Cross-Topic Authorship Verification: Methods, Challenges, and Applications for Robust AI Detection

Abstract

This article provides a comprehensive analysis of modern methods for cross-topic authorship verification (AV), the task of determining whether two texts on different subjects were written by the same author. Aimed at researchers and professionals in computational linguistics and forensic analysis, we explore the foundational paradigms distinguishing authorship attribution from verification, detail advanced methodologies combining stylistic and semantic features, address critical challenges like topic leakage and evaluation instability, and validate approaches through emerging benchmarks and metrics. The synthesis offers a roadmap for developing robust, transparent AV systems capable of reliable performance in real-world, topic-shifted scenarios, with implications for AI-generated text detection and content authentication.

Defining the Paradigm: From Authorship Attribution to Verification in Cross-Topic Analysis

Within computational stylometry, Authorship Attribution (AA) and Authorship Verification (AV) represent two distinct core tasks [1]. The broader thesis of this research focuses on advancing methods for cross-topic authorship verification, a challenging sub-field where models must identify authors based on writing style alone, independent of semantic content. A clear understanding of the task definitions, comparative performance, and appropriate experimental protocols is fundamental to this pursuit. This document provides detailed application notes and protocols to guide researchers in this domain.

Task Definitions and Conceptual Workflows

Core Definitions

• Authorship Attribution (AA): A multi-class classification task where, given a text of unknown authorship and a set of candidate authors, the goal is to identify the most likely author from the candidate set [2] [3]. It can be further divided into closed-set (the author is assumed to be in the candidate set) and open-set (the author may be outside the candidate set) scenarios [2].
• Authorship Verification (AV): A binary classification task that determines whether a given text was written by a specific candidate author [4] [2]. In its most common and challenging form, it is framed as a symmetric task: given two texts, decide if they are by the same author or by different authors [2].

Logical Relationship and Research Workflow

The diagram below illustrates the conceptual relationship between AA and AV, and a general research workflow for cross-topic authorship verification.

Empirical Performance Comparison

A clear understanding of the performance landscape of different methods on AA and AV tasks is crucial for selecting and developing robust models, especially in cross-topic scenarios.

Comparative Performance of AA and AV Methods

Table 1: Empirical performance of various methods on Authorship Attribution (AA) and Authorship Verification (AV) tasks across different datasets. Macro-Accuracy is reported for AA; AV performance varies by evaluation setup.

Method Category	Specific Model	Task	Performance	Key Findings & Context
Traditional N-gram	Character N-gram Model	AA	76.50% (Avg. Macro-Accuracy)	Outperformed BERT on 5 of 7 AA tasks in a large-scale benchmark [5].
Pre-trained Transformer	BERT-based Model	AA	66.71% (Avg. Macro-Accuracy)	Performance was superior on AA datasets with the greatest number of words per author [5].
Pre-trained Transformer	BERT-like Models	AV	Competitive with SOTA	Effective as competitive baselines for AV, but found to be biased towards named entities [6].
Feature-Ensemble	RoBERTa + Stylistic Features	AV	Consistent Improvement	Incorporating style features (sentence length, punctuation) consistently boosted performance over semantic embeddings alone [4].
LLM-based (Zero-shot)	OSST (LLM Log-Prob.)	AA & AV	High Accuracy	Achieved higher accuracy than contrastive baselines when controlling for topical correlations; performance scales with model size [2].

Protocol for Benchmarking Cross-Topic Generalization

A critical protocol for cross-topic authorship verification research involves creating dataset splits that explicitly control for and isolate topic bias.

• Objective: To evaluate whether an AV model genuinely relies on stylistic features rather than topical cues.
• Procedure:
  • Dataset Selection: Utilize datasets where authors have written on multiple, distinct topics. The PAN dataset is a common choice [6].
  • Data Splitting: Create public splits designed to isolate topic bias. This involves:
    • Training Set: Contains pairs of texts from the same author and different authors, covering a set of topics.
    • Test Set: Contains text pairs that involve entirely new, unseen topics not present in the training set [6].
  • Biased Feature Ablation: To test for reliance on named entities (a common topical bias), train and evaluate a model variant on a version of the dataset where all named entities have been removed [6].
  • Evaluation: Compare model performance (e.g., F1-score, AUC) on the standard test set versus the topic-controlled and bias-ablated test sets. A significant drop in performance on the latter indicates sensitivity to topic/style confounding.

Detailed Experimental Protocol for Authorship Verification

The following workflow details the key steps for establishing a robust Authorship Verification protocol, with particular emphasis on challenges specific to cross-topic research.

The Scientist's Toolkit: Research Reagents for AV

Table 2: Essential "research reagents" – datasets, features, and models – for conducting cross-topic authorship verification research.

Category	Item	Function & Application Notes
Datasets	PAN AV Datasets	Standardized benchmarks for AV, often based on fanfiction or mixed genres (essays, emails). Provide training/test splits and enable cross-topic evaluation [6] [2].
	Project Gutenberg	Large-scale corpus of literary works. Useful for pre-training or creating new large-scale benchmarks to study authorial style in long texts [5].
	DarkReddit / VeriDark	Challenging datasets from online forums representing adversarial or real-world conditions. Test model robustness and generalization [6].
Feature Sets	Stylometric Features	Function: Capture statistical style markers (sentence length, punctuation, word frequency). Note: Crucial for cross-topic robustness; combined with semantic features they boost performance [4] [1].
	Pre-trained Embeddings (RoBERTa)	Function: Provide deep, contextual semantic representations of text. Note: Can introduce topic bias; must be used with topic-controlled splits [4] [6].
	LLM Log-Probs (OSST)	Function: Measure style transferability in a zero-shot setting using LLM log-probabilities. Note: Effective for controlling topical correlations [2].
Models	Siamese Networks	Function: Learn a metric space where texts by the same author are close. Application: Well-suited for the pairwise nature of AV tasks [4].
	BERT-based Baselines	Function: Fine-tuned transformer models for AV. Application: Competitive baselines; require bias mitigation (e.g., named entity removal) for cross-topic generalization [6].
Evaluation	Topic-Controlled Splits	Function: Isolate the effect of topic during evaluation. Application: The definitive test for assessing genuine style-based recognition in cross-topic AV [6].
SIQ17	SIQ17, MF:C32H27NO2S, MW:489.6 g/mol	Chemical Reagent
MEDS433	MEDS433, MF:C20H11F4N3O2, MW:401.3 g/mol	Chemical Reagent

A meticulous approach to task definition, dataset construction, and feature engineering is paramount for success in cross-topic authorship verification. The empirical evidence indicates that no single method dominates all scenarios; traditional models like N-grams can be remarkably effective for AA, while transformer-based and feature-ensemble models show strong performance in AV, provided topical biases are rigorously controlled. The future of robust, cross-topic AV research lies in the development of methods that can explicitly disentangle style from semantic content, leveraging both classical stylometric features and the emerging capabilities of large language models.

The Critical Challenge of Topic Shift in Real-World Applications

Application Note: Quantifying Topic Shift in Authorship Verification

Topic shift presents a fundamental challenge in real-world authorship verification (AV), where models trained on texts from specific domains often fail to generalize to new topics. This application note examines the performance degradation caused by topic shift and outlines protocols for developing robust, cross-topic AV systems. The instability arises when models learn topic-dependent features instead of genuine, topic-agnostic authorial fingerprints, compromising their utility in practical applications such as academic integrity checks, forensic analysis, and intellectual property protection [7].

Quantitative Analysis of Cross-Topic Performance

Evaluation using the Million Authors Corpus () demonstrates significant performance variations when models are tested across different Wikipedia domains, highlighting the topic shift problem. The following table summarizes key dataset characteristics and baseline performance metrics [7].

Table 1: Million Authors Corpus () Characteristics and Cross-Topic Performance Baselines

Metric	Value	Description / Implication
Total Textual Chunks	60.08 Million	Scale enables robust, large-scale evaluation [7]
Unique Authors	1.29 Million	Represents a diverse set of writing styles [7]
Language Coverage	Dozens	Enables cross-lingual analysis alongside cross-topic study [7]
Key Cross-Topic Finding	Performance Variance	Model accuracy decreases when topic differs between training and test texts, confirming topic shift sensitivity [7]
Primary Data Source	Wikipedia Edits	Provides natural, long-form textual chunks from diverse domains (e.g., arts, sciences, history) [7]

Experimental Protocol for Cross-Topic Authorship Verification

This protocol provides a standardized methodology for evaluating the resilience of AV models to topic shift.

Protocol 1: Cross-Topic Model Evaluation

Objective: To assess the impact of topic shift on AV model performance and determine the model's reliance on topical features. Materials:

• Computing environment with appropriate ML frameworks (e.g., Python, PyTorch/TensorFlow).
• The Million Authors Corpus () or a comparable cross-domain dataset [7].
• State-of-the-art AV models (e.g., transformer-based architectures).

Procedure:

• Data Partitioning: Segment the dataset into non-overlapping topic-based folds (e.g., "Science," "History," "Arts") based on metadata or source domain.
• Model Training: a. Train the candidate AV model on textual chunks from a limited set of topics (e.g., only "Science" and "History"). b. Implement and monitor standard training procedures, including loss convergence.
• Cross-Topic Testing: Evaluate the trained model on a held-out test set composed exclusively of texts from unseen topics (e.g., "Arts").
• In-Topic Control Testing: For comparison, evaluate the same model on a test set from seen topics (i.e., "Science" and "History").
• Performance Metric Calculation: Compute standard AV metrics (Accuracy, F1-score, AUC-ROC) for both the cross-topic and in-topic test scenarios.
• Analysis: Compare performance metrics. A significant drop in cross-topic performance versus in-topic performance indicates high model sensitivity to topic shift.

Advanced Protocol: Topic-Agnostic Feature Learning

This protocol describes an experimental setup to train models that are explicitly invariant to topic.

Protocol 2: Learning Topic-Invariant Author Representations

Objective: To train an AV model that relies on stylistic features rather than topical content. Materials:

• Same as Protocol 1.
• Feature extraction tools (e.g., for syntactic or lexical features).

Procedure:

• Adversarial Training Setup: a. Design a model with a shared feature extractor, followed by two classifiers: an Author Classifier (primary task) and a Topic Classifier (adversarial task). b. The shared feature extractor and Author Classifier are trained to minimize author identification error. c. Simultaneously, the shared feature extractor is trained to maximize topic classification error (making features uninformative for topic prediction), while the Topic Classifier is trained to minimize it.
• Training Loop: Iterate until convergence, fostering the development of features that are discriminative for authorship but indiscriminative for topic.
• Evaluation: Follow Protocol 1 to evaluate the adversarially trained model against a baseline model, measuring the reduction in cross-topic performance degradation.

Visualization of Workflows and Relationships

Cross-Topic Authorship Verification Workflow

This diagram outlines the core experimental workflow for evaluating topic shift, as described in Protocol 1.

Adversarial Training for Topic Invariance

This diagram illustrates the architecture for learning topic-agnostic author representations, as outlined in Protocol 2.

Feature Analysis in Cross-Topic Scenarios

This diagram conceptualizes the ideal feature space for a robust authorship verification model.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials and Resources for Cross-Topic Authorship Verification Research

Item Name	Function / Description	Specifications / Notes
Million Authors Corpus ()	A cross-lingual and cross-domain Wikipedia dataset for training and evaluating AV models. Provides long, contiguous textual chunks linked to authors across dozens of languages and topics [7].	Contains 60.08M textual chunks from 1.29M authors. Essential for ablation studies on topic and language generalization [7].
Pre-trained Language Models (PLMs)	Foundation models (e.g., BERT, RoBERTa) used for feature extraction or fine-tuning. Capture deep linguistic patterns beyond simple bag-of-words features.	Choosing a model with strong multilingual capabilities is beneficial for generalizability.
Stylometric Feature Extractor	Software library to compute traditional stylometric features (e.g., syntactic patterns, character n-grams, vocabulary richness).	Provides a baseline feature set. Useful for comparing deep learning models with traditional methods.
Adversarial Training Framework	A machine learning framework (e.g., PyTorch, TensorFlow) configured with gradient reversal layers or other adversarial components.	Enables the implementation of Protocol 2 for learning topic-invariant author representations.
Vector Similarity Search Index	A high-performance database (e.g., FAISS) for efficient nearest-neighbor search in high-dimensional feature spaces.	Critical for scaling verification tasks to millions of authors by quickly comparing a query text against a gallery of known author profiles.
A4333	A4333, MF:C28H26F6N6O4S, MW:656.6 g/mol	Chemical Reagent
(Rac)-Sclerone	(Rac)-Sclerone, CAS:19638-58-5, MF:C10H10O3, MW:178.18 g/mol	Chemical Reagent

Application Notes

Background and Significance

In cross-topic authorship verification (AV), the primary objective is to determine whether two texts share the same author based on stylistic cues, independent of their topical content. The core challenge, and the central focus of these application notes, is topic leakage—the unintended overlap of topical information between training and test datasets. This leakage provides models with a superficial shortcut, allowing them to make decisions based on topic similarity rather than genuine stylistic features. Consequently, model performance appears inflated during evaluation, but this performance is not robust and fails to generalize to genuine cross-topic scenarios where the topics of compared documents are truly distinct. This phenomenon directly undermines the evaluation of an AV model's robustness against topic shifts [8] [9].

The conventional cross-topic evaluation paradigm assumes minimal topic overlap. However, even with careful data splits, residual topic leakage can occur, leading to two primary consequences:

• Misleading Model Performance: Evaluations reflect a model's ability to detect topic matches rather than its capacity to identify invariant authorial style, resulting in over-optimistic performance metrics.
• Unstable Model Rankings: The relative ranking of different AV models becomes highly sensitive to the specific random seed or data split used for evaluation, as the degree of topic leakage varies across splits. This instability makes it difficult to identify the truly best-performing model [8].

Quantitative Evidence of Topic Leakage Effects

The following table summarizes empirical findings on the impact of topic leakage and the effect of the proposed mitigation, Heterogeneity-Informed Topic Sampling (HITS).

Table 1: Impact of Topic Leakage and HITS Mitigation on Model Evaluation

Evaluation Condition	Key Metric	Observation / Finding	Interpretation
Standard Cross-Topic Evaluation	Model Performance (e.g., AUC)	Inflated and misleadingly high scores	Models exploit topic shortcuts, not stylistic features.
	Model Ranking Stability (Kendall's Tau variance across splits)	High variance (e.g., ~0.45)	Model rankings are unstable and dependent on the specific data split.
Evaluation with HITS-Sampled Dataset	Model Performance	Reflects genuine cross-topic performance	Topic shortcuts are minimized, forcing models to rely on style.
	Model Ranking Stability (Kendall's Tau variance across splits)	Low variance (e.g., ~0.10)	HITS produces a more stable and reliable ranking of models [8].
Topic Shortcut Test (in RAVEN benchmark)	Performance on "Same-Topic" vs "Different-Topic" pairs	Significant performance drop on "Different-Topic" pairs	Quantifies a model's over-reliance on topic-specific features [8].

Protocols

Protocol: Implementing Heterogeneity-Informed Topic Sampling (HITS)

Purpose: To construct an evaluation dataset with a heterogeneous topic distribution that minimizes the effects of topic leakage, thereby enabling a more robust and stable assessment of authorship verification models.

Primary Applications:

• Creating robust evaluation splits for benchmarking AV models.
• Diagnosing a model's reliance on topic-specific features.

Research Reagent Solutions

Table 2: Essential Materials for HITS Implementation

Item / Reagent	Function / Explanation
Text Corpus	The raw collection of documents from multiple authors and topics. Provides the base data for sampling.
Topic Model	An algorithm (e.g., LDA, BERTopic) to infer the latent topic distribution of each document. Essential for quantifying topic leakage.
HITS Algorithm	The core sampling logic that selects documents to maximize topic heterogeneity within the test set.
RAVEN Benchmark	The Robust Authorship Verification bENchmark, which incorporates HITS and provides a topic shortcut test [8].

Procedure:

• Topic Modeling:
  • Apply a topic modeling algorithm (e.g., LDA) to the entire text corpus.
  • For each document, obtain a topic probability vector representing its distribution over the identified topics.

• Initial Split (Optional):

  • Perform a conventional random split of the data into training, validation, and test pools, ensuring no author overlap between splits. The goal of HITS is to refine the test set from its pool.

• HITS Sampling for Test Set Construction:

  • From the test pool of documents, apply the HITS algorithm, which operates as follows: a. Calculate Topic Heterogeneity: For a candidate set of documents, compute the heterogeneity based on the diversity of its topic distribution. b. Greedy Selection: Iteratively select documents to add to the final test set. The selection criterion is to maximize the increase in overall topic heterogeneity of the test set. c. Stratification: Ensure that the selection process maintains a balance of positive (same-author) and negative (different-author) document pairs.
  • This results in a smaller, but more heterogeneously distributed, test dataset.

• Evaluation:

  • Train your AV models on the training set.
  • Evaluate the models on the HITS-sampled test set.
  • To assess stability, repeat the HITS sampling process with different random seeds and compare the ranking of models across these different evaluation splits.

Protocol: Conducting a Topic Shortcut Test Using the RAVEN Benchmark

Purpose: To diagnostically evaluate and quantify the degree to which an authorship verification model relies on topic-specific features versus genuine stylistic features.

Procedure:

• Dataset Acquisition:
  • Utilize the RAVEN benchmark, which is specifically designed to include a "topic shortcut" test [8].

• Test Set Segmentation:

  • Segment the test pairs in RAVEN into two distinct categories:
    • Same-Topic Pairs: Document pairs that discuss the same or highly similar topics.
    • Different-Topic Pairs: Document pairs that discuss distinct and unrelated topics.

• Model Inference:

  • Run the trained AV model on both subsets of the test pairs (Same-Topic and Different-Topic).

• Performance Comparison:

  • Calculate standard performance metrics (e.g., AUC, F1 score) separately for the Same-Topic pairs and the Different-Topic pairs.
  • Compare the results. A significant performance drop on the Different-Topic pairs indicates a high reliance on topic features and poor generalization of stylistic features.

Visualizations

HITS Sampling Workflow

Topic Shortcut Test Logic

The reliable verification of an author's identity based solely on textual content is a critical challenge in natural language processing (NLP). Authorship verification (AV) serves as a fundamental task for applications ranging from identity confirmation and plagiarism detection to the identification of AI-generated text [7]. A significant limitation in current AV research is the predominance of models trained and evaluated on single-domain, primarily English, datasets. This limitation can lead to overly optimistic performance assessments, as models may inadvertently rely on topic-based features rather than authentic, author-specific stylistic signatures [7]. This document presents a detailed set of application notes and protocols for analyzing the foundational features of writing—style, sentence structure, and vocabulary—within the context of cross-topic authorship verification research. The methodologies outlined herein are designed to enable robust and generalizable AV models that perform reliably across diverse domains and languages.

Foundational Feature Categories and Quantitative Metrics

The analysis of authorship relies on quantifying an author's unique, subconscious writing habits. These features are typically categorized and measured as shown in the table below.

Table 1: Quantitative Metrics for Foundational Authorship Features

Feature Category	Specific Metric	Description	Measurement Method
Lexical (Vocabulary)	Type-Token Ratio (TTR)	Measures vocabulary richness and diversity.	Total Unique Words / Total Words
	Honore's Statistic	Another measure of vocabulary richness, more sensitive to hapax legomena.	R = (100 * log(N)) / (1 - (V1/V)) where V=unique words, V1=words used once, N=total words
Syntactic (Sentence Structure)	Average Sentence Length	Mean number of words per sentence.	Total Words / Total Sentences
	Punctuation Frequency	Frequency of commas, semicolons, and other punctuation marks.	Count of Punctuation Mark / Total Words
	Sentence Structure Complexity	Ratio of complex sentences to simple sentences.	Number of Complex Sentences / Total Sentences
Stylometric (Writing Style)	Word Length Distribution	Mean and distribution of characters per word.	Average Characters per Word
	Function Word Frequency	Usage frequency of common, topic-independent words (e.g., "the", "and", "of").	Count of Specific Function Word / Total Words
	Character-Level n-grams	Frequency sequences of 'n' characters, capturing sub-word patterns.	Count of Specific n-gram / Total n-grams

Experimental Protocols for Feature Extraction and Model Training

Protocol A: Data Preprocessing and Feature Extraction

This protocol details the steps to prepare textual data and extract the quantitative features listed in Table 1.

1. Research Reagent Solutions

Table 2: Essential Materials and Tools for Feature Extraction

Item Name	Function/Explanation
Million Authors Corpus ()	A cross-lingual, cross-domain Wikipedia dataset with 60.08M textual chunks from 1.29M authors, ideal for training and evaluating generalizable AV models [7].
Raw Text Data	The corpus of documents or text chunks from known authors for analysis.
Linguistic Preprocessing Pipeline	A software pipeline for tokenization, sentence splitting, and part-of-speech tagging (e.g., using spaCy, NLTK).
Feature Extraction Scripts	Custom scripts (e.g., in Python) to calculate metrics from Table 1 from the processed text.
Statistical Analysis Software	Environment for statistical analysis and model training (e.g., Python with Pandas, Scikit-learn).

2. Procedure

• Step 1: Data Acquisition and Cleaning. Obtain the dataset. Remove extraneous markup, headers, and footers. For cross-topic verification, ensure the dataset includes writings from the same author on multiple distinct topics [7].
• Step 2: Text Normalization. Convert all text to lowercase to ensure case-insensitive analysis. Optionally, correct for common spelling variations to reduce noise.
• Step 3: Linguistic Preprocessing. Use the linguistic pipeline to split text into sentences and tokens (words/punctuation). Tag parts-of-speech if function word analysis is required.
• Step 4: Feature Calculation. Implement and run feature extraction scripts to compute all target metrics (e.g., TTR, average sentence length, punctuation frequency) for each document or text sample.
• Step 5: Data Structuring. Compile all extracted features into a structured data table (e.g., a CSV file or Pandas DataFrame) where rows represent documents and columns represent the calculated features. This table is the input for model training.

Protocol B: Model Training and Evaluation for Authorship Verification

This protocol outlines the methodology for training AV models that leverage semantic and stylistic features, following state-of-the-art approaches [4].

1. Research Reagent Solutions

Table 3: Essential Materials and Tools for Model Training

Item Name	Function/Explanation
Feature-Enriched Dataset	The structured data table output from Protocol A.
Pre-trained Language Model (RoBERTa)	Generates high-quality contextual embeddings to capture semantic content of the text [4].
Deep Learning Framework	Software like PyTorch or TensorFlow for implementing and training neural networks.
Stylometric Feature Set	The hand-crafted stylistic features (from Table 1) such as sentence length and punctuation frequency [4].
Model Architectures	Frameworks for combining features, such as Siamese Networks or Feature Interaction Networks [4].

2. Procedure

• Step 1: Semantic Embedding Generation. For each text sample, use a pre-trained RoBERTa model to generate a dense vector embedding that represents its semantic meaning [4].
• Step 2: Stylometric Feature Vector Creation. Use the structured feature table from Protocol A to form a separate vector of stylistic features for each text sample.
• Step 3: Feature Fusion. Design a model architecture to combine semantic and stylistic features. The research indicates several effective approaches [4]:
  • Feature Interaction Network: Creates interactions between semantic and style features before making a decision.
  • Pairwise Concatenation Network: Concatenates the features from two texts for a direct comparison.
  • Siamese Network: Uses two identical subnetworks to process each text, comparing their resulting representations.
• Step 4: Model Training. Train the selected model on a dataset of text pairs labeled as "same author" or "different author." The model learns to minimize the verification error on this training data.
• Step 5: Cross-Topic Evaluation. Critically evaluate the model's performance on a held-out test set where the topics of text pairs are different from those in the training set and from each other. This assesses the model's reliance on genuine authorship style versus topic-specific features [7] [4].

Workflow Visualization and Signaling Pathways

The following diagram illustrates the complete experimental workflow for cross-topic authorship verification, from data preparation to model evaluation.

Building Robust Verification Systems: Techniques and Architectural Designs

In the domain of cross-topic authorship verification, the fundamental challenge lies in distinguishing an author's unique writing style from the semantic content of their writing. This task becomes particularly difficult when comparing texts on different subjects, where topic-related features can dominate and obscure the subtle stylistic patterns that identify an author. Feature engineering—the process of creating and selecting optimal feature sets—has emerged as a critical solution to this problem. By strategically combining semantic embeddings with handcrafted stylometric features, researchers can develop more robust models that maintain verification accuracy across diverse topics [4] [10]. This approach leverages the complementary strengths of both feature types: semantic embeddings capture contextual meaning and topic information, while stylometric features quantify surface-level and syntactic patterns that are more topic-independent. The integration of these disparate feature types enables models to focus on the writer's unique stylistic fingerprint rather than being misled by content similarities or differences.

Theoretical Foundation

The Style-Content Entanglement Problem

The core challenge in authorship verification is the inherent entanglement of style and content in written text. Authors frequently write about similar topics, creating a spurious correlation that neural networks can easily exploit as a shortcut learning mechanism. This phenomenon, known as Style-Content Entanglement, becomes particularly problematic in cross-topic verification scenarios where the model must recognize the same author across different subjects [10]. When authors write about the same topic, the model may use topic-related features rather than genuine stylistic patterns for identification, leading to poor generalization when those topic patterns change. This entanglement manifests in the embedding spaces of pre-trained language models, where style and content subspaces overlap, making it difficult to isolate purely stylistic representations.

Semantic Embeddings for Content Representation

Semantic embeddings generated by transformer-based language models like RoBERTa and BERT provide dense vector representations that capture deep contextual meaning and linguistic relationships within text [4] [10]. These embeddings are typically obtained from the final hidden layers of models pre-trained on massive corpora using objectives like Masked Language Modeling. The resulting representations encode rich information about vocabulary usage, conceptual relationships, and syntactic structures that reflect the semantic content of text. However, because these models are primarily trained for content understanding, their embeddings naturally reflect topic information that can interfere with style-based authorship verification, particularly in cross-topic scenarios.

Stylometric Features for Style Representation

Stylometric features provide quantitative measures of writing style that are theoretically more independent of content. These features can be categorized into several distinct types:

• Lexical features: Include character n-grams, word frequency distributions, vocabulary richness, and word length distributions that capture patterns in word usage [10] [11].
• Syntactic features: Encompass punctuation patterns, part-of-speech tag frequencies, sentence structure complexity, and function word usage that reflect grammatical preferences [4] [10].
• Structural features: Comprise sentence length statistics, paragraph organization, document structure, and formatting conventions that capture higher-level organizational patterns [10] [11].
• Application-specific features: May include specialized metrics tailored to particular domains or writing contexts.

Unlike semantic embeddings, these handcrafted features are designed to target specific aspects of writing style that remain consistent across different topics and contexts.

Feature Engineering Approaches

Feature Taxonomies and Properties

The table below provides a comprehensive classification of features used in authorship verification systems, their representations, and their relative robustness to topic variation:

Table 1: Taxonomy of Features for Authorship Verification

Feature Category	Specific Features	Representation Format	Topic Robustness	Primary Strengths
Semantic Embeddings	RoBERTa outputs, BERT embeddings, Transformer hidden states	Dense vectors (768-1024 dimensions)	Low to Medium	Captures deep contextual relationships and nuanced meaning
Lexical Features	Character n-grams, word frequencies, vocabulary richness	Sparse vectors (TF-IDF, frequency counts)	Medium	Quantifies habitual word choices and spelling patterns
Syntactic Features	Punctuation frequency, POS tag patterns, function word ratios	Statistical vectors (frequencies, ratios)	High	Reflects grammatical habits and sentence construction
Structural Features	Sentence length, paragraph length, text organization	Numerical statistics (mean, variance, counts)	High	Captures organizational preferences and formatting habits

Quantitative Comparison of Feature Performance

Recent research has provided quantitative evidence for the performance characteristics of different feature types in authorship verification tasks. The following table summarizes key findings from empirical evaluations:

Table 2: Performance Comparison of Feature Types in Authorship Verification

Feature Type	Model Architecture	Dataset	Accuracy	Cross-Topic Robustness
Semantic Only	RoBERTa-based	PAN dataset	72-76%	Low to Medium
Stylometric Only	TF-IDF + Traditional ML	PAN dataset	65-70%	Medium
Combined Features	Feature Interaction Network	PAN dataset	80-85%	High
Disentangled Representations	Contrastive Learning with Hard Negatives	Diverse authorship corpus	Up to 10% improvement in hard cases	Very High

The data clearly demonstrates that combining feature types yields significant improvements over either approach in isolation, with particularly notable gains in challenging cross-topic scenarios [4] [10]. The performance advantage stems from the complementary nature of these features: while semantic embeddings capture broad contextual patterns, stylometric features provide specific, topic-agnostic signals that remain stable across different writing subjects.

Experimental Protocols and Methodologies

Protocol 1: Feature Interaction Network

Objective: Implement a neural architecture that explicitly models interactions between semantic and stylometric features for improved authorship verification.

Materials and Reagents:

• Text Corpus: PAN authorship verification dataset or custom collection with multiple authors and topics [4] [12]
• Pre-trained Models: RoBERTa-base or similar transformer model for semantic embedding extraction
• Computational Environment: Python with PyTorch/TensorFlow, transformers library, scikit-learn
• Feature Extraction Tools: NLTK or spaCy for syntactic parsing, custom functions for structural features

Procedure:

• Data Preprocessing:
  • Clean and tokenize text using appropriate tokenizers for the target language
  • Segment longer documents into consistent-length passages (e.g., 512 tokens)
  • Annotate texts with metadata including author labels and topic categories

• Feature Extraction:

  • Extract semantic embeddings using the final hidden layer of RoBERTa (768-dimensional vectors)
  • Compute stylometric features including:
    • Sentence length statistics (mean, variance, maximum)
    • Punctuation frequency counts for 15+ punctuation types
    • Word-level features (word length distribution, vocabulary richness)
    • Syntactic features (POS tag frequencies, function word ratios)

• Feature Integration:

  • Implement parallel neural pathways for semantic and stylometric features
  • Project both feature types into a shared dimensional space (e.g., 256 dimensions)
  • Apply cross-attention mechanisms to model feature interactions
  • Concatenate transformed features for final verification decision

• Model Training:

  • Use binary cross-entropy loss for same-author/different-author classification
  • Employ Adam optimizer with learning rate 1e-5
  • Implement early stopping based on validation performance
  • Apply regularization techniques to prevent overfitting

Validation Method: Cross-validation with topic-stratified splits to ensure evaluation across unseen topics [12]

Protocol 2: Style-Content Disentanglement with Contrastive Learning

Objective: Learn style representations that are explicitly disentangled from content using contrastive learning with hard negative examples.

Materials and Reagents:

• Anchor Texts: Primary documents for which authorship is being verified
• Positive Pairs: Different texts by the same author
• Negative Pairs: Texts by different authors, with controlled topic similarity
• Content Model: Pre-trained semantic similarity model for hard negative generation

Procedure:

• Hard Negative Generation:
  • Use semantic similarity models to identify texts by different authors with high topic overlap
  • Synthetically create challenging examples that force style-specific learning

• Contrastive Learning Setup:

  • Implement InfoNCE loss framework with modifications for style-content separation
  • Structure training triplets: (anchor, positive, hard negative)
  • Define similarity metrics optimized for stylistic rather than content similarity

• Multi-Objective Training:

  • Primary objective: Minimize distance between style representations of same-author texts
  • Secondary objective: Maximize separation between style and content embedding spaces
  • Apply mutual information minimization between style and content representations

• Embedding Space Regularization:

  • Implement adversarial components to remove content information from style embeddings
  • Use motivator networks to retain stylistic information in style embeddings [10]

Validation Method: Out-of-domain evaluation on texts from completely different topics and genres to verify true style learning [10]

Protocol 3: Self-Attentive Multi-Feature Ensemble

Objective: Create a robust ensemble model that dynamically weights different feature types based on their discriminative power for specific verification tasks.

Materials and Reagents:

• Multiple Feature Extractors: CNN pathways for different feature types
• Attention Mechanism: Multi-head self-attention for feature weighting
• Fusion Layer: Weighted integration of feature representations

Procedure:

• Parallel Feature Processing:
  • Implement separate convolutional neural networks for different feature categories
  • Train each CNN pathway to extract discriminative patterns from its specific feature type

• Attention-Based Fusion:

  • Apply self-attention mechanisms to compute dynamic weights for each feature type
  • Generate attention scores based on feature discriminativity for each verification pair
  • Compute weighted combinations of feature representations

• Hierarchical Classification:

  • Use SoftMax classifier with temperature scaling for calibration
  • Implement weighted decision fusion based on confidence estimates
  • Apply ensemble refinement through bootstrap aggregation

Validation Method: Comprehensive testing on datasets with varying numbers of authors (4-30) and topic heterogeneity [11]

Visualization of Methodologies

Feature Interaction Network Architecture

Style-Content Disentanglement Framework

The Scientist's Toolkit

Research Reagent Solutions

Table 3: Essential Research Reagents for Authorship Verification Experiments

Reagent/Tool	Specifications	Primary Function	Application Context
Pre-trained Language Models	RoBERTa-base, BERT-large, Transformer architectures	Semantic embedding extraction, baseline representations	Content understanding, contextual feature extraction
Stylometric Feature Extractors	NLTK, spaCy, custom Python libraries	Quantification of syntactic, lexical, and structural patterns	Style representation, topic-agnostic feature generation
Contrastive Learning Frameworks	Modified InfoNCE loss, triplet loss implementations	Style-content disentanglement, representation learning	Cross-domain verification, style purification
Hard Negative Generators	Semantic similarity models, topic modeling tools	Creation of challenging training examples	Model robustness improvement, content bias reduction
Evaluation Datasets	PAN AV corpus, custom multi-topic collections	Model validation, cross-topic performance assessment	Experimental rigor, real-world simulation
Neural Architecture Components	Cross-attention mechanisms, fusion layers	Feature integration, interaction modeling	Multi-modal learning, information combination
DS55980254	DS55980254, MF:C29H18F8N4O4, MW:638.5 g/mol	Chemical Reagent	Bench Chemicals
T100-Mut	T100-Mut, MF:C161H271N49O41S, MW:3581.2 g/mol	Chemical Reagent	Bench Chemicals

The strategic combination of semantic embeddings and stylometric features represents a significant advancement in feature engineering for cross-topic authorship verification. By addressing the fundamental style-content entanglement problem through sophisticated architectural designs and learning paradigms, researchers can develop more robust verification systems that maintain accuracy across diverse topics and domains. The experimental protocols and methodologies outlined provide a comprehensive framework for implementing these approaches, while the visualization tools and reagent specifications offer practical guidance for experimental implementation. As the field evolves, further innovation in feature engineering will continue to enhance our ability to isolate and identify the fundamental stylistic fingerprints that distinguish authors across their varied writings.

In cross-topic authorship verification, the fundamental challenge is to identify an author's unique stylistic signature independently of the text's topic or genre. This requires neural architectures capable of learning topic-invariant representations of writing style. Siamese networks, feature interaction models, and pairwise frameworks have emerged as pivotal paradigms for this task. These architectures facilitate direct comparison between text pairs, enabling the model to discern subtle stylistic commonalities even when documents address entirely different subjects [4] [13]. Their application is crucial for real-world scenarios where training and testing data rarely share thematic content, moving beyond the limitations of traditional approaches that often conflate topic-based and style-based features [14].

The core principle underlying these architectures is metric learning—learning a feature space where same-author documents are positioned closer together than those by different authors. This approach has demonstrated remarkable robustness in cross-topic and open-set conditions, where the authors encountered during testing may not have been present in the training data [13]. By focusing on relative comparisons rather than absolute classification, these models can generalize more effectively to unseen authors and topics, which is essential for practical applications in digital forensics, cybersecurity, and academic integrity verification [4] [14].

Key Neural Architectures for Authorship Verification

Siamese Network Architectures

Siamese networks represent a powerful class of neural architectures for verification tasks, characterized by two or more identical subnetworks that share parameters and process inputs in parallel [15] [13]. This architectural symmetry ensures that both inputs are processed through the same transformation, making the network naturally suited for similarity learning.

• Text-Based Siamese Networks: For textual authorship verification, a Siamese architecture can utilize RoBERTa embeddings to capture semantic content while simultaneously incorporating stylistic features such as sentence length, word frequency, and punctuation patterns [4]. The parallel processing streams generate compact feature representations for each text, which are then compared using distance metrics to determine authorship similarity.

• Graph-Based Siamese Networks: An innovative approach represents texts as graphs based on co-occurrence patterns of Part-of-Speech (POS) tags [13]. In this architecture, Graph Convolutional Networks (GCNs) within a Siamese framework extract structural features from these graph representations. The model computes authorship similarity by comparing these graph-based stylistic representations, effectively capturing syntactic writing patterns that are largely topic-agnostic.

• Computer Vision-Inspired Siamese Networks: For handwritten document verification, Siamese Convolutional Neural Networks process pairs of document images [15]. The identical subnetworks typically comprise convolutional layers with ReLU activation and pooling operations, followed by fully connected layers. The output encodings are concatenated using an expanded feature interaction vector: v = [a, b, a-b, a⊙b], where 'a' and 'b' are the feature vectors from each subnetwork, 'a-b' represents their absolute difference, and 'a⊙b' denotes their element-wise product [15]. This enriched representation captures both individual features and their relational dynamics.

Table 1: Comparative Analysis of Siamese Network Architectures

Architecture Type	Input Modality	Core Components	Feature Representation	Cross-Topic Performance
Text-Based Siamese [4]	Text tokens	RoBERTa embeddings, style features	Semantic + stylistic embeddings	Improved with style features
Graph-Based Siamese [13]	POS co-occurrence graphs	GCN layers, pooling operations	Structural syntactic patterns	AUC 90-92.83%
CV-Inspired Siamese [15]	Document images	Convolutional layers, residual blocks	Visual handwriting features	Robust to topic variation

Feature Interaction Networks

The Feature Interaction Network represents an alternative approach that explicitly models the relationships between different feature types, particularly the interplay between semantic content and stylistic elements [4]. Unlike Siamese architectures that process inputs separately, feature interaction models typically combine representations early in the processing pipeline to learn complex feature correlations.

These networks address the limitation of models that process semantic and stylistic features in isolation, which may fail to capture important interactions between content and style. By explicitly modeling these relationships, feature interaction networks can better disentangle topic-related features from genuine stylistic signatures, which is crucial for cross-topic generalization [4]. The interactive processing allows the model to learn, for instance, how an author's characteristic sentence structure manifests across different semantic contexts, creating a more robust representation of writing style.

Pairwise Models and Frameworks

Pairwise models encompass architectures specifically designed to compare two text samples directly, with the Pairwise Concatenation Network being a prominent example [4]. These frameworks typically employ a single backbone network that processes concatenated or otherwise combined representations of both texts, learning to directly predict authorship similarity without generating intermediate individual representations.

The Rationale-Aware Answer Verification with Pairwise Self-Evaluation (REPS) framework, though developed for answer verification, provides a valuable methodological approach applicable to authorship verification [16]. REPS iteratively applies pairwise self-evaluation using the same language model that generates solutions, selecting valid rationales from candidates. This emphasis on validating the reasoning process rather than just the final output parallels the needs of robust authorship verification, where surface-level features can be misleading, and deeper stylistic consistency must be verified.

Table 2: Performance Metrics of Neural Architectures on Benchmark Tasks

Architecture	Dataset	Evaluation Metrics	Performance	Cross-Topic Robustness
Feature Interaction Network [4]	Diverse authorship corpus	Accuracy, F1-score	Competitive with state-of-the-art	Consistent improvement with style features
Siamese CNN [15]	IAM Handwriting	Verification accuracy	Best with ResNet variant	N/A (image-based)
Graph-Based Siamese [13]	PAN@CLEF 2021	AUC ROC, F1, Brier score	90-92.83%	Specifically designed for cross-topic
Pre-trained LM with MHC [14]	CMCC corpus	Cross-entropy, accuracy	Promising in cross-domain	Effect of normalization corpus crucial

Experimental Protocols for Cross-Topic Authorship Verification

Data Preparation and Preprocessing

Text-Based Approaches: For textual authorship verification, begin by collecting a dataset with multiple authors, topics, and genres. The CMCC corpus is particularly suitable for cross-domain evaluation as it controls for genre, topic, and author demographics [14]. Implement a stratified splitting procedure to ensure that training and testing sets contain completely different topics while maintaining a balanced representation of authors. Apply text preprocessing including lowercasing, punctuation normalization, and tokenization. For models using pre-trained language models like RoBERTa, tokenize texts using the appropriate tokenizer and truncate or pad to the model's maximum sequence length [4].

Handwriting-Based Approaches: For handwritten document verification, utilize the IAM Handwriting Database, which contains samples from 657 writers [15]. Reorganize the dataset for authorship verification by creating positive pairs (same author) and negative pairs (different authors). Apply image preprocessing steps including thresholding to remove scanning artifacts (pixel values above a threshold are set to white), cropping to a standardized horizontal size (e.g., 700 pixels), and potential downsampling to reduce computational requirements. Data augmentation through random cropping can improve model robustness [15].

Graph-Based Representations: For graph-based approaches, convert texts into graph structures using POS co-occurrence relationships [13]. Implement three strategic representations: "short" (simplest graph structure), "med" (moderate complexity), and "full" (most comprehensive). Define nodes representing words or POS tags, with edges reflecting co-occurrence within a specified window. This graph representation explicitly captures syntactic patterns largely independent of topic.

Model Training Protocols

Siamese Network Training: Initialize the base network (CNN, GCN, or transformer) with pre-trained weights when available. For the loss function, employ contrastive loss or binary cross-entropy with a final similarity layer. Set hyperparameters including learning rate (e.g., 0.001 for Adam optimizer), batch size (dependent on memory constraints), and dropout rate (typically 0.5 for regularization) [15]. Monitor training to ensure the distance metric effectively separates same-author and different-author pairs in the learned feature space.

Cross-Topic Evaluation Framework: Implement a rigorous evaluation protocol where test topics are completely disjoint from training topics. Use appropriate evaluation metrics for verification tasks: Area Under the Curve (AUC), F1 score, Brier score, and the PAN@CLEF specific metrics F0.5u and C@1 [13]. Employ a normalization corpus to calibrate model outputs, which is particularly crucial in cross-domain conditions [14]. This corpus should contain documents from the same domain as the test documents to provide relevant normalization signals.

Advanced Training Techniques: For graph-based Siamese networks, experiment with different pooling strategies (graph pooling layers) and classification architectures [13]. Implement ensemble approaches that combine multiple graph representations or integrate stylistic feature extractors alongside the main architecture. For threshold-dependent metrics, perform threshold adjustment on a validation set to optimize performance.

Validation and Interpretation

Perform ablation studies to quantify the contribution of different components, particularly the value of incorporating explicit style features alongside semantic representations [4]. Analyze the model's performance variation across different topic transitions to identify potential topic bias residues. Employ visualization techniques to examine the learned feature space and verify that same-author documents cluster regardless of topic differences. For neural network language models, utilize the multi-headed classifier approach and analyze the cross-entropy scores across different candidate authors, normalized using the relative entropies from an appropriate normalization corpus [14].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Materials and Datasets for Authorship Verification

Research Reagent	Function/Application	Key Characteristics	Implementation Considerations
CMCC Corpus [14]	Cross-topic and cross-genre evaluation	Controlled corpus with 21 authors, 6 genres, 6 topics	Enables rigorous cross-domain testing
IAM Handwriting Database [15]	Handwritten document verification	657 writers, 1539 scanned pages	Requires reorganization for authorship pairs
PAN@CLEF Datasets [13]	Benchmarking for authorship verification	Fanfiction texts, cross-topic scenarios	Provides "small" and "large" corpus options
Pre-trained Language Models (RoBERTa, BERT) [4] [14]	Semantic feature extraction	Contextual token representations	Requires fine-tuning on authorship task
POS Tagging Tools [13]	Graph construction for syntactic analysis	Converts text to sequence of POS tags	Multiple tagging strategies available
Normalization Corpus [14]	Calibrating model outputs	Unlabeled domain-relevant texts	Crucial for cross-domain performance
SCAL-266	SCAL-266, MF:C27H28F3N5O2, MW:511.5 g/mol	Chemical Reagent	Bench Chemicals
BaENR-IN-1	BaENR-IN-1, MF:C12H8ClNO4, MW:265.65 g/mol	Chemical Reagent	Bench Chemicals

Architectural Diagrams and Workflows

Siamese Network Architecture for Authorship Verification

Graph-Based Authorship Verification Workflow

Cross-Topic Verification Protocol

Leveraging Pre-Trained Language Models for Offline and Secure AV

Authorship Verification (AV) is a critical task in natural language processing, essential for applications ranging from plagiarism detection and identity verification to the authentication of digital content. The core challenge in AV is to accurately determine whether two texts were written by the same author, a task that becomes significantly more difficult when the texts cover different topics. Cross-topic authorship verification research aims to develop methods that are robust to topic variation, forcing models to rely on genuine stylistic fingerprints rather than superficial semantic cues.

The emergence of pre-trained language models (PLMs) has revolutionized this field, offering powerful, generalized text representations. When leveraged for offline and secure AV, these models provide a formidable toolkit for creating privacy-preserving, reliable, and efficient verification systems that do not depend on cloud-based services. This application note details the protocols and methodologies for implementing such systems within a broader cross-topic AV research framework, providing researchers and development professionals with structured guidance, quantitative comparisons, and reproducible experimental workflows.

The Role of Pre-Trained Language Models in AV

Pre-trained language models, including both large language models (LLMs) and their more efficient counterparts, small language models (SLMs), provide a foundational capability for modern AV systems. Their primary value lies in their ability to generate high-quality, contextualized embeddings that capture deep semantic and syntactic features of text, many of which are correlated with an author's unique stylistic signature.

Operating these models offline introduces significant advantages, particularly for security-sensitive domains like drug development, where protecting proprietary research data is paramount. Offline operation ensures that sensitive documents never leave the local environment, mitigating data breach risks and providing uninterrupted functionality regardless of internet connectivity [17]. This aligns with the growing emphasis on data protection and threat management in corporate AI strategies [18].

For cross-topic verification, the generalized knowledge encoded in PLMs during their pre-training on vast corpora is invaluable. It allows the model to separate an author's persistent stylistic choices from the variable content of the text, which is a prerequisite for effective cross-topic analysis. Recent research confirms that combining the deep semantic features from PLMs with explicit stylistic features—such as sentence length, word frequency, and punctuation—consistently enhances AV model performance, making the approach more robust to the topic shifts encountered in real-world data [4].

Model Selection: Quantitative Comparison of Pre-Trained Models

Selecting an appropriate PLM is a balance between performance, computational requirements, and operational constraints. For offline and secure AV, smaller models are often advantageous due to their lower hardware demands and faster inference times, making them suitable for deployment on standard workstations or even laptops. The following table summarizes key candidate models ideal for an offline AV research setup.

Table 1: Comparison of Small Language Models for Offline AV Applications

Model Name	Parameter Size Range	Key Features for AV	Context Window	Ideal Deployment Hardware
Gemma 3 [19]	1B - 27B	Multilingual support (140+ languages), efficient decoder-only transformer with RoPE.	32K - 128K tokens	Laptops (1B) to single GPU (27B)
Qwen 3 [19]	0.6B - 30B	Strong multilingual capability (100+ languages), supports quantization for low-memory devices.	32K - 128K tokens	Mobiles, browsers, laptops, single GPU
Llama 3.2 [19]	1.3B - 13B	Grouped Query Attention, SwiGLU activations for efficient processing.	Varies by size	Mobile/edge (1.3B) to server-side (13B)
Mistral Small 3 [19]	24B	High performance relative to size (81% on MMLU), optimized for low-latency.	Information missing	Single Nvidia RTX 4090 or MacBook with 32GB RAM
Phi-3 [18]	Information missing	Compact model designed with enhanced reasoning capabilities.	Information missing	Resource-constrained environments

The choice of model should be guided by the specific requirements of the AV task. For instance, a multilingual verification system would benefit from Gemma 3 or Qwen 3, whereas a setup with strict latency requirements might leverage the optimizations in Mistral Small 3 or Llama 3.2. The trend towards specialized, fine-tuned models promises enhanced performance and cost-efficiency for domain-specific applications [18].

Experimental Protocols for Cross-Topic Authorship Verification

A robust experimental protocol for cross-topic AV must be designed to force the model to learn author-specific stylistic features rather than topic-specific artifacts. The following workflow provides a detailed methodology for training and evaluating an AV system using pre-trained models.

Data Preparation and Cross-Topic Splitting

The foundation of reliable cross-topic evaluation is a dataset where individual authors have written on multiple, distinct topics.

• Dataset Selection: Prefer datasets explicitly designed for cross-topic and cross-lingual ablation. The Million Authors Corpus (MAC) is a prime example, containing 60.08 million textual chunks from 1.29 million Wikipedia authors across dozens of languages, with each author contributing texts on diverse subjects [7].
• Data Splitting Protocol: Split the data at the author level, not the document level, to prevent data leakage. For each author, deliberately assign texts from different topical categories (e.g., "Arts," "Sciences," "Politics") to the training and test sets. This ensures the model is evaluated on its ability to verify authorship across unseen topics.

Feature Extraction and Fusion

This step involves generating a feature vector for each text that captures both meaning and style.

• Semantic Feature Extraction: Pass each pre-processed text through your selected PLM (e.g., a RoBERTa model) to obtain a contextualized embedding. The standard approach is to use the vector representation of the [CLS] token or the mean pooling of all output vectors as the text's semantic embedding [4].
• Stylistic Feature Calculation: Concurrently, compute a set of stylistic features for each text. Key features include:
  • Syntax: Average sentence length, sentence length variance, part-of-speech tag frequencies.
  • Vocabulary: Word richness (type-token ratio), frequency of function words, character-level n-grams.
  • Layout: Punctuation frequency (commas, semicolons), paragraph length.
• Feature Fusion: Combine the semantic embeddings and stylistic features into a unified representation. Research has shown that architectures like the Feature Interaction Network or Pairwise Concatenation Network are effective at modeling the relationship between these two feature types for the verification task [4].

Model Training and Evaluation

The fused features are used to train a verification model.

• Training Protocol: Frame the problem as a binary classification task where the input is a pair of texts and the label indicates whether they are from the same author. Use a Siamese Network architecture to process text pairs and compute a similarity score. Train the model using a contrastive loss or binary cross-entropy loss.
• Cross-Topic Evaluation: The final model must be evaluated on a held-out test set where all text pairs are topic-disjoint from the training data. Performance should be reported using standard metrics for verification tasks, such as Accuracy, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC).

Offline Deployment and Security Protocols

Deploying the trained AV system offline requires a streamlined local infrastructure.

• Local Model Serving with Ollama: Ollama is a tool that simplifies running LLMs and SLMs locally via a command-line interface. It manages model downloads, dependencies, and provides a simple API for inference, making it an ideal choice for research environments [17].
• Enhanced Security via Watermarking: To protect the intellectual property of the deployed AV model itself and ensure the traceability of its outputs, consider integrating LLM watermarking technology. This involves embedding a covert, robust signal into the model's generated embeddings or decisions, allowing for later verification of the model's use and providing a mechanism for accountability in secure environments [20].

The Scientist's Toolkit: Research Reagents and Materials

The following table details essential "research reagents"—software and data components—required to build and experiment with an offline AV system.

Table 2: Essential Research Reagents for Offline AV System Development

Item Name	Type	Function/Benefit	Example Source/Platform
Ollama	Software Tool	Simplifies local deployment and management of various open-source LLMs/SLMs.	ollama.ai [17]
Pre-trained Models (SLMs)	Model Weights	Provide foundational semantic understanding; smaller size allows for offline operation.	Hugging Face, Kaggle [19]
The Million Authors Corpus (MAC)	Dataset	Enables rigorous cross-topic and cross-lingual evaluation, preventing topic-based overfitting.	ACL Anthology [7]
Hugging Face Transformers	Software Library	Provides open-source APIs to load, fine-tune, and extract features from thousands of PLMs.	huggingface.co [17]
Chromium Oxide	Information missing	Information missing	Information missing
Stylometric Feature Extractor	Custom Code	Calculates linguistic style features (syntax, vocabulary) crucial for distinguishing authors.	NLTK, spaCy libraries
Watermarking Toolkit	Algorithm Suite	Embeds detectable signatures into model outputs for IP protection and traceability.	Custom implementation based on research [20]
DPI-2016	DPI-2016, MF:C25H32N8O10S2, MW:668.7 g/mol	Chemical Reagent	Bench Chemicals
NC-R17	NC-R17, MF:C53H67N7O7, MW:914.1 g/mol	Chemical Reagent	Bench Chemicals

Validation and Metamorphic Testing

Ensuring that an AV model makes decisions based on genuine stylistic signals and not on dataset artifacts is crucial. Metamorphic Testing (MT) provides a powerful validation framework.

• Principle: Instead of verifying the absolute correctness of a single output, MT checks whether the model behaves consistently across multiple, related inputs [21].
• Protocol: Define a set of Metamorphic Relations (MRs)—semantic-preserving transformations that should not affect the authorship of a text. For a given text pair (A, B), create transformed versions (A', B').
  • Example MRs: Uppercase/lowercase conversion, synonym replacement (using a thesaurus), punctuation removal, or sentence paraphrasing.
• Validation Check: For a robust AV model, the prediction for (A, B) should be consistent with the prediction for (A', B'). A high rate of inconsistency (violations) indicates that the model is sensitive to irrelevant surface-level changes and is not learning a stable authorship representation [21]. This is particularly important for validating model performance in cross-topic scenarios.

The integration of pre-trained language models into offline and secure authorship verification systems represents a significant advancement for cross-topic research and application. By following the detailed protocols and application notes outlined above—from selecting efficient models like Gemma 3 or Mistral Small 3, to implementing cross-topic experimental designs with datasets like MAC, and validating systems with metamorphic testing—researchers can build robust, privacy-conscious AV tools. These systems are capable of reliably identifying authors based on their unique stylistic signatures, independent of topic, thereby enabling more trustworthy authentication in critical fields such as academic research, intellectual property protection, and drug development.

The evaluation of authorship verification (AV) models—which determine if two texts were written by the same author—faces a significant challenge in achieving robustness against topic variation. Conventional cross-topic evaluation aims to assess how well a model generalizes across different subjects by minimizing topic overlap between training and test sets. However, topic leakage can persist within test data, where models may leverage residual, topic-specific lexical features as shortcuts rather than learning an author's genuine stylistic signature. This leakage leads to misleading performance metrics and an unstable ranking of AV models, ultimately hindering reliable progress in the field [8] [22].

To address this core issue, the Heterogeneity-Informed Topic Sampling (HITS) framework was developed. HITS is an evaluation methodology designed to create datasets with a controlled, heterogeneous distribution of topics. This design intentionally exposes and mitigates the confounding effects of topic leakage, enabling a more rigorous and stable assessment of an AV model's true capability to identify authorship based on style, irrespective of content [8].

Core Concepts and Rationale

The Problem of Topic Leakage

In an ideal authorship verification scenario, a model should make decisions based on consistent, topic-agnostic features of an author's writing style. However, models can achieve high performance by exploiting spurious correlations. If texts within the same topic often share the same author in the test set, a model may learn to associate specific vocabulary or phrases related to that topic with an author, rather than their fundamental stylistic patterns. This reliance on topic-specific features inflates performance metrics in evaluations but fails to generalize to real-world scenarios where an author writes on diverse topics [8]. The conventional evaluation practice assumes minimal topic overlap, but HITS research argues that subtle topic leakage can still occur, corrupting the evaluation process [8] [22].

The HITS Solution

The HITS framework counters this by systematically constructing evaluation datasets where topics are heterogeneously distributed. This sampling strategy ensures that topic identity cannot be used as a reliable shortcut for verifying authorship. The core outcome is a more stable model ranking across different random seeds and data splits, providing researchers with greater confidence in the comparative performance of different AV architectures and training methodologies [8].

Experimental Protocols and Implementation

The HITS Sampling Workflow

Implementing the HITS framework involves a structured process for creating a robust evaluation dataset. The following diagram and protocol outline the key steps.

Diagram Title: HITS Dataset Creation Workflow

Protocol 1: HITS Dataset Creation

• Input: A large raw corpus of text documents with associated author metadata.
• Procedure:
  • Topic Annotation: Manually or automatically assign a topic label to every document in the corpus. This can be achieved using predefined categories, keyword matching, or unsupervised topic modeling techniques (e.g., Latent Dirichlet Allocation).
  • Analyze Topic Distribution: Profile the initial corpus to understand the frequency and co-occurrence of topics per author. This analysis helps identify potential topic biases that could lead to leakage.
  • Heterogeneity-Informed Sampling: Sample a subset of documents from the larger corpus. The sampling strategy is designed to maximize the diversity of topics within the subset and, crucially, to ensure that for the selected authors and topics, the link between a single topic and a single author is broken. The goal is to create a "heterogeneously distributed topic set" [8].
  • Construct Text Pairs: From the sampled documents, create the final dataset composed of text pairs, each labeled as either "same-author" or "different-author." The pairing should be done in a way that maintains the topic heterogeneity established in the previous step.
• Output: A HITS-sampled dataset ready for use in model evaluation [8].

Benchmark Evaluation with RAVEN

A critical component of the HITS framework is the Robust Authorship Verification bENchmark (RAVEN). RAVEN is designed explicitly to test AV models' susceptibility to topic-specific shortcuts [8] [22].

Protocol 2: Model Evaluation using RAVEN

• Objective: To reliably rank different AV models based on their robustness to topic shifts and not their reliance on topic leakage.
• Procedure:
  • Dataset Splitting: Partition the HITS-generated dataset (or RAVEN) into standard training, validation, and test splits, ensuring no author overlap between splits.
  • Model Training & Fine-tuning: Train or fine-tune various AV models (e.g., BERT-based architectures, Siamese networks) on the training split.
  • Performance Evaluation: Evaluate all models on the held-out test set from the HITS/RAVEN benchmark.
  • Model Ranking: Rank the models based on their performance metric (e.g., AUC, F1-score) on this test set.
  • Stability Assessment: Repeat the evaluation across multiple random seeds and data splits. The HITS framework demonstrates its value by yielding a more stable and reliable model ranking compared to evaluations on datasets prone to topic leakage [8].

Key Findings and Validation

The efficacy of the HITS framework is demonstrated through quantitative experimental results. The primary finding is that datasets created using HITS sampling lead to a more stable ranking of AV models across different evaluation conditions [8].

Table 1: Comparative Model Performance and Ranking Stability on Conventional vs. HITS-Sampled Datasets

Evaluation Metric	Conventional Dataset	HITS-Sampled Dataset	Implication
Model Ranking Volatility	High volatility across random seeds and splits [8]	Low volatility, stable rankings [8]	Enables reliable model comparison
Susceptibility to Topic Shortcuts	High; models exploit topic leakage [8]	Reduced; topic shortcuts are mitigated [8]	Measures genuine stylistic understanding
Benchmark Utility	Potentially misleading performance metrics [22]	Provides a robust test for model generalization (as in RAVEN) [8] [22]	Drives development of more robust AV models

The following table details key computational tools and resources essential for research in robust authorship verification and for implementing the HITS framework.

Table 2: Essential Research Reagents and Resources for Authorship Verification

Resource / Tool	Type	Primary Function in AV Research
RAVEN Benchmark	Dataset / Benchmark	Provides a standardized testbed for evaluating AV model robustness against topic shortcuts [8] [22].
Pre-trained Language Models (e.g., BERT)	Software / Model	Serves as a foundational backbone for building modern, high-performance AV systems through transfer learning [22].
Sentence Transformers (e.g., Sentence-BERT)	Software / Library	Generates semantically meaningful sentence embeddings, which are crucial for comparing the stylistic similarity of text pairs [22].
Scikit-learn	Software / Library	Provides a wide range of state-of-the-art machine learning algorithms for medium-scale modeling and data preprocessing [22].

Application in Experimental Design

The logical relationship between the core components of a robust AV evaluation, as championed by the HITS framework, is summarized below.

Diagram Title: HITS Logic: From Problem to Solution

The HITS framework provides a critical methodology for strengthening the experimental foundations of authorship verification research. By systematically addressing topic leakage through heterogeneity-informed dataset creation and the RAVEN benchmark, it empowers researchers to develop models that genuinely capture authorship style, thereby advancing the field's reliability and applicability.

Generating Controllable Explanations (CAVE) for Transparent Decision-Making

Authorship Verification (AV), the task of determining whether two texts share the same author, is a critical challenge in natural language processing with applications in plagiarism detection, forensic analysis, and content authentication [23]. While high-performing models exist, a significant limitation in real-world deployment—particularly in privacy-sensitive domains like legal proceedings or academic integrity investigations—is their lack of accessible, transparent explanations for their decisions [23]. The CAVE (Controllable Authorship Verification Explanations) framework addresses this gap by generating free-text explanations that are both controllable and easily verifiable by human analysts [23].

Traditional stylometry-based AV systems often suffer from limited accuracy, while modern deep learning models can function as "black boxes," making it difficult for users to trust and understand their outputs [23] [4]. The CAVE model is designed specifically for offline, on-premises deployment, making it suitable for environments where data cannot be shared with external application programming interfaces (APIs), such as with confidential legal documents or unpublished manuscripts [23]. By producing structured explanations grounded in specific linguistic features, CAVE enhances the transparency and practical utility of AV systems, enabling researchers and professionals to verify not just the outcome of an authorship decision, but the reasoning behind it.

Core Principles and System Architecture

The CAVE framework is built upon the principle that explanations for authorship decisions must be controllable and consistent. Controllability ensures that the generated explanations follow a uniform structure, decomposing the rationale into sub-explanations that are grounded in relevant linguistic features [23]. This structured approach makes the explanations easier for humans to parse and evaluate. Consistency ensures that the generated explanation logically aligns with the final verification label (i.e., "same author" or "different authors"), which is crucial for building trust in the system [23].

Architectural Workflow

The following diagram illustrates the end-to-end workflow of the CAVE system, from data preparation and model training to the final generation of a verified explanation.

Key Design Considerations

The architecture of CAVE is designed to overcome several challenges inherent to AV explanation generation:

• Offline Proprietary Model: CAVE is a trained, offline model, which is essential for processing sensitive documents that cannot be sent to cloud-based APIs [23].
• Combining Semantic and Stylistic Features: Effective AV requires analyzing both what is written (semantic content) and how it is written (stylistic features) [4]. CAVE's approach is informed by models that use RoBERTa embeddings to capture semantic content and incorporate style features such as sentence length, word frequency, and punctuation [4].
• Explanation-Label Consistency: A novel metric, Cons-R-L, is used during training to filter generated explanations for rationale-label consistency, ensuring the final output is logically sound [23].

Quantitative Performance Evaluation

The performance of the CAVE framework was evaluated on three difficult AV datasets. The results demonstrate its competitiveness in terms of task accuracy and the quality of the generated explanations, as measured by both automatic metrics and human evaluation [23].

Table 1: Key Performance Metrics of the CAVE Model on Benchmark Datasets

Dataset	Primary Task Accuracy	Explanation Quality (F1)	Key Strengths
Dataset 1	Competitive	High	Robust performance on stylistically diverse texts
Dataset 2	Competitive	High	Effective handling of topic shifts between documents
Dataset 3	Competitive	High	High rationale-label consistency (Cons-R-L)

The model achieves these results by fine-tuning a Llama-3-8B parameter model on a silver-standard training dataset created via a prompt-based method called Prompt-CAVE [23]. This method generates the initial training data, which is grounded in desirable linguistic features, before being filtered for quality.

Experimental Protocols and Methodologies

This section provides a detailed, step-by-step protocol for replicating the CAVE training pipeline and applying the model for authorship verification with explanations.

Protocol A: Data Generation and Model Training

Objective: To create a silver-standard dataset and train the CAVE model for controllable explanation generation.

Materials:

• A collection of text pairs with known authorship (same author/different authors).
• Pre-trained Llama-3-8B model.
• Computational resources (GPU cluster recommended).

Procedure:

• Data Generation via Prompt-CAVE:
  • For each text pair in the training corpus, use a powerful instructor model (e.g., GPT-4) with tailored prompts to generate candidate explanations.
  • The prompt should instruct the model to generate explanations that are structured, referencing specific stylistic and semantic features (e.g., "The consistent use of complex sentence structures across both texts suggests a shared authorial voice.").
  • This results in a preliminary, "silver-standard" dataset of {text pair, label, explanation} triplets.

• Data Filtering with Cons-R-L:

  • Apply the Cons-R-L metric to score each generated explanation for its consistency with the assigned authorship label.
  • Filter out all triplets with a Cons-R-L score below a predetermined threshold (e.g., below the top 30th percentile). This ensures only high-quality, logically consistent examples are used for training.

• Model Fine-Tuning:

  • Initialize the training process with the pre-trained Llama-3-8B model.
  • Fine-tune the model on the filtered silver-standard dataset. The training objective is a standard text-generation task, where the model learns to map input text pairs to the corresponding, structured explanation and verification label.
  • Validate the model on a held-out development set, monitoring for task accuracy and explanation coherence.

Protocol B: Authorship Verification with Explanation Generation

Objective: To use the trained CAVE model for verifying authorship and generating a controllable explanation for a new pair of documents.

Materials:

• Trained CAVE model (from Protocol A).
• Two text documents (Document A and Document B) of unknown authorship relation.

Procedure:

• Pre-processing:
  • Clean and standardize the text of both documents (e.g., remove extraneous headers, footers).
  • Tokenize the text into a format compatible with the CAVE model's input requirements.

• Model Inference:

  • Concatenate the processed texts of Document A and Document B into a single input sequence for the model.
  • Feed the input sequence into the CAVE model.
  • The model will generate an output sequence containing both the verification decision ("same author" or "different authors") and a free-text explanation.

• Output and Analysis:

  • Parse the model's output to separate the final label from the explanatory text.
  • The generated explanation should be structured, referencing specific linguistic features (e.g., syntax, vocabulary, punctuation) present in the input texts that support the decision.
  • A human analyst can then verify the accuracy of the explanation by checking for the presence of the cited features in the original documents, ensuring explanation-label consistency.

The Scientist's Toolkit: Research Reagent Solutions

This table details the key computational "reagents" and their functions essential for implementing the CAVE framework.

Table 2: Essential Research Reagents for CAVE Implementation

Reagent / Tool	Type / Category	Primary Function in the CAVE Workflow
Llama-3-8B	Base Language Model	The foundational model that is fine-tuned to become the core of the CAVE system. [23]
RoBERTa	Text Embedding Model	Used to generate high-quality semantic embeddings of the input texts, capturing meaning and context. [4]
Prompt-CAVE	Data Generation Method	A prompt-based technique for creating the initial silver-standard training data. [23]
Cons-R-L Metric	Evaluation Metric	A novel metric for filtering training data by measuring the consistency between a generated rationale and its corresponding label. [23]
Stylometric Features	Feature Set	Pre-defined features (sentence length, word frequency, punctuation) used to ground explanations and differentiate author style. [4]
GPU Cluster	Computational Resource	Provides the necessary processing power for fine-tuning large language models and running inference.
ERK2-IN-3	ERK2-IN-3, MF:C26H21F3N4O, MW:462.5 g/mol	Chemical Reagent
SYHA1813	SYHA1813, MF:C25H19FN4O, MW:410.4 g/mol	Chemical Reagent

System Integration and Logical Data Flow

The internal logic of the CAVE model involves processing two texts simultaneously, analyzing them through a unified representation that combines their semantic and stylistic profiles, and using this to generate a final, verifiable output. The following diagram details this integrated reasoning process.

Overcoming Topic Leakage and Enhancing Model Generalization

Identifying and Mitigating Topic Leakage in Cross-Topic Benchmarks

Topic leakage represents a significant and often overlooked challenge in cross-topic authorship verification (AV) research. This phenomenon occurs when topic-related information from the training data inadvertently influences the model's decision-making process on test documents, thereby compromising the validity of authorship claims. In standard AV, the core question is whether two documents were written by the same person, but when topic features dominate stylistic features, models may simply learn to associate topics with authors rather than capturing genuine stylistic signatures [24]. This problem is particularly acute in cross-topic benchmarks, where models are tested on documents with topics different from those in the training set.

The critical importance of addressing topic leakage stems from its potential to severely undermine the real-world applicability of AV systems. Forensic applications, academic integrity investigations, and historical document analysis rarely provide topic-matched writing samples, requiring models that can disentangle an author's unique stylistic choices from subject matter content. Research by Halvani et al. has demonstrated that existing AV methods are particularly prone to performance degradation in cross-topic verification scenarios, highlighting an urgent need for specialized benchmarking protocols and mitigation strategies [24].

Understanding Topic Leakage Mechanisms

Conceptual Framework and Definitions

Topic leakage constitutes a specific manifestation of data leakage in machine learning pipelines, characterized by the intrusion of topic-specific information into the feature space used for authorship determination. Unlike general data leakage, which involves any breach of the separation between training and test data, topic leakage specifically concerns the model's inability to distinguish between an author's consistent writing style and the semantic content of the documents [25] [26].

In formal terms, topic leakage occurs when a model trained on document pairs ( D{train} = {(di, dj, y{ij})} ) learns a mapping function ( f ) such that its predictions ( \hat{y}{test} = f(dk, dl) ) for test pairs ( (dk, d_l) ) are influenced by topic similarity between training and test domains, rather than purely by authorial style. This problem is exacerbated when the training corpus contains limited topical diversity or when feature extraction methods fail to adequately separate content-based from style-based features.

Impact on Authorship Verification Performance

The detrimental effects of topic leakage on AV systems are multifaceted and profound. When topic leakage occurs, models typically demonstrate:

• Overoptimistic Performance Metrics: Artificially inflated accuracy and F1 scores during validation that fail to generalize to real-world cross-topic scenarios [26]
• Poor Generalizability: Significant performance degradation when deployed on documents with unfamiliar topics or domains
• Misleading Feature Importance: Attribution of predictive power to topic-related features rather than genuine stylistic markers
• Reduced Forensic Utility: Limited applicability in practical investigations where topic-controlled reference documents are unavailable

Empirical studies assessing AV methods have confirmed that cross-topic verification cases present particularly challenging scenarios, with even state-of-the-art approaches experiencing substantial performance drops compared to topic-matched conditions [24]. This performance discrepancy signals the presence of undetected topic leakage during model development and evaluation.

Experimental Protocols for Detecting Topic Leakage

Benchmark Design Principles

Effective detection of topic leakage requires carefully constructed benchmarks that explicitly control for topical variation while preserving authentic stylistic signals. The foundation of such benchmarks rests on three core principles:

• Topical Stratification: Documents must be explicitly categorized by topic before assignment to training, validation, and test splits, ensuring that topic distributions differ systematically between splits
• Author Disjointness: No author should have documents in both training and test sets that share the same topic, preventing direct topic-based memorization
• Stylistic Consistency: The benchmark must contain sufficient topic-matched document pairs to establish baseline performance, enabling meaningful comparison with cross-topic conditions

These principles align with broader benchmarking protocols that emphasize rigorous dataset partitioning, explicit performance metrics, and statistical validation [27].

Cross-Topic Benchmarking Protocol

The following protocol provides a standardized method for detecting and quantifying topic leakage in AV systems:

Table 1: Cross-Topic Authorship Verification Benchmark Protocol

Step	Procedure	Output
1. Corpus Construction	Collect documents with reliable authorship attribution and explicit topic labels. Ensure each author has documents on multiple topics.	Topic-annotated corpus with author metadata
2. Topic Disjoint Splitting	Split data into training, validation, and test sets such that no topic appears in more than one split. Preserve author disjointness across splits.	Three topic-disjoint dataset partitions
3. Feature Extraction	Extract linguistic features with varying sensitivity to topic content (lexical, syntactic, structural features).	Feature matrices for each partition
4. Model Training	Train AV models on training set using standard protocols. Use validation set for hyperparameter tuning.	Trained authorship verification model
5. Cross-Topic Evaluation	Evaluate model on test set containing exclusively unseen topics. Compare performance with topic-matched validation set.	Performance metrics (accuracy, F1, AUC-ROC)
6. Leakage Quantification	Calculate topic leakage index: ( TL{index} = P{matched} - P_{cross-topic} ) where ( P ) denotes performance metric.	Quantitative measure of topic leakage

This protocol emphasizes the critical importance of proper data splitting techniques, as inappropriate splitting strategies represent a common source of data leakage in machine learning pipelines [28]. The subject-wise (author-wise) splitting approach must be maintained throughout to prevent inadvertent leakage through shared authors across splits.

Diagnostic Measurements for Topic Leakage

Beyond performance comparisons, specific diagnostic measurements can isolate topic leakage:

• Topic Influence Score: Train a topic classifier on the features used for AV; high classification accuracy indicates topic-related information in features
• Cross-Topic Generalization Gap: The performance difference between topic-matched and cross-topic conditions
• Feature Ablation Studies: Systematically remove topic-indicative features and observe performance changes
• Adversarial Validation: Train a classifier to distinguish training from test data based on features; successful classification indicates leakage

These diagnostics help researchers pinpoint the specific mechanisms through which topic information influences model predictions, enabling more targeted mitigation strategies.

Mitigation Strategies for Topic Leakage

Feature Engineering Approaches

Feature selection and engineering represent the first line of defense against topic leakage. Effective approaches include:

• Topic-Agnostic Features: Prioritize syntactic, structural, and function-word features that are less semantically loaded than content words [24]
• Topic-Adversarial Regularization: Implement loss functions that simultaneously maximize authorship discrimination while minimizing topic classification accuracy
• Vocabulary Filtering: Remove topic-specific terminology through custom stopword lists or frequency-based filtering
• Cross-Topic Feature Stability: Select features that demonstrate consistent distributions across different topics by the same author

These techniques aim to create a feature space that captures stylistic consistency while remaining invariant to topic changes, essentially forcing the model to focus on writing style rather than content.

Algorithmic Solutions

Several algorithmic adaptations can reduce sensitivity to topic information:

Adversarial Topic Invariance Framework

• Domain Adaptation Techniques: Employ domain adversarial training or domain separation networks to learn topic-invariant style representations
• Multi-Task Learning: Jointly train on authorship verification and topic classification with gradient reversal layers
• Data Augmentation: Generate synthetic training examples through text transformation techniques that preserve style but alter content
• Regularization Methods: Apply constraints that penalize topic-dependent patterns in model parameters or attention mechanisms

These algorithmic solutions explicitly model the relationship between topic and style, creating internal representations that are explicitly optimized for topic invariance.

Research Reagents and Experimental Materials

Table 2: Essential Research Reagents for Cross-Topic Authorship Verification

Reagent Category	Specific Examples	Function in Topic Leakage Research
Benchmark Corpora	PAN AV datasets, Amazon reviews, academic writing corpora	Provide standardized evaluation environments with topic annotations
Linguistic Feature Sets	POS n-grams, function word frequencies, syntactic complexity metrics	Capture stylistic patterns independent of topic content
Topic Modeling Tools	LDA, BERTopic, Top2Vec	Quantify and control topical variation in corpora
Evaluation Metrics	Cross-topic generalization gap, topic leakage index	Quantify magnitude of topic leakage
Adversarial Frameworks	Gradient reversal layers, domain adversarial networks	Implement topic-invariant learning objectives

The careful selection and application of these research reagents is essential for rigorous experimentation. Benchmark corpora must contain adequate topical diversity and reliable authorship attributions. Feature sets should be chosen to balance discriminative power for authorship with resistance to topical influence. As with all experimental protocols, proper documentation of reagents and configurations is essential for reproducibility [27].

Validation and Reporting Standards

Comprehensive Evaluation Protocol

Robust validation of topic leakage mitigation requires multi-faceted evaluation:

Multi-Factor Model Validation Protocol

• In-Domain Performance: Measure standard AV metrics on topic-matched documents
• Cross-Topic Performance: Evaluate on completely unseen topics
• Cross-Genre Performance: Test generalization across different document genres
• Cross-Time Performance: Assess temporal stability with documents from different time periods
• Ablation Studies: Systematically remove mitigation components to measure their contribution
• Statistical Significance Testing: Apply appropriate statistical tests to confirm observed differences

This comprehensive approach ensures that mitigation strategies produce genuine improvements rather than simply shifting the leakage problem to different dimensions.

Reproducibility Framework

To enhance reproducibility and comparability across studies, researchers should adopt standardized reporting practices:

• Full Disclosure of Data Splits: Explicit documentation of how documents were assigned to training, validation, and test sets, including topic labels
• Feature Documentation: Complete specification of all features used, including preprocessing steps and selection criteria
• Model Configuration Details: Comprehensive description of model architectures, hyperparameters, and training procedures
• Computational Environment: Specification of software versions, hardware configurations, and random seeds
• Failure Analysis: Transparent reporting of conditions where methods underperform or fail

These practices align with emerging standards for machine learning reproducibility, particularly important in fields like authorship verification where methodological variations can significantly impact outcomes [27] [25].

Topic leakage presents a fundamental challenge to the validity and practical utility of cross-topic authorship verification systems. Through the application of specialized benchmarking protocols, targeted mitigation strategies, and rigorous validation frameworks, researchers can develop AV systems that genuinely capture authorial style independent of topic content. The protocols and methods outlined in this document provide a foundation for advancing the field toward more robust, applicable, and trustworthy authorship verification in real-world scenarios where topic control is rarely possible. As the field progresses, continued attention to topic leakage and other forms of data leakage will be essential for bridging the gap between laboratory performance and practical effectiveness [26].

Strategies for Reducing Reliance on Topic-Specific Shortcuts

In cross-topic authorship verification (AV), a core challenge is developing models that identify authors based on writing style rather than topical content. The phenomenon of topic leakage—where test data unintentionally contains topical information similar to training data—undermines evaluation by allowing models to rely on topic-specific shortcuts rather than genuine stylistic features [29]. This reliance creates misleading performance metrics and unstable model rankings, ultimately hindering the development of robust AV systems [29]. Framed within the broader thesis on advancing cross-topic AV research, this article details practical strategies and protocols to mitigate these shortcuts, focusing on the innovative Heterogeneity-Informed Topic Sampling (HITS) framework and complementary methods for shortcut detection [29] [30].

Understanding Topic Leakage and Its Consequences

Topic leakage occurs when topics in cross-topic test sets share underlying attributes, keywords, or thematic content with topics in the training set, despite being labeled as distinct categories [29]. This leakage violates the assumption of topic heterogeneity, diminishing the intended distribution shift in cross-topic evaluation.

The consequences are twofold. First, it leads to misleading evaluation, where a model's performance appears robust to topic shifts because it exploits spurious correlations between topic-specific keywords and authors, not because it has learned invariant writing style features [29]. Second, it causes unstable model rankings, as the performance hierarchy of models can vary significantly between evaluation splits with different degrees of topic leakage, complicating the selection of truly robust models [29].

Table 1: Causes and Consequences of Topic Leakage

Aspect	Description
Primary Cause	Assumption of perfect topic heterogeneity in datasets, where labeled topic categories are treated as mutually exclusive when they are not [29].
Mechanism	Shared topical attributes (e.g., entity mentions, keywords) between training and test topics after a standard cross-topic split [29].
Consequence 1	Misleading Evaluation: Models exploit topic-specific features, inflating performance on supposedly "unseen" topics [29].
Consequence 2	Unstable Model Rankings: Model performance is inconsistent across different splits, reducing reliability for model selection [29].

Core Strategy: Heterogeneity-Informed Topic Sampling (HITS)

The HITS framework addresses topic leakage at its root by systematically constructing a dataset with a more heterogeneous distribution of topics. The core hypothesis is that a dataset with less overlapping information between topic categories will exhibit a higher degree of distribution shift in any cross-topic train-test split, thereby providing a more reliable assessment of model robustness [29].

Protocol for Implementing HITS

The following protocol outlines the steps to apply HITS to an existing dataset for AV evaluation.

Protocol 1: HITS Dataset Construction

Objective: To create a topically heterogeneous dataset from a source corpus to mitigate topic leakage in cross-topic AV evaluation. Inputs: A source dataset (e.g., Fanfiction) containing texts labeled with topic metadata [29]. Outputs: A HITS-sampled dataset and a corresponding cross-topic benchmark (e.g., part of the RAVEN benchmark) [29].

Step-by-Step Methodology:

• Topic Similarity Quantification: Compute a similarity score for every pair of topics in the source dataset. This can be achieved by:

  • Generating a vector representation for each topic (e.g., via average word embeddings of all documents within a topic or using topic model representations).
  • Calculating pairwise cosine similarity between these topic vectors.

• Heterogeneity-Based Sampling:

  • Define a target number of topics, K, for the final HITS-sampled dataset.
  • Initialize the sampled topic set, S, by selecting the two topics with the lowest pairwise similarity.
  • Iteratively add new topics to S by selecting the topic that maximizes the minimum distance (i.e., minimizes the maximum similarity) to any topic already in S. This greedy algorithm ensures the selected topic set is as heterogeneous as possible.

• Document Subsampling: From the K selected topics, subsample a fixed number of documents per topic to form the final HITS dataset. This controls for dataset size effects when comparing against random sampling baselines [29].

• Benchmark Creation (RAVEN): Use the constructed HITS dataset to define robust train/validation/test splits, ensuring no topic overlaps across splits. This forms a benchmark like RAVEN, which includes a "topic shortcut test" to diagnose model reliance on topic-specific features [29].

Figure 1: HITS Dataset Construction Workflow.

Complementary Strategy: The "Too-Good-To-Be-True" Prior

Another powerful strategy involves modifying the training objective to directly discourage the use of shortcuts, as proposed in the "too-good-to-be-true" prior [30]. This approach posits that simple solutions (shortcuts) are unlikely to generalize across contexts. It uses a low-capacity network (LCN) as a shortcut detector to guide the training of a high-capacity network (HCN) [30].

Protocol for LCN-HCN Two-Stage Training

This protocol is adapted from general machine learning principles for out-of-distribution generalization and can be applied to AV model training [30].

Protocol 2: Two-Stage Shortcut Detection and Training

Objective: To train a high-capacity AV model that relies on deep, invariant stylistic features rather than superficial topic shortcuts. Inputs: Training dataset with text pairs and authorship labels. Outputs: A trained High-Capacity Network (HCN) for authorship verification.

Step-by-Step Methodology:

• Stage 1: Train the Low-Capacity Network (LCN)

  • Architecture Selection: Choose a simple, shallow neural network model (the LCN) with limited representational power.
  • Training: Train the LCN on the entire training dataset. Due to its low capacity, it is expected to primarily learn superficial, topic-specific shortcuts that are easy to model [30].
  • Inference & Scoring: Use the trained LCN to make predictions on the training set. Items (text pairs) on which the LCN achieves high confidence and accuracy are flagged as potentially solvable via shortcuts.

• Stage 2: Train the High-Capacity Network (HCN) with Down-Weighting

  • Architecture Selection: Choose a more complex, deeper neural network model (the HCN).
  • Modified Loss Function: During HCN training, down-weight the loss contribution of the training items that the LCN mastered. This forces the HCN to focus its capacity on learning from the more challenging examples that require deeper, invariant features (e.g., genuine writing style) [30]. A common implementation is to use a weighted loss function where the weight for item i is (1 - LCN_confidence_i).

Figure 2: Two-Stage Training to Avoid Shortcuts.

Experimental Validation and Benchmarking

Validating the effectiveness of any AV strategy requires rigorous cross-topic evaluation. The RAVEN benchmark, constructed using the HITS method, is designed for this purpose [29].

Protocol for Cross-Topic Evaluation on RAVEN

Protocol 3: Evaluating Shortcut Reliance with RAVEN

Objective: To assess an AV model's robustness against topic shifts and its reliance on topic-specific shortcuts. Inputs: A trained AV model; The RAVEN benchmark dataset [29]. Outputs: Model performance metrics (e.g., AUC, F1) and analysis of shortcut reliance.

Step-by-Step Methodology:

• Standard Cross-Topic Split: Evaluate the model on the standard RAVEN test set, which contains topics not seen during training.
• Topic Shortcut Test: Perform an additional test using the "topic shortcut" component of RAVEN. This test is specifically designed to expose models that rely on topic features.
• Performance Comparison:
  • Stable Ranking: A robust model should maintain consistent performance across different evaluation splits of RAVEN, demonstrating stable ranking relative to other models [29].
  • Low Shortcut Reliance: Compare the model's performance on the standard test versus the topic shortcut test. A significant performance drop in the shortcut test indicates high reliance on topic-specific features.

Table 2: Key Components of the RAVEN Benchmark

Component	Function in Evaluation
HITS-Sampled Dataset	Provides a topically heterogeneous dataset that minimizes topic leakage by design [29].
Cross-Topic Splits	Standard train/test splits with disjoint topics to simulate real-world topic shifts [29].
Topic Shortcut Test	A diagnostic test to specifically identify and quantify a model's dependence on topic-specific shortcuts [29].

The Scientist's Toolkit: Research Reagent Solutions

The following table details key resources and their functions for implementing the described strategies in a research setting.

Table 3: Essential Research Reagents for Cross-Topic AV Research

Research Reagent	Function & Application
Fanfiction Dataset (e.g., from PAN AV competitions)	A large-scale, topic-labeled corpus (over 4,000 topics) serving as a primary source for building cross-topic benchmarks and evaluating model robustness [29].
RAVEN Benchmark (Robust Authorship Verification bENchmark)	A benchmark comprising datasets with heterogeneous topic sets, created via HITS. It is used for stable model evaluation and includes a topic shortcut test [29].
Topic Modeling Tools (e.g., LDA, BERTopic)	Algorithms to quantify and represent topic content in a corpus, essential for calculating pairwise topic similarity in the HITS protocol [29].
Low-Capacity Network (LCN) Model	A shallow neural network used as a shortcut detector in the two-stage training protocol. It identifies training examples solvable via superficial features [30].
High-Capacity Network (HCN) Model	A deep neural network (e.g., Transformer-based) trained to learn deep, invariant stylistic features, often guided by the outputs of the LCN [30].
MMV009085	MMV009085, MF:C22H22N2O6, MW:410.4 g/mol

Framed within a thesis on methods for cross-topic authorship verification research.

Conceptual Framework and Rationale

A central challenge in authorship verification is the confounding influence of topic, where models often rely on semantic keywords rather than the fundamental, topic-agnostic stylistic fingerprint of an author. Text Distortion and Content Masking have emerged as powerful pre-processing techniques to mitigate this issue. These methods systematically remove or obfuscate topic-specific information from text, thereby forcing subsequent feature extraction and modeling to focus on stylistic elements such as syntax, punctuation, and other lexico-grammatical patterns [31].

The theoretical foundation is that an author's unique style is embedded in their consistent use of function words, syntactic structures, and other shallow features that persist regardless of what they are writing about. By applying distortion or masking, we intentionally create a "noisy" signal where topical content is degraded, making stylistic signals more salient for computational models [31] [9]. This approach is particularly vital for cross-topic authorship verification, where the training and test corpora do not share the same topics, and models that rely on topical cues are prone to failure [31]. Empirical evidence has confirmed that this method can enhance existing authorship attribution techniques, especially under these challenging cross-topic conditions [31].

Core Masking and Distortion Techniques

This section details the primary methodologies for implementing text distortion, categorized by their approach and target.

Table 1: Taxonomy of Core Text Distortion Techniques

Technique	Description	Primary Effect	Key Considerations
Token Masking (Term-Frequency Based) [31]	Replaces high-frequency, content-bearing nouns and verbs with a placeholder (e.g., [MASK]).	Directly removes topic-specific lexical items.	Relies on accurate POS-tagging; the masking threshold is a key parameter.
Random Token Masking [32] [33]	Randomly selects and masks a percentage of all input tokens.	Introduces noise to reduce over-reliance on any specific word.	Simpler to implement; masking rate must be tuned to avoid destroying all semantic meaning.
Span Masking [33]	Masks contiguous spans (sequences) of tokens rather than individual tokens.	Challenges the model to understand longer-range contextual and syntactic structures.	More computationally intensive; requires tuning of span length and quantity.
Text Distortion (Pre-processing) [31]	A general term for the step of altering text before feature extraction to mask topic-specific information.	Creates a modified text representation that is richer in stylistic than semantic information.	Serves as an umbrella term for various masking and obfuscation methods.

Protocol: Implementing Term-Frequency Based Token Masking

This protocol is adapted from the seminal work by Stamatatos (2017) [31].

Objective: To pre-process a corpus of text documents by masking topic-specific words, thereby creating a style-rich dataset for subsequent stylometric analysis.

Materials and Input:

• Text Corpus: A collection of documents in plain text format.
• Natural Language Processing (NLP) Toolkit: Such as spaCy or NLTK for tokenization and part-of-speech (POS) tagging.
• Computing Environment: Standard workstation with sufficient memory for corpus processing.

Procedure:

• Text Pre-processing: For each document in the corpus: a. Tokenize the text into words and sentences. b. Perform part-of-speech tagging on all tokens.
• Content Word Identification: Identify all nouns and verbs across the entire corpus.
• Frequency Analysis: Calculate the document frequency (DF) for each identified noun and verb.
• Masking Application: For each document, replace every noun and verb with a DF above a pre-defined threshold (e.g., the top 20% most frequent nouns and verbs in the corpus) with a universal placeholder token, such as [MASK].
• Output Generation: Save the masked versions of all documents to form a new, topic-agnostic corpus for model training and evaluation.

Quantitative Evaluation in Cross-Topic Scenarios

The effectiveness of text distortion is quantitatively measured by the performance gain in authorship verification tasks under cross-topic conditions. The following table synthesizes findings from key research, demonstrating the utility of these methods.

Table 2: Empirical Performance of Text Distortion for Authorship Verification

Research Context / Method	Evaluation Dataset	Key Metric	Performance without Distortion/Masking	Performance with Distortion/Masking	Notes & Implications
Authorship Attribution using Text Distortion [31]	Proprietary datasets (Cross-topic conditions)	Attribution Accuracy	Baseline performance of existing methods	Enhanced performance	Specifically improves effectiveness in cross-topic conditions where training and test topics differ.
Heterogeneity-Informed Topic Sampling (HITS) [9]	RAVEN benchmark (for AV)	Model Ranking Stability	Unstable model rankings due to topic leakage	More stable and reliable model rankings	HITS creates a robust evaluation set, revealing model reliance on topic-specific features.
Combining Style and Semantics [4]	Challenging, imbalanced, and stylistically diverse dataset	Model Performance (e.g., F1-Score)	N/A (Baseline not explicitly stated)	Competitive results achieved	Confirms the value of combining masked/style features with semantic features (RoBERTa) for robust AV.

Advanced and Emerging Protocols

Building upon basic masking, recent research explores more sophisticated paradigms.

Protocol: Distortion-Aware Contextual Learning (DCL) for Robustness

Inspired by advancements in vision-language models, DCL can be adapted for authorship tasks to improve model robustness [32].

Objective: To train a model that produces consistent stylistic representations regardless of topic-induced distortions, using a dual-path framework.

Workflow Diagram:

Dual-Path Training with Distillation

Procedure:

• Dual-Path Processing: For each training batch, process the original text through the primary path and a masked/distorted version of the same text through the secondary path.
• Feature Extraction & Prediction: Each path produces its own feature representation and final authorship prediction (e.g., same-author/different-author probability).
• Loss Calculation and Optimization: The total training loss is a weighted sum of:
  • The standard cross-entropy loss from the primary path.
  • A distillation loss (e.g., KL-divergence) that minimizes the difference between the predictions of the primary and secondary paths. This alignment encourages the model to be invariant to the applied distortions, thus focusing on core stylistic features [32].

Protocol: LLM-Based One-Shot Style Transfer (OSST) Scoring

This novel, unsupervised protocol uses Large Language Models (LLMs) for authorship verification by measuring style transferability, inherently masking semantic content [2].

Objective: To perform zero-shot authorship verification by quantifying how easily the style of a reference text can be transferred to a neutralized version of a target text.

Workflow Diagram:

OSST Score Calculation for Authorship Verification

Procedure:

• Neutralization: For a target text A, use an LLM prompt to generate a neutralized version that preserves semantic content but removes stylistic flair (e.g., "Rewrite the following text in a neutral, AP-news style").
• Style Transfer: Provide the LLM with a one-shot example demonstrating how to transfer the style from a reference text B to its own neutralized version. Then, task the LLM to apply the style of B to the neutralized version of A, producing a reconstructed text A*.
• OSST Score Calculation: The OSST score is the average log-probability the LLM assigns to the original text A, given the context of the neutralized A and the style of B. A higher score indicates that the style of B was more helpful in reconstructing A, suggesting a higher likelihood that A and B share the same author [2].
• Decision: A threshold is applied to the OSST score to make the final authorship verification decision.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Tools for Text Distortion Research

Item / Reagent	Function / Role in the Experimental Pipeline
Pre-trained Language Model (e.g., RoBERTa, BERT) [4]	Serves as a feature extractor for deep semantic representations that can be combined with stylistic features.
Large Language Model (e.g., GPT-family, LLaMA) [2] [34]	Core engine for advanced protocols like OSST scoring and for generating distorted or style-augmented text variants.
Standardized AV Datasets (e.g., PAN Clef) [2] [34]	Provides benchmark corpora for training and fair evaluation, often including cross-topic and cross-genre challenges.
NLP Library (e.g., spaCy, NLTK)	Provides the essential utilities for tokenization, part-of-speech tagging, and other linguistic pre-processing steps.
Stylometric Feature Set [4] [34]	A collection of hand-crafted features (e.g., character n-grams, syntactic patterns, vocabulary richness) used to quantify writing style.
Text Distortion Script [31]	Custom software that implements the specific masking algorithms (e.g., frequency-based masking, random masking).

Authorship verification (AV) is a critical task in computational linguistics with applications in identity verification, plagiarism detection, and AI-generated text identification [7]. Cross-discourse type authorship verification represents a particularly challenging scenario where systems must determine whether two texts are written by the same author when those texts belong to different discourse types (DTs), such as written language (e.g., essays, emails) and spoken language (e.g., interviews, speech transcriptions) [35]. This methodological framework addresses the significant stylistic variations that occur across different forms of communication, enabling more robust and generalizable authorship attribution.

The Aston 100 Idiolects Corpus provides a foundational dataset for this research, containing texts from approximately 100 individuals with similar demographic characteristics (age 18-22, native English speakers) across four discourse types: essays and emails (written discourse), and interviews and speech transcriptions (spoken discourse) [35]. This controlled dataset allows researchers to isolate discourse-related stylistic variations from other confounding factors, advancing methods for cross-domain authorship analysis.

Quantitative Evaluation Framework

Performance Metrics for Cross-Discourse AV

Cross-discourse authorship verification systems require multi-faceted evaluation using complementary metrics that assess different aspects of system performance. The PAN-CLEF 2023 evaluation framework employs five primary metrics that provide a comprehensive assessment of verification capabilities [35].

Table 1: Evaluation Metrics for Cross-Discourse Authorship Verification

Metric	Purpose	Interpretation	Advantages
AUC	Measures ranking capability of verification scores	Higher values indicate better separation of same-author and different-author pairs	Provides overall performance assessment independent of threshold selection
F1-score	Evaluates binary classification accuracy	Balances precision and recall for decided cases	Standard metric for classification performance
c@1	Measures accuracy while rewarding abstention	Rewards systems for leaving difficult cases unanswered (score = 0.5)	Handles uncertainty effectively; appropriate for realistic scenarios
F_0.5u	Emphasizes correct identification of same-author pairs	Puts more weight on deciding same-author cases correctly	Addresses practical need to minimize false negatives in verification
Brier	Evaluates calibration of probabilistic scores	Measures how well predicted probabilities reflect true probabilities	Assesses quality of confidence estimates, not just classification accuracy

These metrics collectively address the core challenges in cross-discourse AV: the need for reliable confidence estimation (Brier), the ability to handle uncertain cases (c@1), the practical requirement to correctly verify same-author pairs (F_0.5u), and overall discriminative power (AUC) [35].

Discourse Type Characteristics and Challenges

The cross-discourse verification task involves handling pairs of texts from different discourse types with distinct linguistic properties and structural characteristics.

Table 2: Discourse Type Characteristics in the Aston 100 Idiolects Corpus

Discourse Type	Category	Structural Features	Stylistic Challenges	Preprocessing Requirements
Essays	Written	Formal structure, complete sentences, organized paragraphs	High lexical diversity, complex syntax	Minimal beyond tokenization
Emails	Written	Concatenated messages with <new> tags, variable formality	Rapid topic shifts, inconsistent formatting	Message boundary detection, named entity replacement
Interviews	Spoken	Transcripts with nonverbal vocalization tags (<cough>, <laugh>)	Conversational patterns, disfluencies, interruptions	Handling vocalization tags, dialogue structure parsing
Speech Transcriptions	Spoken	Monologic structure, potential transcription artifacts	Oral discourse markers, repetition, simplification	Dealing with transcription inconsistencies, pause markers

The structural diversity across these discourse types necessitates specialized preprocessing approaches. For emails and interviews, which can contain very short text segments, the corpus employs concatenation with explicit boundary markers (<new> for email messages) [35]. Additionally, author-specific and topic-specific information has been replaced with standardized tags to prevent models from relying on extraneous content rather than stylistic features.

Experimental Protocols

Data Handling and Preprocessing Protocol

Protocol 1: Data Preparation and Sanitization

• Objective: Ensure consistent text representation across discourse types while preserving stylistic features.
• Materials: Raw text pairs from Aston 100 Idiolects Corpus with discourse type labels.
• Procedure:
  • Boundary Marking Preservation: Maintain all XML-style tags (<new>, <nl>, <cough>, <laugh>) during text extraction to preserve structural and paralinguistic information [35].
  • Text Concatenation Handling: For emails and interviews, treat concatenated messages as single text units while retaining segmentation markers for potential segment-level analysis.
  • Named Entity Sanitization: Replace author-specific and topic-specific named entities with standardized tags to prevent content-based cheating [35].
  • Text Normalization: Apply consistent lowercasing, punctuation preservation, and tokenization across all discourse types.
  • Length Filtering: Implement minimum length requirements (e.g., >100 words) or employ data augmentation techniques for very short texts.
• Quality Control: Manual verification of tag preservation across 5% of processed samples; statistical analysis of length distributions by discourse type.

Baseline System Implementation Protocol

Protocol 2: Character N-gram Similarity Baseline

• Objective: Establish a robust baseline using character-level stylometric features.
• Materials: Preprocessed text pairs, TFIDF vectorizer, cosine similarity metric.
• Procedure:
  • Feature Extraction:
    • Extract character 4-grams (character tetragrams) from each text [35].
    • Create TFIDF-weighted bag-of-character-tetragrams representations.
    • Apply L2 normalization to feature vectors.
  • Similarity Calculation:
    • Compute cosine similarity between text pairs in the feature space.
    • Formula: similarity = (A · B) / (||A|| × ||B||) where A and B are TFIDF vectors.
  • Score Calibration:
    • Use grid search on calibration data to find optimal similarity threshold.
    • Apply linear scaling to transform similarity scores to probability estimates in [0,1] range [35].
• Validation: Evaluate using 5-fold cross-validation on training data; assess metric consistency across discourse type combinations.

Protocol 3: Cross-Entropy Compression Baseline

• Objective: Leverage information-theoretic approaches for cross-discourse verification.
• Materials: Preprocessed text pairs, compression algorithm (PPM), logistic regression model.
• Procedure:
  • Model Building:
    • Build Prediction by Partial Matching (PPM) compression model for Text A [35].
    • Build PPM compression model for Text B.
  • Cross-Entropy Calculation:
    • Calculate cross-entropy of Text B using Text A's model: H(B|A)
    • Calculate cross-entropy of Text A using Text B's model: H(A|B)
  • Feature Extraction:
    • Compute mean cross-entropy: (H(B|A) + H(A|B)) / 2
    • Compute absolute difference: |H(B|A) - H(A|B)|
  • Classification:
    • Train logistic regression model using mean and difference features.
    • Output verification probability in [0,1] range.
• Validation: Compare performance against character n-gram baseline; analyze feature importance.

Cross-Discourse Model Training Protocol

Protocol 4: Discourse-Aware Feature Learning

• Objective: Develop models that explicitly account for discourse type variations.
• Materials: Text pairs with discourse type labels, neural architecture components.
• Procedure:
  • Discourse-Invariant Feature Extraction:
    • Implement shared encoder (BERT, CNN, or LSTM) for all discourse types.
    • Extract character-level, lexical, and syntactic features known to be stable across genres.
  • Discourse-Specific Normalization:
    • Learn discourse-specific projection layers that transform shared features.
    • Incorporate discourse type embeddings as conditioning signals.
  • Multi-Task Optimization:
    • Primary task: authorship verification (binary classification).
    • Auxiliary task: discourse type identification (multi-class classification).
    • Joint loss: L_total = λ1 * L_verification + λ2 * L_discourse
  • Domain Adaptation:
    • Apply gradient reversal layers to encourage discourse-invariant representations.
    • Use adversarial training to minimize discourse-type predictability from style features.
• Validation: Ablation studies to measure contribution of discourse-aware components; cross-validation across different discourse type pairs.

Visualization of Experimental Workflows

Cross-Discourse Authorship Verification System Architecture

Cross-Discourse AV System Architecture

Discourse-Type Pair Analysis Matrix

Discourse-Type Pair Complexity Matrix

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Materials for Cross-Discourse Authorship Verification

Research Reagent	Specifications	Function	Usage Notes
Aston 100 Idiolects Corpus	100 native English speakers (18-22 years); 4 discourse types; text pairs with same/different author labels [35]	Gold-standard dataset for cross-discourse AV; enables controlled evaluation	Request access via FoLD repository; restricted to research use only
Discourse-Type Annotations	JSONL metadata specifying discourse types (essays, emails, interviews, speech transcriptions) for each text [35]	Enables discourse-aware model training and cross-domain evaluation	Essential for ablation studies on discourse type influence
TFIDF Vectorizer	Character n-gram features (n=4 typically); L2 normalization; cosine similarity scoring [35]	Baseline feature extraction; character-level stylometric representation	Fast to compute; language-independent; effective baseline
PPM Compression Model	Prediction by Partial Matching; cross-entropy calculation between text pairs [35]	Information-theoretic approach; captures sequential dependencies	Computationally intensive; requires specialized libraries
Evaluation Metrics Suite	AUC, F1, c@1, F_0.5u, Brier score [35]	Comprehensive performance assessment across multiple dimensions	Preferable to single-metric evaluation; reveals different system strengths
Universal Stylometric Features	Function word frequencies, POS tag patterns, character n-grams, vocabulary richness measures [35]	Discourse-invariant style markers; stable across genres	Requires linguistic preprocessing; some features are language-specific
Neural Style Encoders	BERT-based, CNN, or LSTM architectures with domain adaptation components	Learning discourse-invariant representations	Computational resource intensive; requires GPU acceleration

Cross-discourse type authorship verification represents a significant advancement in stylometric research, addressing the critical challenge of generalizing authorship signals across different forms of written and spoken language. The protocols and frameworks outlined in these application notes provide researchers with standardized methodologies for developing and evaluating robust verification systems. The integration of discourse-aware modeling techniques with multi-faceted evaluation metrics enables more accurate assessment of true stylistic invariance, moving beyond domain-specific authorship analysis.

Future research directions should focus on expanding cross-lingual approaches [7], developing more sophisticated domain adaptation techniques, and addressing the unique challenges of spoken language transcription artifacts. As authorship verification technologies continue to evolve, the cross-discourse paradigm will play an increasingly important role in ensuring reliable attribution across diverse communication contexts.

Ensuring Data Security and Privacy with Local Model Deployment

The deployment of Large Language Models (LLMs) on local infrastructure—such as researcher workstations, institutional servers, or high-performance computing clusters—is often motivated by the paramount need for data security and privacy, particularly when handling sensitive research information. This approach ensures that proprietary data, such as experimental results or patient information, never leaves the controlled environment, mitigating risks associated with cloud-based services [36]. However, this strategy introduces a significant security paradox: while local deployment enhances data privacy by preventing exposure to external entities, it can simultaneously reduce model security. Research indicates that local, open-source models are often more susceptible to sophisticated attacks, such as prompt injection, than their larger, cloud-based "frontier" counterparts [37]. Their weaker reasoning capabilities and less robust safety alignment make them easier to exploit, creating a critical vulnerability within the research pipeline [37]. This document outlines application notes and protocols for researchers to leverage the privacy benefits of local models while implementing robust defenses against these emerging threats.

Quantitative Analysis of Local Model Vulnerabilities

Understanding the specific risks is the first step toward mitigation. Recent red-teaming exercises reveal quantitatively higher vulnerability rates for local models compared to frontier models when subjected to malicious prompts. The table below summarizes the success rates of two primary attack classes.

Table 1: Success Rates of Code Injection Attacks on Local LLMs

Attack Class	Mechanism	Objective	Reported Success Rate (Local LLMs)	Frontier Model Comparison
"Easter Egg" Backdoor	Malicious prompt disguised as a feature request (e.g., a hidden "easter egg") [37]	Plants a persistent backdoor (e.g., an RCE vulnerability) in the generated code for later exploitation [37]	Up to 95% [37]	Appears resistant in limited testing [37]
Immediate RCE via Cognitive Overload	Obfuscated malicious payload delivered after a series of rapid-fire questions to bypass safety filters [37]	Achieves immediate Remote Code Execution (RCE) on the developer's machine during the coding session [37]	43.5% [37]	Vulnerable, but at a lower success rate [37]

These attack vectors are particularly lethal because they exploit the model's core function—code generation—turning a research tool into a potential threat vector. A single successful compromise can lead to the theft of credentials, intellectual property, or sensitive data, and allow an attacker to move laterally across the research network [37].

Experimental Protocol for Secure Local Model Deployment

The following protocol provides a detailed methodology for establishing a secure research environment for local LLMs, focusing on preventing code injection and data exfiltration.

Aim

To deploy a local LLM for research assistance (e.g., code generation, data analysis script writing, literature summarization) while implementing a multi-layered defense strategy to mitigate security risks from prompt injection attacks.

Materials and Reagent Solutions

The following toolkit comprises the essential software and hardware components for a secure setup.

Table 2: Research Reagent Solutions for Secure Local LLM Deployment

Item Name	Function / Explanation	Example Solutions
Local LLM	The core model run on local hardware; chosen for data privacy but requiring security containment.	Llama, Mistral [36]
Containerization Platform	Provides an isolated, ephemeral environment for executing untrusted code generated by the LLM.	Docker, Podman
Static Analysis Tool	Scans LLM-generated code for dangerous patterns (e.g., eval(), exec(), suspicious network calls) before execution.	Semgrep, Bandit, CodeQL
AI-Native Data Security Platform	Discovers, classifies, and protects sensitive data automatically using machine learning, ensuring compliance and monitoring for leaks.	Cyera, Securiti, BigID [38]
Network Traffic Monitor	Detects and blocks anomalous outbound connections, a key indicator of data exfiltration or callback from a backdoor.	Wireshark (for analysis), host-based firewalls (for blocking)

Workflow and Defense-in-Depth Diagram

The following diagram visualizes the integrated, multi-layered security workflow for processing a local LLM request.

Step-by-Step Procedure

• Input and Generation:

  • The researcher submits a prompt to the locally deployed LLM (e.g., "Write a Python script to normalize the gene expression dataset in /mnt/lab_data/.").
  • The model generates the requested code or text.

• Static Analysis (The "First Look"):

  • Before any execution, the generated code is automatically passed to a static analysis tool.
  • The tool checks for dangerous patterns, such as:
    • Use of eval(), exec(), os.system, or similar functions.
    • Obfuscated code or encoded strings.
    • Suspicious network endpoints or IP addresses.
    • References to sensitive file paths or environment variables.
  • If a dangerous pattern is identified, the code is rejected, and the workflow restarts from step 1. The researcher is notified of the reason for rejection.

• Researcher Review and Approval:

  • Code that passes static analysis is presented to the researcher for manual review.
  • The researcher must explicitly approve the code before it can proceed to execution. This human-in-the-loop step is a critical last line of defense.

• Sandboxed Execution (The "Safe Lab"):

  • Upon researcher approval, the code is executed within a tightly controlled, ephemeral container (e.g., a Docker container).
  • This container should have:
    • No network access.
    • Read-only access to only the specific data files required for the task.
    • Limited CPU and memory resources.
  • The container is destroyed immediately after the execution is complete.

• Real-time Monitoring:

  • During sandboxed execution, system and network activity are monitored.
  • Any attempt by the code to make an outbound network call or access unauthorized resources is flagged as an anomaly.
  • If an anomaly is detected, the execution is terminated, the container is destroyed, and the workflow restarts. The event is logged for security analysis.

Contextualization within Cross-Topic Authorship Verification Research

The security protocols described above are not merely operational; they are foundational to the integrity of computational research, including cross-topic authorship verification. This field relies on the provenance and integrity of its datasets and models.

• Data Integrity and Model Provenance: Authorship verification research often utilizes large, curated datasets like the Million Authors Corpus, which contains textual chunks from numerous Wikipedia authors [7]. Training models on this data or using LLMs to assist in analysis requires guarantees that the data and models have not been tampered with. A compromised local LLM could subtly alter training data or model weights, leading to invalid research outcomes and conclusions. The sandboxing and monitoring protocols prevent such tampering during data preprocessing and model interaction phases.
• Protection of Intellectual Property: The methodologies, model architectures, and experimental code developed in authorship research are valuable intellectual property. The multi-layered defense strategy, particularly static analysis and network monitoring, directly protects these assets from theft or sabotage via a compromised AI assistant.
• Ensuring Reproducibility: Reproducibility is a cornerstone of scientific research. By securing the local LLM environment, researchers ensure that the code and analyses generated are solely the product of their prompts and have not been influenced by external malicious actors, thereby upholding the reproducibility of their experiments.

In conclusion, local model deployment offers a path to unparalleled data privacy for sensitive research. However, this path is fraught with a paradoxical security risk. By adopting the structured application notes, protocols, and defense-in-depth strategy outlined in this document, researchers and drug development professionals can confidently leverage the power of local LLMs while safeguarding their data, their systems, and the integrity of their scientific work.

Benchmarks, Metrics, and Comparative Analysis of State-of-the-Art Models

In cross-topic authorship verification, the core task is to determine whether two texts are written by the same author based on writing style, often under challenging conditions where topics differ between the verification pairs [39]. The performance of verification systems must be evaluated using a suite of complementary metrics that assess different aspects of model capability, as no single metric provides a complete picture. This protocol details the application and interpretation of five standardized evaluation metrics—AUC, F1, c@1, F_0.5u, and Brier score—within the PAN authorship verification framework, providing researchers with a comprehensive toolkit for rigorous model assessment [39].

Metric Definitions and Computational Methods

The following metrics provide a holistic assessment of a system's performance, measuring aspects from ranking ability and binary decision accuracy to probability calibration [39].

Table 1: Core Evaluation Metrics for Authorship Verification

Metric	Formal Definition	Interpretation	Computational Method
AUC	Area under the Receiver Operating Characteristic curve	Probability that a randomly chosen positive (same-author) pair is ranked higher than a randomly chosen negative (different-author) pair [40] [41].	sklearn.metrics.roc_auc_score(y_true, y_scores) [40]
F1-Score	Harmonic mean of precision and recall: ( F1 = 2 \times \frac{Precision \times Recall}{Precision + Recall} ) [40] [41]	Balanced measure of a model's precision and recall for the positive class [42].	sklearn.metrics.f1_score(y_true, y_pred_class) [40]
c@1	Variant of accuracy that rewards abstention (score = 0.5) on difficult cases [39].	Measures accuracy while accommodating non-decisions, reflecting real-world usability.	Official PAN evaluation script [39].
F_0.5u	Modified F0.5 measure that treats non-answers (score = 0.5) as false negatives [39].	Emphasizes correct identification of same-author cases while penalizing uncertainty.	Official PAN evaluation script [39].
Brier Score	Mean squared difference between predicted probability and actual outcome: ( \frac{1}{N}\sum{i=1}^{N}(yi - p_i)^2 ) [43].	Measures calibration quality of predicted probabilities (lower is better). Complement reported [39].	sklearn.metrics.brier_score_loss(y_true, y_scores)

Figure 1: Logical workflow for calculating the five standardized evaluation metrics from model predictions.

Experimental Protocol for Authorship Verification

Data Preparation and Annotation

• Dataset Acquisition: Obtain fanfiction.net datasets from PAN CLEF 2021 authorship verification task, available in small (for symbolic ML) and large (for deep learning) variants [39].
• Data Structure: Data is provided as newline-delimited JSON files (pairs.jsonl) containing text pairs with unique IDs and fandom metadata. Ground truth files (*_truth.jsonl) contain same/different author labels [39].
• Cross-Topic Partitioning: Ensure training and test sets contain distinct authors and topics (open-set verification) to evaluate generalization [39].

Model Training and Prediction

• Feature Engineering: Implement baseline methods such as TFIDF-weighted character n-gram cosine similarity or compression-based cross-entropy [39].
• Model Selection: Train and compare multiple approaches (e.g., traditional ML, neural networks) using cross-validation on the training set.
• Prediction Generation: For each text pair, output a verification score between 0 and 1, indicating the probability of same-author authorship. Systems may leave difficult cases unanswered by setting score = 0.5 [39].

Evaluation and Metric Calculation

• Score Extraction: Execute the official PAN evaluation script to compute all five metrics from the prediction file [39].
• Result Compilation: Record AUC, F1, c@1, F_0.5u, and Brier score for each model.
• Statistical Comparison: Rank models based on the arithmetic mean of all five metrics to determine overall performance [39].

Table 2: Example Results from PAN CLEF 2021 Authorship Verification Task

Team	Training Set	AUC	c@1	F1	F_0.5u	Brier	Overall Mean
boenninghoff21	Large	0.9869	0.9502	0.9524	0.9378	0.9452	0.9545
embarcaderoruiz21	Large	0.9697	0.9306	0.9342	0.9147	0.9305	0.9359
weerasinghe21	Large	0.9719	0.9172	0.915	-	-	-

The Scientist's Toolkit: Research Reagents and Solutions

Table 3: Essential Research Reagents for Authorship Verification Experiments

Item	Function/Specification	Example Usage
PAN Dataset	Fanfiction.net pairs (53k total) with fandom metadata; small/large training set variants [39].	Model training and benchmarking for cross-topic verification.
TFIDF Char N-grams	Baseline feature extraction: cosine similarity between TFIDF-weighted character tetragrams [39].	Establishing performance baseline; stylistic feature representation.
Compression Method	Baseline method calculating cross-entropy between texts using Prediction by Partial Matching [39].	Alternative baseline without manual feature engineering.
Evaluation Script	Official PAN metric calculator (AUC, F1, c@1, F_0.5u, Brier) [39].	Standardized performance assessment and result reproduction.
QLoRA Fine-Tuning	Efficient fine-tuning of LLMs for sequence classification [44].	Adapting large language models (e.g., Qwen3) for authorship tasks.

Metric Interpretation Guidelines

Figure 2: Interpretation framework for the five evaluation metrics, highlighting their distinct purposes and considerations.

• AUC should be prioritized when the overall ranking capability of the model is most important, as it evaluates the model's ability to assign higher scores to same-author pairs compared to different-author pairs across all possible thresholds [40]. Note that it can be less sensitive in highly imbalanced scenarios [40] [45].
• F1-score provides the most value when both false positives and false negatives carry significant cost and the classes are imbalanced [40] [42]. It becomes the metric of choice when correctly identifying the positive class (same-author) is crucial, but false alarms cannot be completely neglected.
• c@1 is particularly useful in real-world applications where some verification cases are inherently ambiguous, as it rewards systems that appropriately recognize their limitations rather than forcing incorrect binary decisions [39].
• F_0.5u should be emphasized when correctly identifying same-author cases is more critical than identifying different-author cases, as it weights precision twice as heavily as recall and treats non-answers as false negatives [39].
• Brier score provides critical insight when well-calibrated probability estimates are required for decision-making, as it directly measures the accuracy of probabilistic predictions [39] [43].

The comprehensive evaluation of authorship verification systems requires multiple complementary metrics, as each reveals different performance aspects. AUC assesses ranking capability, F1 evaluates binary decision balance, c@1 values knowing when to abstain, F_0.5u emphasizes same-author detection, and Brier score validates probability calibration. Together, this standardized metric suite enables robust comparison of verification systems in cross-topic authorship research, ensuring advances are measurable and reproducible within the research community.

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The PAN Evaluation Series represents a coordinated, community-driven effort to establish rigorous benchmarks and shared tasks for authorship analysis, with a significant focus on the challenging problem of Authorship Verification (AV). AV, the task of determining whether two texts were written by the same author, is a cornerstone of computational stylometry with applications in plagiarism detection, forensic investigation, and intellectual property attribution [4]. A central, unresolved challenge in this domain is achieving model robustness to topic variation and discourse shifts. Models that rely on topic-specific words or genre-conventions as discriminatory features often fail catastrophically when faced with texts from unfamiliar domains, a phenomenon that limits their real-world applicability [9] [5].

This document frames the PAN Evaluation Series within a broader thesis on cross-topic authorship verification research. It posits that robust AV methodologies must deliberately dissociate an author's unique stylistic fingerprint—their writerprint—from the content and genre of the text. The benchmarks and protocols detailed herein are designed explicitly to test and promote this dissociation, pushing the field beyond methods that leverage topic leakage and towards models capable of genuine stylistic generalization.

Benchmark Design and Core Principles

The design of the PAN Evaluation Series is guided by the principle of creating a challenging, realistic, and fair assessment environment that directly confronts the problem of topic-induced bias.

The Challenge of Topic Leakage

A fundamental issue in conventional AV evaluation is topic leakage, where topical overlap between training and test data provides models with a superficial shortcut, inflating performance metrics without demonstrating true stylistic understanding [9]. A model may correctly verify authorship not because it recognizes stylistic patterns, but because it associates certain vocabulary or phrases (e.g., "gradient descent," "convolutional layer") with authors who frequently write on machine learning. This leads to misleading performance estimates and unstable model rankings when the topic distribution shifts between evaluation runs [9].

The HITS Methodology: Heterogeneity-Informed Topic Sampling

To address this, the PAN series advocates for and implements the Heterogeneity-Informed Topic Sampling (HITS) methodology [9]. HITS is a data curation strategy designed to construct evaluation datasets with a controlled, heterogeneous distribution of topics.

• Objective: To create a compact yet challenging test set that minimizes topic leakage and ensures a more stable, reliable ranking of AV models.
• Process: Instead of assuming minimal topic overlap, HITS actively constructs the test split to contain a diverse and representative mix of topics that mirrors the heterogeneity of the underlying corpus. This forces models to generalize across topical boundaries.
• Outcome: Datasets constructed using HITS have been demonstrated to yield more stable model rankings across random seeds and evaluation splits, providing a more trustworthy assessment of model robustness [9].

The RAVEN Benchmark: Robust Authorship Verification bENchmark

Building on HITS, the PAN series includes the RAVEN benchmark, which is explicitly designed for a "topic shortcut test" [9]. RAVEN's primary function is to uncover and quantify AV models' reliance on topic-specific features rather than genuine stylistic markers, providing a dedicated tool for stress-testing model robustness against topic shifts.

Quantitative Benchmark Data

The following tables summarize key quantitative data from studies and models relevant to the cross-topic AV landscape, providing a basis for comparison.

Table 1: Performance Comparison of Authorship Analysis Methods on Diverse Datasets. This table synthesizes findings from a large-scale empirical evaluation, highlighting the performance of traditional and neural approaches across different data conditions [5].

Model Type	Example Model	Avg. Macro-Accuracy (7 AA Datasets)	Performance on AV Datasets	Key Characteristic
Traditional N-gram Model	-	76.50% [5]	Lower than BERT-based	Excels when authors have fewer words; relies on surface-level style features.
BERT-based Model	BERT, RoBERTa	66.71% [5]	Higher	Better with more words per author; can capture deeper semantic and syntactic features.
Authorship Verification (AV) Methods	-	Competitive with AA methods when applied with hard-negative mining [5]	Specifically designed for AV task	Often overlooked as baselines in AA papers.

Table 2: Impact of Style Feature Integration on a Robust AV Model. This table illustrates the performance gains achieved by a state-of-the-art approach that combines semantic and stylistic features on a challenging, imbalanced dataset [4].

Model Architecture	Base Components	Performance (Style Features Absent)	Performance (Style Features Integrated)	Interpretation
Feature Interaction Network	RoBERTa Embeddings	Lower	Improved [4]	Style features provide a consistent boost.
Pairwise Concatenation Network	RoBERTa Embeddings	Lower	Improved [4]	The extent of improvement varies by model architecture.
Siamese Network	RoBERTa Embeddings	Lower	Improved [4]	Combining semantics and style enhances real-world robustness.

Experimental Protocols

This section outlines detailed methodologies for key experiments cited in the PAN series, providing a reproducible blueprint for cross-topic AV research.

Protocol: Cross-Topic Evaluation with HITS

Objective: To evaluate the robustness of an AV model against topic shifts using the HITS methodology [9].

• Data Preprocessing:
  • Text Preparation: Collect a large corpus of texts with author labels and topic annotations. Perform standard text cleaning (lowercasing, removal of metadata/punctuation).
  • Topic Modeling: Apply a topic modeling algorithm (e.g., Latent Dirichlet Allocation) to the entire corpus to infer a set of latent topics for each document.
• HITS Dataset Construction:
  • Stratified Sampling: Instead of a simple random train/test split, employ HITS to create the test set. Sample documents to ensure the test set's topic distribution is heterogeneous and representative of the full corpus's diversity.
  • Topic Leakage Control: Explicitly check for and minimize the presence of dominant, overlapping topics between the training and test splits that could provide easy shortcuts.
• Model Training & Evaluation:
  • Training: Train the AV model on the standard training split. The model should learn to output a verification score for a pair of texts.
  • Testing: Evaluate the trained model on the HITS-constructed test set.
  • Metric Calculation: Calculate standard AV metrics such as AUC-ROC or F1 score. To assess evaluation stability, repeat the process with multiple different HITS-generated test splits and report the mean and standard deviation of the metrics.

Protocol: Integrating Style and Semantic Features

Objective: To implement an AV model that combines deep semantic representations with explicit stylistic features for improved cross-topic robustness [4].

• Feature Extraction:
  • Semantic Features: Pass input texts through a pre-trained language model like RoBERTa to generate contextualized semantic embeddings for each token. Use the [CLS] token embedding or mean-pooled token embeddings as the document-level semantic representation.
  • Style Features: Extract a vector of pre-defined stylistic features for each text. This vector should include:
    • Lexical: Average sentence length, word length distribution, vocabulary richness (e.g., Type-Token Ratio).
    • Syntactic: Part-of-speech tag frequencies, punctuation counts and ratios.
    • Structural: Paragraph length, use of capitalization.
• Feature Fusion:
  • Implement one of the following fusion architectures to combine the semantic (R) and style (S) feature vectors for a pair of documents (Doc_A, Doc_B):
    • Feature Interaction Network: Creates interaction features between R and S from both documents before making a decision.
    • Pairwise Concatenation Network: Concatenates the feature vectors [R_A, S_A, R_B, S_B] and feeds them into a classifier.
    • Siamese Network: Uses twin subnetworks to process each document's combined R and S features, comparing the resulting representations.
• Model Training:
  • Use a binary cross-entropy loss function, training the model to distinguish between same-author and different-author pairs.
  • Employ a dataset with a challenging, imbalanced, and stylistically diverse split to simulate real-world conditions and force the model to learn topic-agnostic features.

The logical workflow for constructing a robust, cross-topic benchmark and model as described in these protocols is summarized below.

Diagram 1: Workflow for building a cross-topic benchmark and model, integrating the HITS sampling method and multi-feature model architecture.

The Scientist's Toolkit: Research Reagent Solutions

The following table details key resources, datasets, and software tools essential for conducting research and experiments within the PAN Evaluation Series framework.

Table 3: Essential Research Reagents and Tools for Cross-Topic Authorship Verification.

Item Name	Type	Function / Application	Relevant Citation
Valla	Software Framework & Benchmark	Standardizes and benchmarks AA/AV datasets and evaluation metrics, enabling apples-to-apples comparisons between methods.	[5]
RAVEN	Specialized Benchmark	The "Robust Authorship Verification bENchmark" allows for a dedicated "topic shortcut test" to evaluate model robustness against topic shifts.	[9]
HITS	Methodology / Protocol	The "Heterogeneity-Informed Topic Sampling" protocol for creating evaluation datasets that minimize topic leakage and ensure stable model rankings.	[9]
Pre-trained Language Models (RoBERTa)	Model / Feature Extractor	Provides deep, contextualized semantic embeddings of text, serving as a base component for modern AV models.	[4]
Style Feature Set	Feature Set	A predefined set of stylistic markers (sentence length, punctuation, word frequency) used to augment semantic models and improve robustness.	[4]
Project Gutenberg Dataset	Data	A large-scale, publicly available text corpus useful for training and evaluating authorship analysis models.	[5]

Model Architectures and Signaling Pathways

The architectures of leading AV models can be conceptualized as pathways for processing and fusing information. The following diagram details the components and flow of a state-of-the-art model that integrates style and semantic features.

Diagram 2: Architecture of a robust AV model integrating semantic and stylistic features.

Application Notes

The Challenge of Topic Shortcuts in Authorship Verification

Within cross-topic authorship verification (AV), a core challenge is the propensity of models to rely on topic-based features rather than genuine stylometric signatures for attribution. This confounds the accurate assessment of an author's unique writing style, as a model may achieve high performance by simply recognizing thematic content present in both known and questioned texts, without learning the fundamental stylistic patterns of the author. The "RAVEN" (Robust Benchmark for Analogy and Verification via Entailed Nucleotides) framework, inspired by the principles of Raven's Progressive Matrices (RPM), is designed to systematically test and eliminate such shortcuts by enforcing relational reasoning over abstract attributes, thereby isolating true authorship signals from topical noise [46] [7].

RAVEN Principles for Shortcut Elimination

The RAVEN benchmark is architected around several key principles derived from its psychometric predecessors [46]:

• Attribute Bisection Tree (ABT): This answer-set generation algorithm ensures that distractor choices are context-dependent and cannot be eliminated through simple, context-independent statistical checks. This forces models to perform genuine contextual reasoning to identify the correct author [46].
• Compositional Generalization: By creating explicit generalization regimes that hold out specific rule-attribute combinations (e.g., a particular writing style marker in a specific thematic context) during training, RAVEN stringently evaluates whether models can recombine learned concepts in novel ways for out-of-distribution (OOD) testing [46].
• Stratified Rule Embedding: Problems require reasoning across multiple hierarchical levels—from individual lexical choices (cell-level) to sentence structures (row-level) and overall compositional logic (matrix-level). This multi-granularity approach prevents models from latching onto simplistic, single-level features [46].

Quantitative Benchmarking Insights

Empirical evaluations on RAVEN-style benchmarks reveal significant performance drops in models that rely on shortcut learning. The following table summarizes the performance of various model architectures, highlighting their vulnerability to OOD generalization when topic shortcuts are removed.

Table 1: Model Performance on RAVEN-Style Abstract Reasoning Benchmarks [47] [46]

Model / Architecture	Key Property	In-Distribution Accuracy (%)	Out-of-Distribution (OOD) Accuracy (%)
Transformer (seq-to-seq)	Token prediction	~92 – 98	~31 – 47 (on held-out rules)
CoPINet	Dual-path, contrastive, vision	High (specific value not stated)	30 – 41
CPCNet	Iterative perceptual-conceptual alignment	96 – 98	Significant drop (specific value not stated)
SRAN	Stratified rule embedding	~60 (on I-RAVEN)	Not stated
ARLC (Neuro-symbolic)	Bayesian abduction with entropy regularization	>88 (on I-RAVEN-X with heavy noise)	High robustness maintained

The data indicates that while contemporary models like Transformers can achieve high in-distribution scores, their accuracy can plummet to near-chance levels under OOD testing regimes that disrupt topic-based shortcuts [46]. Neuro-symbolic models like ARLC, which explicitly reason over disentangled rules, demonstrate superior robustness, a finding that directly informs optimal model selection for rigorous AV research [46].

Experimental Protocols

Protocol: Cross-Topic Authorship Verification with RAVEN

1. Objective: To evaluate an authorship verification model's robustness against topic shortcuts by testing its performance on texts where topical cues are decorrelated from authorial identity.

2. Materials & Dataset:

• The Million Authors Corpus (MAC): A cross-lingual and cross-domain dataset containing 60.08 million textual chunks from 1.29 million Wikipedia authors. Its scale and diversity make it ideal for creating train/test splits that systematically hold out specific topic-author combinations [7].
• I-RAVEN-X Framework: The symbolic benchmark generator, which introduces perceptual uncertainty (e.g., confounding attributes, smoothed value distributions) to simulate the noise and varied expression found in real-world texts [47].

3. Methodology:

• Step 1 - Data Splitting: Implement a topic-stratified split. For a given set of authors, ensure that the specific topics or domains present in the testing set are completely unseen for those same authors in the training set.
• Step 2 - Feature Extraction & Rule Abstraction: Instead of using raw text, preprocess the data from MAC into a RAVEN-compatible symbolic structure.
  • Attributes: Define a set of stylistic attributes (e.g., sentence_length, lexical_complexity, pos_tag_ratio_NN).
  • Rules: Define the abstract relations that govern how these attributes change or relate across text samples from the same author (e.g., Constant, Progression, Arithmetic) [46].
• Step 3 - Model Training & Evaluation:
  • Train the AV model on the training split of the symbolic problem set.
  • Evaluate the model on the held-out test set, specifically analyzing performance on problems involving unseen topic-rule combinations.
  • Metric: Primary metric is OOD Accuracy – the model's ability to correctly verify authorship when topical shortcuts are invalid.

Diagram 1: RAVEN-X Experimental Workflow for AV

Protocol: Testing Robustness to Perceptual Uncertainty

1. Objective: To assess how an AV model performs when authorial signals are obscured by noise and variation, mimicking real-world scenarios like paraphrasing or diverse writing contexts.

2. Methodology:

• Step 1 - Benchmark Generation: Use the I-RAVEN-X generator to create problems that include:
  • Confounding Attributes: Introduce stylistic attributes that are randomly sampled and do not contribute to the underlying "authorship rule." The model must learn to ignore these irrelevant features [47].
  • Non-degenerate Value Distributions: Smoothen the distributions of input values for key attributes, moving away from discrete, easily identifiable values to continuous, overlapping ranges that increase ambiguity [47].
• Step 2 - Model Testing: Benchmark the model on this noisy dataset. Compare its performance against a baseline test on a clean dataset (e.g., standard I-RAVEN).
• Step 3 - Analysis: Quantify the performance degradation. As shown in Table 2, even advanced Large Reasoning Models (LRMs) experience significant challenges when reasoning under uncertainty [47].

Table 2: Impact of Perceptual Uncertainty on Model Performance [47]

Model Type	Performance on Clean Data (Task Accuracy)	Performance with Uncertainty (Task Accuracy)	Performance Drop
Large Reasoning Models (LRMs)	High (e.g., ~80-84%)	Significantly Challenged	-61.8% (in task accuracy)
Neuro-symbolic (ARLC)	>88%	>88% (maintained with heavy noise)	Minimal

Diagram 2: Neuro-symbolic Model Architecture for Robust AV

The Scientist's Toolkit

Table 3: Essential Research Reagents for Cross-Topic Authorship Verification

Research Reagent	Function & Utility
I-RAVEN / I-RAVEN-X Generator	A procedural algorithm for generating benchmark problems that test systematic and robust abstract reasoning. It is the core tool for creating evaluations free from topic shortcuts. [47] [46]
The Million Authors Corpus (MAC)	A large-scale, cross-lingual, and cross-domain dataset providing the foundational text data necessary for training and evaluating AV models under realistic, shortcut-breaking conditions. [7]
Attribute Bisection Tree (ABT)	A distractor-generation algorithm that ensures answer choices are fair and cannot be eliminated via superficial, context-independent features, forcing genuine relational reasoning. [46]
Neuro-symbolic Architecture (e.g., ARLC)	A hybrid model combining a neural feature extractor with a symbolic, logic-based reasoning backend. It is particularly robust to perceptual noise and domain shift, making it a leading architecture for rigorous AV. [46]
Stratified Rule Embedding	A modeling technique that constructs rule representations at multiple levels of granularity (e.g., word, sentence, document), enabling interpretable and composable reasoning about authorship style. [46]

The Million Authors Corpus (MAC) represents a transformative resource for authorship verification (AV), a discipline critical to identity verification, plagiarism detection, and AI-generated text identification [7] [48]. A significant limitation has historically constrained progress in the AV field: the predominance of English-language datasets confined to single domains [7]. This restriction not only precludes analysis of model generalizability but also creates a perilous scenario where seemingly valid AV solutions may inadvertently rely on topic-based features rather than genuine, stylometric authorship signals [7] [8]. The MAC directly addresses these shortcomings by providing a massive, multilingual, and multi-domain dataset extracted from Wikipedia, enabling rigorous cross-lingual and cross-domain evaluation to ensure accurate analysis of model capabilities [7] [48]. This application note details the corpus's construction, quantitative characteristics, and experimental protocols for its utilization within cross-topic authorship verification research frameworks.

Corpus Construction and Characteristics

The MAC is constructed through a systematic, language-agnostic pipeline designed to extract high-quality, substantive textual contributions from Wikipedia's full revision history [48]. The dataset encompasses 60.08 million textual chunks contributed by 1.29 million authors across 60 languages, strategically selected based on content volume and editor activity to ensure robust analysis [7] [48]. To capture diverse writing styles and communicative purposes, the corpus incorporates four distinct Wikipedia namespaces, treated as separate domains: article pages (namespace 0), user pages (namespace 1), talk pages associated with articles (namespace 2), and talk pages associated with users (namespace 3) [48].

A multi-stage filtering process ensures data quality and stylistic richness. The pipeline retains only edits introducing a minimum number of contiguous words (α), dynamically adjusted per language to account for morphological differences (e.g., α=100 for English, α=85 for Russian) [48]. The corpus excludes edits exceeding 5α words to filter out large-scale content imports, and further cleaning steps remove tables, bot contributions (identified via username patterns), and mixed-language content [48]. Each retained text chunk is definitively linked to its author, enabling longitudinal and cross-context analysis. The final dataset contains over 560,000 authors contributing across multiple domains and over 250,000 authors writing in multiple languages, providing unprecedented opportunities for studying authorship invariance across linguistic and topical boundaries [48].

Table 1: MAC Dataset Composition by Wikipedia Namespace (Domain)

Namespace	Description	Text Chunks	Percentage (%)
0	Article Pages	Dominant	Primary
1	User Pages	Minor	Smaller
2	Talk Pages (Articles)	Significant	Secondary
3	Talk Pages (Users)	Minor	Smaller

Table 2: Top Language Statistics in MAC (from a total of 60 languages)

Language	Text Chunks	Authors	Cross-Domain Authors	Cross-Lingual Authors
English	Most Dominant	~	~	~
German	Significant	~	~	~
French	Significant	~	~	~
Russian	Significant	~	~	~
Total (All Languages)	60.08 Million	1.29 Million	>560,000	>250,000

Experimental Design and Evaluation Framework

The evaluation framework for MAC is designed to assess AV models across five fundamental research questions (RQs), with RQ4 and RQ5 uniquely enabled by MAC's cross-lingual and cross-domain structure [48]. The AV task is formulated as a similarity-based information retrieval problem: given a query text, the model must retrieve a candidate text written by the same author from a larger pool [48]. Evaluation employs Success@k, a standard IR metric measuring the proportion of queries where the correct author match appears in the top-k ranked candidates, with Success@1 serving as the primary metric for strict evaluation [48].

Core Research Questions

• RQ1 (In-Language, In-Domain): Assesses baseline performance where training and testing occur within the same language and domain [48].
• RQ2 (Out-of-Language): Evaluates model generalization to languages not encountered during training [48].
• RQ3 (Out-of-Domain): Tests model performance on domains (namespaces) not seen during training [48].
• RQ4 (Cross-Lingual Verification): Examines the ability to verify authorship when the two texts from the same author are in different languages [48].
• RQ5 (Cross-Domain Verification): Probes the capability to verify authorship when the two texts from the same author are from different Wikipedia domains [48].

Experimental Protocol

Dataset Splitting: MAC is reprocessed into query-candidate pairs for training, validation, and test sets. For each author, one positive pair is extracted, with hard positive selection based low SBERT similarity to minimize topic overlap [48]. Training and validation sets are restricted to domain 0 (article pages) to specifically evaluate out-of-domain generalization, and texts are limited to 300 words to reduce translation risks [48].

Model Categories: The framework evaluates two model categories:

• Off-the-Shelf IR Baselines: Including BM25 (lexical overlap-based) and SBERT (semantic similarity using pre-trained multilingual embeddings) [48].
• Fine-Tuned Authorship Representation Models: Such as SBERT_AV (fine-tuned on MAC with multiple negatives ranking loss) and SADIRI (incorporating hard negative mining) [48]. All fine-tuned models use multilingual base architectures (e.g., xlm-roberta-base) [48].

Evaluation Metrics: The primary metric is Success@1. Performance is assessed separately for each research question, with specific test sets constructed for RQ4 and RQ5 by pairing texts from the same author but across different languages or domains [48].

Figure 1: MAC Creation and Experimental Workflow

Research Reagent Solutions

Table 3: Essential Research Materials for MAC-Based Experiments

Reagent / Resource	Type	Function / Application	Example / Specification
Million Authors Corpus (MAC)	Dataset	Primary data for training and evaluating cross-lingual/cross-domain AV models	60.08M texts, 1.29M authors, 60 languages, 4 domains [7]
Pre-trained Language Models	Software	Provides foundational multilingual text representations	paraphrase-multilingual-mpnet-base-v2 (SBERT), xlm-roberta-base [48]
Information Retrieval Baselines	Algorithm	Establishes performance baselines without AV-specific tuning	BM25, SBERT (off-the-shelf) [48]
Fine-tuning Framework	Software	Adapts pre-trained models for authorship verification tasks	Multiple negatives ranking loss, hard negative mining (SADIRI) [48]
Evaluation Metrics	Metric	Quantifies model performance for comparison and validation	Success@1, Success@k [48]
Topic Leakage Mitigation	Methodology	Addresses confounding factor of topic features in AV	Heterogeneity-Informed Topic Sampling (HITS) [8]

Addressing Topic Leakage in Cross-Topic Evaluation

A critical challenge in AV evaluation is topic leakage, where seemingly robust model performance may actually stem from reliance on topic-specific features rather than genuine authorship style [8]. This concern is particularly relevant for MAC's cross-domain experiments. Conventional evaluation assumes minimal topic overlap between training and test data, but topic leakage in test data can cause misleading performance and unstable model rankings [8].

The Heterogeneity-Informed Topic Sampling (HITS) methodology addresses this by creating smaller datasets with heterogeneously distributed topic sets, reducing the effects of topic leakage and yielding more stable model rankings across random seeds and evaluation splits [8]. Researchers using MAC should incorporate HITS or similar techniques when constructing evaluation splits to ensure that measured performance reflects true authorship verification capability rather than topic matching.

Figure 2: Authorship Verification with Topic Assessment

The Million Authors Corpus represents a paradigm shift in authorship verification research, providing the first benchmark supporting large-scale, cross-lingual, and cross-domain authorship analysis beyond English and narrow domains [49]. Its scale and diversity enable researchers to develop and validate models that capture genuine authorship style invariant to topic and language, crucial for real-world applications where authors frequently write across multiple languages and genres.

For researchers operating within cross-topic authorship verification frameworks, MAC offers unprecedented opportunities to:

• Develop models resilient to topic variations through cross-domain training and evaluation.
• Create language-agnostic authorship representations through multilingual learning.
• Implement rigorous evaluation protocols that specifically probe for topic independence using methodologies like HITS [8].
• Advance applications in identity verification, plagiarism detection, and AI-generated text identification with more robust, generalizable models.

The baseline evaluations provided with MAC demonstrate substantial headroom for improvement, particularly for cross-lingual and cross-domain tasks [48], indicating fertile ground for future research. By adhering to the experimental protocols outlined in this document and leveraging MAC's unique characteristics, researchers can significantly advance the state of the art in robust, topic-invariant authorship verification.

Comparative Performance of Feature-Based, Neural, and Explainable Models

Within the domain of digital forensics and computational linguistics, cross-topic authorship verification (AV) presents a particularly challenging task: determining whether two texts were written by the same author when their topics differ [9]. The core challenge is to develop models that are sensitive to an author's unique stylistic signature while remaining invariant to topic-specific vocabulary and content [14]. This Application Note provides a structured comparison of three model families—traditional Feature-Based, modern Neural, and Explainable AI (XAI) models—evaluating their robustness and performance in cross-topic scenarios. The proliferation of deep learning models, while improving performance, often comes at the cost of interpretability, making it difficult to trust and debug these systems in high-stakes applications like cybersecurity and academic integrity [50] [51]. This document outlines detailed experimental protocols and provides a scientific toolkit to empower researchers in developing robust, explainable, and high-performing AV models.

Model Classifications and Performance Comparison

Authorship verification models can be broadly categorized into three families, each with distinct strengths and weaknesses for cross-topic analysis:

• Feature-Based Models: These models rely on hand-crafted stylistic features, such as character n-grams, function words, and syntactic markers. Their primary advantage is high intrinsic interpretability, as the features directly correspond to linguistic constructs [14] [4].
• Neural Models: This class includes deep learning architectures like Recurrent Neural Networks (RNNs) and Transformer-based models (e.g., BERT, GPT-2). They automatically learn feature representations from data, often achieving superior performance but operating as "black boxes" [14] [51].
• Explainable AI (XAI) Models: These are not always a separate model class but rather a set of techniques applied to interpret black-box models. XAI methods can be model-agnostic (e.g., LIME, SHAP), working with any underlying model, or model-specific (e.g., Grad-CAM for CNNs), leveraging the model's internal structure [50] [52].

The following table synthesizes the comparative performance of these model families based on current research, with a specific focus on their behavior in cross-topic conditions.

Table 1: Comparative Performance of Model Families in Cross-Topic Authorship Verification

Model Family	Key Example Models	Cross-Topic Robustness	Interpretability	Key Strengths	Key Limitations
Feature-Based	Character N-gram Models, Function Word Analysis	Moderate to High (when using topic-agnostic features) [14]	High (intrinsically interpretable)	• Resistance to topic bias• Computational efficiency• Well-understood features	• Performance ceiling• Requires manual feature engineering• May miss complex stylistic patterns
Neural	RNNs with MHC, BERT, RoBERTa, Siamese Networks [14] [4]	Variable (can be high with domain adaptation) [14]	Low (black-box)	• State-of-the-art accuracy• Automatic feature learning• Handles complex patterns	• Prone to learning topic leaks [9]• Requires large data volumes• Difficult to debug
XAI-Augmented	SHAP on GBTs, LIME on Neural Models, Grad-CAM [50] [53]	Dependent on the base model	High (post-hoc explanations)	• Insights into model decisions• Helps identify feature leakage• Builds trust in predictions	• Explanations can be approximate• Additional computational cost• Risk of misleading explanations [52]

A critical finding from recent studies is that neural models, despite their high performance, are susceptible to topic leakage, where the model leverages spurious topic correlations in the test data rather than genuine stylistic cues. This leads to inflated and unreliable performance metrics [9]. The Heterogeneity-Informed Topic Sampling (HITS) method has been proposed to create more robust evaluation datasets that mitigate this issue, leading to more stable model rankings [9].

Experimental Protocols for Cross-Topic Evaluation

Core Protocol: Benchmarking with HITS

This protocol is designed to evaluate model robustness against topic shifts while minimizing the effects of topic leakage.

• Objective: To reliably benchmark the performance of authorship verification models across different topics.
• Dataset:
  • Utilize a controlled corpus like the CMCC corpus, which contains texts from multiple authors across different genres (e.g., blog, email, essay) and topics (e.g., privacy rights, gender discrimination) [14].
  • Apply Heterogeneity-Informed Topic Sampling (HITS) to partition the data, ensuring the test set contains a heterogeneous distribution of topics not seen during training. This provides a more stable and truthful assessment of model performance [9].
• Procedure:
  • Data Preprocessing: Apply text normalization (lowercasing, punctuation/digit replacement with specific symbols) to reduce vocabulary sparsity [14].
  • Model Training:
    • For Feature-Based Models, train a classifier (e.g., SVM) on a training set with topics {T1, T2}.
    • For Neural Models, fine-tune a pre-trained language model (e.g., BERT) or train an RNN with a Multi-Headed Classifier (MHC) on the same training set [14].
  • Evaluation: Test all models on a held-out test set with topics {T3, T4, T5} that were excluded from training. Use metrics such as Accuracy, F1-score, and AUC-ROC.
  • Explanation (for XAI): Apply a model-agnostic explainer like SHAP to the best-performing model on individual text pairs to identify the most influential features for the verification decision [50] [53].

Diagram 1: HITS Evaluation Workflow - A robust protocol for cross-topic model benchmarking.

Advanced Protocol: Integrating Semantic and Stylistic Features

This protocol is based on findings that combining deep semantic representations with explicit stylistic features enhances model performance and provides a natural path for interpretation [4].

• Objective: To develop a high-performance, robust model by fusing semantic and stylistic information.
• Feature Extraction:
  • Semantic Features: Generate contextual embeddings for the text using a pre-trained model like RoBERTa [4].
  • Stylistic Features: Extract a set of explicit stylistic markers, including:
    • Surface features: Sentence length, word length distribution, punctuation frequency [4].
    • Lexical features: Function word ratios, character n-grams (especially those related to affixes and punctuation) [14].
• Model Architecture:
  • Implement a Siamese Network or Feature Interaction Network that takes both the semantic embeddings and the hand-crafted stylistic feature vector as inputs [4].
  • The network is trained to output a similarity score indicating whether the pair of texts shares an author.
• Validation:
  • Use the HITS benchmark to evaluate the fused model against baseline models that use only semantic or only stylistic features.
  • Apply XAI techniques to analyze the relative contribution of semantic vs. stylistic features to the final decision.

Diagram 2: Feature Fusion Architecture - Combining semantic and stylistic pathways.

The Scientist's Toolkit: Research Reagent Solutions

This section details essential "research reagents"—datasets, software, and algorithms—required for conducting rigorous cross-topic authorship verification research.

Table 2: Essential Research Reagents for Authorship Verification

Reagent Category	Specific Tool / Dataset	Function and Application
Benchmark Datasets	CMCC Corpus (Controlled Multi-Genre Corpus) [14]	Provides a controlled corpus with varied genres and topics, ideal for cross-domain and cross-topic evaluation.
	RAVEN Benchmark (Robust Authorship Verification bENchmark) [9]	A benchmark designed using HITS to minimize topic leakage, enabling a more stable and reliable ranking of AV models.
Pre-trained Models	RoBERTa, BERT [14] [4]	Provides powerful, contextual semantic embeddings as a base for neural models or as features in fusion architectures.
Explanation Frameworks	SHAP (SHapley Additive exPlanations) [50] [53]	A model-agnostic method to explain output by quantifying the contribution of each feature to the prediction.
	LIME (Local Interpretable Model-agnostic Explanations) [50] [52]	Explains individual predictions by approximating the black-box model locally with an interpretable one.
Stylometric Features	Character N-grams (esp. affixes/punctuation) [14]	A set of topic-agnostic features proven effective for cross-topic attribution, capturing author-specific stylistic habits.
	Surface/Syntactic Features (sentence length, function words) [4]	Explicit stylistic markers that can be combined with semantic vectors to improve performance and interpretability.
Evaluation Libraries	scikit-learn	Provides standard metrics (e.g., F1, AUC-ROC) and implementations for feature-based models and data preprocessing.

The pursuit of robust authorship verification in cross-topic scenarios necessitates a balanced approach that does not sacrifice interpretability for performance. While neural models, particularly those leveraging pre-trained language models and feature fusion, show state-of-the-art potential, they must be evaluated with robust benchmarks like HITS to prevent misleading results from topic leakage [9] [4]. The integration of Explainable AI (XAI) is no longer optional but a critical component for validating that models learn genuine stylistic patterns rather than spurious topic correlations. The experimental protocols and scientific toolkit detailed in this document provide a foundation for researchers to develop the next generation of trustworthy, high-performing, and robust authorship verification systems. Future work should focus on developing intrinsically explainable neural architectures and more sophisticated methods for explicitly disentangling style from topic during model training.

Conclusion

Cross-topic authorship verification has evolved from a simplistic attribution task to a nuanced verification paradigm, demanding models that discern genuine writing style from topical content. The synthesis of stylistic and semantic features within robust neural architectures, combined with rigorous evaluation on heterogeneous benchmarks like RAVEN and the Million Authors Corpus, is key to building systems resilient to topic shifts. Critical challenges such as topic leakage have been addressed through frameworks like HITS, ensuring more reliable model assessment. The future of AV lies in developing highly interpretable, secure, and polyglot systems that can be trusted in high-stakes environments like forensic analysis and AI-generated text detection. As these technologies mature, their application will be crucial for ensuring authenticity and accountability in biomedical literature, clinical trial documentation, and the broader digital ecosystem.

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