
N MORE RESULTS ON HUMAN-EDITED AI TEXT
We focus on the human-edited Al versions of "Generation" and "OpenQA" categories of BEEMO, because the other categories, such as "Rewrite" and "Summarize", are already themselves Al-edited versions of human text, "Closed QA" the answers are so tightly constrained we would consider the answers to be human-written, and would not consider the model outputs fully Al-generated. We also measure the correlation coefficient between our similarity metrics and the model scores. The intuition for this is that the human edit is more invasive, we would expect the similarity metrics to increase, and the model score to decrease. As expected, we find moderate negative correlation between our model's scores and similarity, with 00.96 for cosine distance and 0.501 for soft n-grams.
In Figures and 9 we present the output distribution of EDITLENS for BEEMO's s Generation and OpenQA splits, on the fully Al-generated text (orange) and human-edited version (blue).
Diariizt on of piod score and anod acore al pan for Oper 0A
pnd www.
011
03-
13
30-
23
62
24
Figure 8: BEEMO Generation
Figure 9: BEEMO OpenQA
As is shown in the figures, the predicted score distribution moves significantly towards human- generated following editing, as expected.
O EDITING PROMPTS
Table Full list of editing prompts by split and contributor.
TRAIN
Editing Prompt
Contributor
Change this it fits what business person would want
ChatGPT 4o
Make this sound more sure and strong
ChatGPT 4o
Edit this people who know the topic well
ChatGPT 4o
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Published as a conference paper at ICLR 2026
Editing Prompt
Contributor
Make this more direct and hold
ChatGPT 4o
Make this sound fair and not take sides
ChatGPT 4o
Make this sound more serious and important
ChatGPT 4o
Make this sound more excited and energetic
ChatGPT 4o
Make this casier for someone who doesn't t know that much ahout
ChatGPT 4o
Make this more accessible to non-expert reader
ChatGPT 4o
Adapt this for readers with prior background in the topic
ChatGT 4o
Write this like you't re talking to someone
ChatGPT 4o
Make this sound more doubtful and questioning Adjust the voice sound
ChatGPT 4o
academic.
ChatGPT 4o
Make this sound more relaxed and friendly
ChatGPT 4o
Write this in a way that top company leaders would like
ChatGPT 4o
Make this more formal and proper
ChatGT 4o
Make this easier remember and repeat
ChatGPT 4o
Tailor this message to suit a lay audience Make this sound more exciting and well written Make this sound more serious and proper Use smart and serious tone like in official stuff Edit this to sound more urgent and important Make this more convincing and easier understand Change this so it's easy for a teen to read Make this more logical and fact-based Make consequences feel more important
ChatGPT 4o
ChalGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Make this sound uplifting and encouraging Rewrite this to align with a formal tone Make this more convincing and clear Sth grader understand it
ChatGPT 4o
ChatGPT 4o
ChalGPT 4o
Change this Take out hard words and explain them in a simple way Fix this to make it more interesting
ChatGP 4o
ChatGP 4o
ChatGPT 4o
Change this to make as strong as possible Rewrite this better suit a business audience Use simpler words so anyone can understand this Make this sound more like school writing Make this sound nicer and more fun Change this to sound more thoughtful Make this funnier and more lighthearted Use better words make this sound smarter Add some personality to this
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Make this sound more like professional writing Give examples help make this clearer Edit this with clear examples to help explain this better Add details that create mood or feeling
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
some story behind this to help understand Explain more about what this means and why it matters Explain the main points using real examples Add descriptions of sounds, smells, textures, and how things foel Include exact sizes, colors, and what things look like Add details about the setting and background Add details that help readers see, hear, and feel what's happening Add conversations and quotes to make scenes more real
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Develop this text further by explaining the implications
ChatGPT 4o
Help readers picture this more clearly with specific details Describe how things look, sound, or feel more
ChatGPT 4o
ChaiGPT 4o
Use interesting and specific describing words
ChatGPT 4o
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Published as a conference paper at ICLR 2026
Editing Prompt
Contributor
Add comparisons to help explain things better Use real-life examples to show what you mean Add background facts back up these points Add true stories examples from real life Use specific names of places, brands, and things
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Add more ideas or facts that support what you' re saying Add details about the place and situation Share what experts think or what research shows Use real examples to explain this better Use stronger, exciting action words Add illustrative examples to clarify these points Ensure the sentences transition smoothly from one idea to the next Help ideas move from one to the next easily
ChatGPT 4o
ChatGP 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChalGPT 4o
ChatGT 4o
Make the ideas connect better
ChatGP 4o
Make the language flow more fluidly without sounding forced Can you parts that sound choppy or
ChatGPT 4o
ChalGPT 4o
Make this writing smoother and better Make the words fit together better Make this easier and smoother to read
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Help ideas connect more smoothly
ChatGPT 4o
Make this sound like someone who speaks English well wrote it Make the sentences flow better
ChatGPT 4o
ChatGPT 4o
Make rhythm the sentences better
ChatGPT 4o
Remove all extra words and phrases
ChatGPT 4o
Trim this down while keeping the tone and meaning intact Remove extra or repeated words Use clearer words but keep the meaning
ChatGPT 4o
ChalGPT 4o
ChatGP 4o
Get rid of long confusing parts
ChatGP 4o
Make this shorter and more direct
ChatGPT 4o
Take out anything extra but keep the good parts
ChatGPT 4o
Say this in fewer words but still make it strong Take out words that aren't I needed to make this better Take out words that don't add anything Cut this down but keep the same meaning and style Rearrange this content for a clearer argument progression Put this in better order Put similar ideas together Build up to a strong ending Put this in an order that makes more sense
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
clearly
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Set this up a way that's casier read Group ideas that go together and add connections Move things around to make the main points stand out Split this into paragraphs that connect better Make sentences match and sound good together Put the most important stuff first
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Organize this information in more feier--riendly format Tell the story
ChatGPT 4o
interesting way Make this work better overall Make this writing more polished and effective Refine this
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
improve its overall impact
ChatGPT 4o
Fix this but keep the same mcaning
ChatGPT 4o
Fix this but keep the main point the same Improve this passage while preserving its original intent Restate this using different vocabulary and sentence structure
ChatGPT 4o
ChaiGPT 4o
ChatGPT 4o
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Published as a conference paper at ICLR 2026
Editing Prompt
Contributor
Use easier words that mean the same thing Share this idea from a new point of view Say this a new way
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Usc different words to say the same thing Say this using different words and sentence types Say this using different words and ideas Say this
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
and interesting way
ChatGT 4o
Paraphrase this to make it simpler and easier to understand Explain any hard words so people know what they mean Fix parts that sound weird hard read Say clearly who or what each word is talking about Make this clear and easy to read Make sure one idea leads the next clearly Make obvious what causes what Say this a clear and simple way Correct any grammar, punctuation, or spelling errors in this text Use better words and fix how the sentences written Make this more direct and confrontational Make this more memorable and quotable Make this sound more diplomatic and tactful Make this more emotionally resonant
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChalGPT 4o
ChatGP 4o
ChatGPT 4o
ChatGPT 4o
ChalGPT 4o
ChatGPT 4o
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Make this more formal
Claude Sonnet 4
Simplify for customers no technical background Make this suitable for social media sharing Inject enthusiasm and energy into this writing Translate this teenage audience Soften the tone while maintaining the message Make this more relatable to the reader's experience Make this more inspiring and motivational Add gravitas and weight this statement Adopt a more skeptical and questioning tone Make this more casual
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Adjust peer-reviewed academic journal
Claude Sonnet
Convert
analytical and logical approach
Claude Sonnet 4
Make this appropriate for C-suite executives Add vivid imagery and sensory details to bring this to life Add backstory
Claude Sonnet 4
Claude Sonnet
enrich understanding
Claudc Sonnet 4
Add depth and context make this more comprehensive Include specific measurements, colors, and physical characteristics Flesh out these ideas with supporting information Provide conerete examples illustrate these points Add sensory details to make this more vivid Use more precise and colorful adjectives Add dialogue and quoted speech to make seenes more vivid Elaborate on the key points with conerete details Add storytelling elements to increase engagement
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Add descriptive metaphors and similes enhance understanding Expand with real-world applications
Claude Sonnet 4
Claude Sonnet 4
Use more evocative and powerful verbs
Claude Sonnet 4
Include expert opinions research findings
Claudc Sonnet 4
Incorporate specific brand names, locations, and proper nouns
Claude Sonnet 4
Paint clearer picture with specific visual descriptions
Claude Sonnet 4
figurative language to make concepts more tangible Include personal anecdotes studies
Claude Sonnet
Claude Sonnet 4
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Published as a conference paper at ICLR 2026
Editing Prompt
Contributor
Make this read like it was written by native speaker
Claude Sonnet 4
Make this sound more conversational and engaging Improve the rhythm and readability this writing Make the progression of idcas feel effortless Create smoother connections between these ideas Improve the natural rhythm of this text Smooth out the awkward phrasing in this passage Eliminate any choppy aw awkward sentences Remove every unnecessary word and phrase Trim the fat without losing the muscle
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Make this more concise without losing important information Eliminate wordy expressions and redundancies
Claude Sonnet 4
Claude Sonnet 4
Tighten this writing by removing unnecessary words
Claude Sonnet
Arrange these points in order of importance Create better section breaks and headers Create more compelling narrative are Build toward stronger climax conclusion Reorganize this for better logical flow Use parallel structure to enhance readability Break this into clearer paragraphs with smooth transitions Revise this to make it more engaging
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Enhance the overall effectiveness of this passage
Claude Sonnet 4
Refine this writing to make more professional Enhance this text while maintaining the original meaning Optimize this text for maximum impact Strengthen this writing by improving word choice and structure
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Transform this into more compelling prose
Claude Sonnet 4
Polish this text for clarity and readability Upgrade the sophistication of this writing Recast these ideas in a different style Rephrase this text to avoid repetition Express these ideas using alternative phrasing Say the same thing but a fresh way Present the same information from fresh angle Reframe this argument using different terminology Add precise measurements and timeframes Define any terms that might be unclear
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Eliminate ambiguous or vague language Make the cause-and effect relationships clearer Inject more personality and warmth into this text Adjust the tone to be more persuasive and convincing Make this sound more professional and authoritative Rewrite this to more empathetic and understanding Make this sound more urgent and compelling Make this sound more objective and unbiased Adopt a more academic and scholarly tone Improve the transitions between the paragraphs Create more effective and engaging opening Clarify the main idea
Claude Sonnet 4
Claude Sonnet 4
Gemini 2.5 Pro
Gemini 2.5 Pro
Gemini 2.5 Pro
Gemini 2.5
Gemini 2.5 Pro
Gemini 2.5 Pro
Gemini 2.5 Pro
Gemini 2.5 Pro
Gemini 2.5 Pro
Gemini 2.5 Pro
Remove any filler words
Gemini 2.5 Pro
Remove any jargon or technical terms and explain them in plain language
Gemini 2.5 Pro
Replace complex words with simpler alternatives Identify and eliminate any redundant phrases or words Make this concrete and less abstract
Gemini 2.5 Pro
Gemini 2.5 Pro
Gemini 2.5 Pro
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Published as a conference paper at ICLR 2026
Editing Prompt
Contributor
Simplify this text for 5th-grade reading level Group related ideas together more effectively Ensure clear introduction, body, and conclusion Fix any grammatical mistakes in this text Write this in a way that a business person would get it Edit this to sound more polite Make this
Gemini 2.5 Pro
Gemini 2.5 Pro
Gemini 2.5 Pro
Gemini 2.5 Pro
Human
Human
descriptive Make this more detailed Please add more details make my argument better Rearrange this
Human
Human
Human
Human
Can you make this sound fluent?
Human
Rewrite to be more concise and powerful Can you help my essay get a better grade? Rewrite this Make my essay better Make this essay look better Can you improve this?
Human
Human
sounds good
Human
Human
Human
Human
Make this an A paper
Human
Remake all this in a different way
Human
Rewrite of this in different words
Human
Can you fix the problems with my argument?
Human
Emphasize the key points
Human
Make my grammar sound better
Human
VAL
Make this connect better with people's 's feelings Make this sound more like friendly conversation Make this casier for readers relate
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
to the topic get Make this sound more businesslike and serious Change
Make this casier for someone
ChatGPT 4o
ChatGPT 4o
sentences are written
ChatGPT 4o
Say this another way
CharGPT 4o
Include contextual information to support these claims Give more background so it's casier to understand Use creative comparisons make ideas clearer Make this easier to understand
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Use simpler language anyone can get Add better breaks and section titles Help the paragraphs connect better Remove anything confusing or unclear
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Make this sound more confident authoritative Adapt this for international readers
Claude Sonnet
Claude Sonnet 4
Translate this into more accessible language Find alternative ways to express these concepts Rework this using varied sentence structures Add atmospheric details create mood and setting Add environmental and contextual descriptions Restructure this emphasize the main points Connect these thoughts more seamlessly Flevate the quality of this writing
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet 4
Claudc Sonnet 4
Claude Sonnet 4
Claude Sonnet 4
Rewrite this more conversational and approachable style Rewrite this be direct and the point
Gemini 2.5
Gemini 2.5 Pro
Edit this into blog post I can share online Rewrite of this
Human
Human
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Published as a conference paper at ICLR 2026
Editing Prompt
Contributor
Proofread this for spelling and grammar errors
Human
TEST
Make this sound more formal school-like Make this nicer but keep the main point
ChatGPT 4o
ChatGPT 4o
Change this so people from other countries can get it too Add a short story to make this more interesting Show how this works in real life
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Say this in fewer words without losing meaning Edit this to be punchier and more direct Improve how sentences sound logether Refine the pacing and cadence of this paragraph Write better and more interesting beginning Make main idea clearer
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Improve this to make stronger and clearer Reword this to improve clarity while keeping the meaning Make sure there's beginning, middle, and end Group related ideas and use transitions to improve structure Adjust the tone to be more friendly Increase the emotional stakes Expand this text Show rather than tell by adding scene-setting details Include sounds, smells, textures, and other sensory elements Reduce wordiness while amplifying impact Make this text flow more naturally Rewrite this in different words while keeping the same meaning Lighten the tone and add touch of humor Make this longer with more evidence Simplify this text Make this more specific Write this a way that my teacher would get
ChatGT 4o
ChatGPT 4o
ChatGPT 4o
ChatGPT 4o
Claude Sonnet 4
Claude Sonnet 4
more specific examples and details
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Claude Sonnet
Claude Sonnet 4
Claude Sonnet 4
Gemini 2.5 Pro
Human
Human
Human
Human
Can you make my paper persuasive?
Human
Paraphrase this
Human
Can you fix any spelling, grammar, or punctuation issues?
Human
Train
Val
Test
All
Prompt Contributors
ChatGPT 4o
52.9% (128)
50.0% (15)
48.4% (15)
52.1% (158)
Claude Sonnet 4
31.4% (76)
33.3% (10)
25.8% (8)
31.0% (94)
Human
7.9% (19)
10.0% (3)
22.6% (7)
9.6% (29)
Gemini 2.5 Pro
7.9% (19)
6.7% (2)
3.2% (1)
7.3% (22)
TOTAL COUNTS
242
30
31
303
Table 10: Distribution of prompt categories and contributors across Train, Val, and Test splits shown as percentages with raw counts parentheses.
P LLM USAGE STATEMENT
Large Language Models (LLMs) were used in the experiments for the paper as described, to assist in writing the code the experiments, brainstorm the formalization of the task, assist in generating
25

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Published as a conference paper at ICLR 2026
Unique source texts
Train
22690
Validation
1391
Test
3809
Table 11: Count of unique source texts across Train, Val, and Test datasets.
Human
Al-edited
Al-generated
Total
Train 18810
22381
18809
60000
Validation
753
894
753
2400
Test 1881
2238
1881
6000
Table 12: Distribution of examples by label across Train, Val, and Test datasets.
Claude Sonnet 4
GPT-4.1
Gemini 2.5 Flash
Train
Al-edited
7528
7521
7332
Train
Al- Al-generated
6205
6243
6361
Validation
Al-cdited
298
298
298
Validation
Al-generated
256
235
262
Test
Al-edited
739
795
704
Test
Al-generated
627
627
Table 13: Distribution of source LLM for Al-edited and Al-generated examples, across Train, Val, and Test datasets.
Train
Validation
Test
Tone Style Adjustments
301
459
Adding Detail
Fluency and Flow
2302
Structure and Organization
2110
71
176
Concision
2037
76
301
Paraphrasing
1679
176
229
Clarity and Precision
1229
33
290
Grammar and Mechanics
400
27
70
General Improvement
2005
37
Table 14: Composition of the Al-edited dataset, by split
Mean Word Count
Min Word Count
Max Word Count
Train
Human
330.45
75
799
Train
Al-edited
328.11
75
799
Train
AT-generated
302.88
76
799
Validation
Human
331.02
75
798
Validation
Al-edited
324.29
75
799
Validation
A Al-generated
299.81
79
797
Test
Human
241.47
75
799
Test
Al-edited
258.37
798
Test
Al-generated
218.05
792
Table 15: Word count statistics across splits for Human, Al-edited, and Al-generated texts in the
dataset.
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Published as a conference paper at ICLR 2026
Table 16: Ternary Classification (Soft N- N-Grams)
Model
# buckets
params
Acc.
Macro
Human
AIFI
Al-edited FI
Sotral-Small-2455
-44--Base-251 Base-2501
24B
0.897
0.899
0.896
0.941
0.861
Mistral-Small--24 244B-Base-250
24B
0.873
0.876
0.913
0.875
0.840
Mistral-Smalll2244 2
24B
0.882
0,886
0.886
0.918
0.853
Mistral-Small-2445 -2B--BBs-20B0
Regression
24B
0.861
0.865
0.880
0.887
0.828
0.941
Llama-3,1-8B
8B
0.895
0.898
0.895
0.942
0.856
Llama-3.2-3B
3B
0.858
0.861
0.888
0.876
0.820
the figures for the paper, assist with LaTeX formatting, and review the paper to help the authors with constructive feedback. The authors did not use LLMs directly in the original writing of the manuscript, but did use LLMs to help with wording and phrasing some sections. The authors take full responsibility for the factuality and originality of content in this manuscript.
GRAMMARLY SUPERVISION SCORES
In Figures TO and we show the distributions of scores according different intermediate super- vision metrics by Grammarly edit prompt.
Fix any mistakes
OO
Paraphrase
Improve
Simplify it
Sound fluent
Make more descriptive
1000
Shorten it
000
Make it more detailed
10
Summarize it
0.00
0.2
0.4
0.8
1.0
Cosine Score
Figure 10: Distribution of cosine scores dataset from Grammarly by edit instruction.
Fix any mistakes Improve it
a
O0
Paraphrase it
Simplify it
Sound fluent
Shorten it
Make more descriptive
Summarize it
Make it more detailed
0.0
0.2
0.6
0.8
1.0
Soft N-Grams Score
Figure 11: Distribution of soft n-grams scores on dataset from Grammarly by edit instruction.
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Published as a conference paper at ICLR 2026
Table 17: Ternryy Classification (Cosine Similarity)
Model
buckets
params
Acc.
Maero Fl
Human Fl
AI F1
Al-edited F1
Mistrl--mall-2445 Ba00-BBs-- (selected)
24B
0.902
0.904
0.904
0.941
0.868
Mistral-Small-241 24---BB---B-a2011
5
24B
0,874
0.878
0.880
0,912
0,84]
Distrul-Small-2--BBBaaa Base-2501
24B
0.855
0.859
0.872
0.882
0.822
Mistral Hissral-Small--aa Small-24BB -oaall-24B-aaa Base-2501
Regression
24B
0.861
0.833
0.879
Misual Saaall-eeo-B-ssaae Base-2407
12B
0.889
0.896
0.900
0.939
0.845
Llama-3.1-88
8B
0.861
0.865
0.877
0.899
0.819
Llama-3.2-1 3.2-3B
0.855
0.878
Table 18: Binary Classification (Soft N-Grams)
Model
buckets
params
Human vs Rest Ace.
Human vs Rest Fl
vs Res Ace.
vs Rest
Mistral- Small-24BB 24B--Bse-B2BB0B0 Base-2501
24B
93,967
95.542
94,333
90.151
Mistral Small 240- Base-2501
24B
94,400
95.857
92.983
87,533
Mistral Small 240 Base-2501
24B
93.033
94.969
95.233
91,833
Mistral-Small Sma.l------BB----- 4BB-B0s--2501
Regression
24B
92.683
94.664
93.433
88.419
Histral-Nemo-Baste B4se-2407
12B
93,267
94.993
96.467
94.147
3.1-B
8B
93.033
94.780
96.483
94.157
Llama-3322 3.2-3B
3B
92.783
94.664
93.367
88.322
Table 19: Binary Classification (Cosine Similarity)
Model
# buckets
il params
Human Rest Acc.
Human vs Rest F1
Al ys Rest Acc.
Al vs Rest Fl
Mistral Mistral-Small Small 24B-Base 2501 (selected)
24B
93,783
95,377
96.433
94.062
Histral-Small-222 A4B--Baee2250
24B
92.583
94.608
94.367
91.170
Histral-Snnll-- 21B-Base-2200
24B
92.167
94.332
93.311
88.202
Siitra--Smal-24000
Regression
87.867
Mistrat-Nemo-Bare Base--
96.448
93.847
Llama-3.
92.017
94.055
94.183
89.869
1ama-3.2-30
3B
92.133
94.046
93.083
87.776
28


Reviews:
[{"review_id": "CJbp1FWERR", "reviewer": "Reviewer_yUTu", "summary": "This paper addresses the detection of AI-edited text, moving beyond simple classification to quantify the extent of AI involvement. The authors introduce EDITLENS, a regression model trained to predict a continuous score representing the magnitude of AI editing. To achieve this, they create a large-scale dataset of human texts edited by various LLMs and use lightweight similarity metrics, validated against human judgments, as supervision. Their model achieves state-of-the-art performance when adapted for both binary and ternary classification tasks and demonstrates a nuanced ability to track the intensity of AI polish.", "questions": "- Given the moderate agreement between your similarity metrics and human annotators, how can you be confident that the model is learning to detect stylistic cues of AI editing, rather than simply learning to approximate a text similarity function? What specific artifacts of AI editing do you believe the model is capturing that a generic similarity metric would not?\n\n- How would you expect EDITLENS to perform on a text written by a human and then heavily edited by another human (e.g., a student paper revised by a writing tutor)? Would the model assign a high AI-edit score in this case? Answering this seems crucial to understanding if the model is a true \"AI editor\" detector or a more general \"heavy revision\" detector.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "+ The paper addresses a highly relevant and timely problem. The shift from binary AI detection to quantifying the continuous spectrum of AI co-writing is a necessary and practical step forward for the field, with clear applications in areas like academic integrity.\n\n+ The creation and planned release of a large-scale dataset specifically for the task of detecting homogeneous mixed authorship is a significant contribution. This resource will undoubtedly enable further research in this under-explored area.\n\n+ The paper is exceptionally well-written, logically structured, and easy to follow. The figures are effective at illustrating the core concepts and the model's performance.", "weaknesses": "- The paper's fundamental limitation is that EDITLENS is not directly trained to detect the \"extent of AI editing,\" but rather to regress a similarity-driven target (e.g., cosine distance). The authors themselves report only \"moderate agreement\" (Krippendorff's α ≈ 0.67) between this proxy metric and human judgments. The model is learning to approximate a text similarity function, which may not capture the specific stylistic artifacts and semantic patterns characteristic of AI editing, but rather any significant textual change.\n\n- The study fails to include a crucial control experiment to disentangle the detection of \"AI editing\" from the detection of \"significant editing\" in general. The model is exclusively trained on human text edited by AI. A necessary test would be to evaluate its scores on human-written texts that have been substantially edited by humans.\n\n- While the problem formulation is a valuable contribution, the technical novelty of the proposed method is low. The approach consists of fine-tuning a standard language model with a regression head, a well-established technique. The success of EDITLENS is therefore entirely dependent on the quality of the supervision signal, which, as noted in the first point, is a flawed proxy for the actual phenomenon of interest.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PWhkV8Z9iW", "reviewer": "Reviewer_Ri7B", "summary": "This paper introduces EditLens, a novel approach for the quantification of the magnitude of AI editing present within a given text. To this aim, the authors first propose a set of similarity metrics to quantify the magnitude of AI editing in a text given the original, human-written one. Second, after validating these metrics via human annotators, the EditLens regression model is trained on such scores to predict the amount of AI editing in unseen texts. Experiments were performed using diverse models and datasets, and a case study on the Grammarly writing assistant is presented to further showcase the EditLens' capabilities.", "questions": "- Related to the first raised weakness, I wonder if the authors could experiment with similar \"attack\" scenarios (e.g., similar semantics with tangible edits), to better prove the robustness of the proposed approach. Furthermore, do results change when the encoder is replaced with a similar-performing one? This would better highlight any potential \"fitting\" of the model biases rather than actual editing.\n- I would like to ask the authors how robust EditLens to editing made by humans is. That is, a text that is rephrased by humans, comes with the risk of being flagged as AI edited? Could the authors provide some more insights into false positives?\n- Why is EditLens based on soft n-grams found to be better in the binary setting, whereas the cosine backbone performs better in the ternary classification?\n- Appendix N has a missing reference to the confusion matrix figure.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- This paper aims to address an important issue in current AI detection approaches, as simply stating AI vs Human-written is reductive and leads to a lot of false positives in current settings.\n- EditLens directly regresses the score corresponding to the degree of AI involvement in a text as a whole, rather than simply returning \"human-AI mixed\" as in the case of its competing methods.\n- Results seems to be promising compared to existing approaches, and the experimental setup covers multiple domain scenarios as well as LLMs.", "weaknesses": "- The proposed approach extensively leverages a set of intermediate supervision metrics to train EditLens. However, these represent a key weakness to me. Specifically, these metrics heavily rely on semantic (dis)similarity among the original human-authored texts and their AI-edited counterparts, being potentially misleading. In this regard, if an edit keeps the same semantic meaning of a text while changing multiple \"words\", the cosine distance would receive a lighter shift than expected. Note that similar considerations hold for the soft n-grams, since they still account for cosine similarity. Related to the previous point, EditLens is hence optimized to follow a concept of similarity learned by *Linq-Embed-Mistral* (which is in charge of producing embeddings), creating a sort of model-dependent similarity, which might include potential biases due to training data of the encoder, as well as capture stylistic variations rather than actual editing. \n- The human evaluation process is not sufficiently robust, as the authors relied on only three annotators and evaluated merely 100 tasks. Although the reported agreement is moderate, the small number of annotators and limited sample might impact the generalizability of the reported findings, e.g., due to annotator biases or sample variability.\n- EditLens is found to achieve strong correlation with edit magnitude metrics, but isn't it by construction? It is trained on edit metric signals, so this correlation should be expected rather than a finding.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AQ124Fvuqp", "reviewer": "Reviewer_K4W3", "summary": "The paper introduces a method to predict the extent to which text has been edited by AI. The method is given a piece of text, and must predict (without seein the original unedited source) the extent to which the text was generated by an LLM. This is possible due to the statistical distinctiveness of AI-text compared to human-written text, and is done by training a regression head on top of a transformer, to regress more embedding/n-gram based metrics, which are shown to correlate with expert opinion of AI-editing.\nExperimental results seem very strong, though they are presented in such a way that lacks clarity and makes the paper much less valuable in terms of scientific findings, which is unfortunate. But in short, the model can be used in both binary and ternary classification, and is shown to be robust to other kinds of editings that are not included in the training, giving a sense of generalization.", "questions": "1. The two proxy metrics used for training achieve similar (0.66-0.67) correlation with experts, how well do they correlate with each other. Is there a combination of both (say min(A,B), mean(A,B), max(A,B)) that correlates better with experts? Why do you keep both, I presume they each add something semantic to the problem. Can you comment on why both are needed?\n2. It seems like the threshold (based on Table 1) of EditLens on the binary tasks are still rather close to the extremes (0.04 and 0.96), when one could have expected that these would be closer to the mid-range? Can you comment on why that is observed? Does this mean that the community assumed that a 4% change to a piece of human-written text is the threshold at which there should be disclosure of AI-editing?\n3. I wonder to what extent the Prompt CLS helps. Does having a model know about categories of edits help it learn to regress, did you conduct any training ablations?\n4. Please see questions listed in the weaknesses. How would you modify the current findings /discussion description to improve the submission's narrative?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 2, "contribution": 4, "strengths": "- AI detection is an important topic, and the framing is novel and interesting, for example the introduced concepts of heterogeneous and homogeneous edited text, which expand prior simplistic views in the literature about text editing.\n- The method and use of data is quite clever and validated through an expert annotation.\n- The findings indicate that this approach is very strong, as it not only outperforms at the correlation task, but also at the more boxed \"classification\" tasks, through the selection of a threshold. It is an elegant finding that \"relaxing\" the problem helps improving performance on the initial problem.", "weaknesses": "Though I am very positive about the work overall, my score is only slightly positive for now. I would be willing to raise my score if the authors commit to improvements in the presentation of the work.\nA main limitation of the current submission is the extremely rushed description of results. To name only a few things: (1) APT-Eval is never explained or cited (?) leaving readers unclear of what it is and where it comes from, (2) in 4.3 you mention a \"calibration procedure above\" that is I believe never defined, (3) Table 2 is never described (?), (4) most results are described in a way that is descriptive but not interpretative: you need to help the reader understand the result and why it is important, not just what it is.\nThis generally really lowers the value of the submission as is, as a reader can tell that the method is promising (clearly), but is not clear about when it shines and when it doesn't and what leads to the sucess.\n\nI highly recommend picking a subset of the presented findings and going over them adequately, rather than rushing through all of them at breakneck speed.\n\n2. I would also encourage the authors to reflect on the work and describe more scientifically where the method still lags, or where there is still room for improvement. As is, all results describe the EditLens method as \"solving\" the task to the best extent, which is wonderful, but leaves the reader not knowing what remains unsolved. As the authors of this work, you have this perspective from your deep involvement in this project, and scientific communication requires you to help put the work in perspective. A good paper should not only report promising scientific findings, but also help guide the field forward from there.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lNjoHLGFiI", "reviewer": "Reviewer_BnBc", "summary": "This paper introduces EditLens, a detector that estimates the degree of AI editing in a text under homogeneous mixed authorship, where human and AI contributions are entangled at the token level—rather than simply classifying text as human- or AI-written. The task is formalized as regressing a change-magnitude score from the edited text alone, without access to the original. Using cosine distance of sentence embeddings and a \"soft n-grams\" precision metric—validated against human judgments—as supervision, EditLens is trained on a synthetic dataset of human texts and their AI-edited variants generated via diverse prompts and LLMs. The model fine-tunes open LLM backbones with QLoRA using a multi-task objective. EditLens achieves strong performance as a classifier and produces monotonic scores with increasing edit intensity. It generalizes well to unseen LLMs and domains, and a Grammarly case study shows instruction-specific edit distributions. Models and data will be released.", "questions": "- Beyond pairwise \"which is more AI-edited,\" can you provide an absolute magnitude annotation (e.g., 5- or 10-point scale) with more annotators, report inter-rater reliability, and correlate EditLens with that scale?\n- How does EditLens fare under paraphrasers/back-translation/\"humanizers\" and multi-pass mixed human↔AI edits beyond five steps? Any detection degradation curves?\n- Since the supervision labels rely on soft n-gram precision and embedding similarity, how sensitive are the results to different choices of parameters (a,b,τ) or sentence encoders? A brief ablation or robustness analysis would help confirm that the reported improvements are not specific to one proxy configuration.\n- Does EditLens generalize to the reverse case where humans edit AI-written drafts? If not, do the authors anticipate differences in the learned signal or dataset construction for such cases?\n\nIf the authors can substantively address the questions and weaknesses outlined above, I would be inclined to raise my score.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Strong and timely motivation. The paper identifies a real and underexplored gap in AI text detection, i.e., the prevalence of heterogeneous, mixed authorship rather than purely human or AI-generated content. By shifting from binary classification to a continuous estimation of edit intensity, it tackles a far more realistic and societally relevant problem.\n- The curated dataset of human texts paired with systematically AI-edited variants offers a rich benchmark for studying varying degrees of human-AI co-authorship. Its structure provides a foundation for future research.\n- EditLens demonstrates monotonic response to increasing AI involvement, strong macro-F1 across binary and ternary settings, and clear ablation studies.\n- OOD tests and the Grammarly case study provide useful insights into how edit types and prompt categories affect the model's judgments.", "weaknesses": "- The dataset is generated exclusively via AI editing human-written text, capturing only one direction of authorship interaction. Yet heterogeneous mixed authorship fundamentally refers to cases where both humans and AI make substantial contributions to the same text. In many real-world workflows, such as researchers revising AI-generated drafts, the reverse direction (human editing AI text) is equally common and important.\n- Limited human study scope and unclear methodology. Only three annotators assessed 100 samples using pairwise/tie protocols, without inter-annotator error analysis. Moreover, the paper does not clearly describe the annotation process, such as annotator recruitment or instructions, which limits the reproducibility and interpretability of the results.\n- The supervision target for EditLens is derived from embedding and n-gram similarity metrics, which serve only as proxies for the true extent of AI editing. As these same metrics are also used for evaluation, the model may appear to perform well simply because it learns to reproduce the proxy rather than measure real editing strength. A clearer human-grounded calibration or validation study with more annotators would make the construct more reliable and interpretable.\n- While the paper is generally clear, some cross-references are vague and hinder readability. For instance, in about line 206, the authors write \"see the Appendix\", but do not specify which appendix section actually contains it. Such vague pointers make it difficult for readers to locate the intended material, especially given the length of the supplementary text.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)