arXiv.org / 2026

Overview of PAN 2026: Voight-Kampff Generative AI Detection, Text Watermarking, Multi-Author Writing Style Analysis, Generative Plagiarism Detection, and Reasoning Trajectory Detection

Janek Bevendorff, Maik Frobe, André Greiner-Petter, Andreas Jakoby, Maximilian Mayerl, Preslav Nakov, Henry Plutz, Martin Potthast, Benno Stein, Minh Ngoc Ta, Yuxia Wang, Eva Zangerle

AI SafetyGenerative AIPopular and Landmark Papers

The goal of the PAN workshop is to advance computational stylometry and text forensics via objective and reproducible evaluation. In 2026, we run the following five tasks: (1) Voight-Kampff Generative AI Detection, particularly in mixed and obfuscated authorship scenarios, (2) Text Watermarking, a new task that aims to find new and benchmark the robustness of existing text watermarking schemes, (3) Multi-author Writing Style Analysis, a continued task that aims to find positions of authorship change, (4) Generative Plagiarism Detection, a continued task that targets source retrieval and text alignment between generated text and source documents, and (5) Reasoning Trajectory Detection, a new task that deals with source detection and safety detection of LLM-generated or human-written reasoning trajectories. As in previous years, PAN invites software submissions as easy-to-reproduce Docker containers for most of the tasks. Since PAN 2012, more than 1,100 submissions have been made this way via the TIRA experimentation platform.

0 citations0 influential

Full paper

Read the original paper

Source page

A direct open-access PDF is not available in the database yet. Use the source page or learning resources below to open the complete paper from the publisher or index.

Learning resources

Google Scholar referencesGoogle Scholar Papers with Code searchPapers with Code Semantic Scholar paper pageSemantic Scholar YouTube explanationsYouTube

Reading state

Discuss in ChatGPT

Uses your own ChatGPT account. The paper context is copied into a tutor prompt before ChatGPT opens.

Preview prompt

You are my AI/ML research paper instructor. I want to deeply understand the paper below.

First, teach it in layers:
1. One-paragraph intuition.
2. Problem statement and why it mattered.
3. Key method, architecture, or algorithm.
4. Important equations or mechanisms, explained intuitively.
5. Experiments and evidence.
6. Limitations, assumptions, and failure modes.
7. How this paper influenced later AI/ML/Deep Learning/GenAI work.
8. A 30-minute study plan with checkpoints.
9. Quiz me with 5 questions and wait for my answers.

When something is not available in the attached context, say what is missing and infer carefully.

### Paper attached as context
Title: Overview of PAN 2026: Voight-Kampff Generative AI Detection, Text Watermarking, Multi-Author Writing Style Analysis, Generative Plagiarism Detection, and Reasoning Trajectory Detection
Authors: Janek Bevendorff, Maik Frobe, André Greiner-Petter, Andreas Jakoby, Maximilian Mayerl, Preslav Nakov, Henry Plutz, Martin Potthast, Benno Stein, Minh Ngoc Ta, Yuxia Wang, Eva Zangerle
Year: 2026
Venue: arXiv.org
Categories: AI Safety, Generative AI, Popular and Landmark Papers
Citations: 0
Paper URL: https://www.semanticscholar.org/paper/fc239d57f47fb2c0d15e57d8097c1879180e6930
Open PDF: Not available

Abstract:
The goal of the PAN workshop is to advance computational stylometry and text forensics via objective and reproducible evaluation. In 2026, we run the following five tasks: (1) Voight-Kampff Generative AI Detection, particularly in mixed and obfuscated authorship scenarios, (2) Text Watermarking, a new task that aims to find new and benchmark the robustness of existing text watermarking schemes, (3) Multi-author Writing Style Analysis, a continued task that aims to find positions of authorship change, (4) Generative Plagiarism Detection, a continued task that targets source retrieval and text alignment between generated text and source documents, and (5) Reasoning Trajectory Detection, a new task that deals with source detection and safety detection of LLM-generated or human-written reasoning trajectories. As in previous years, PAN invites software submissions as easy-to-reproduce Docker containers for most of the tasks. Since PAN 2012, more than 1,100 submissions have been made this way via the TIRA experimentation platform.

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Overview%20of%20PAN%202026%3A%20Voight-Kampff%20Generative%20AI%20Detection%2C%20Text%20Watermarking%2C%20Multi-Author%20Writing%20Style%20Analysis%2C%20Generative%20Plagiarism%20Detection%2C%20and%20Reasoning%20Trajectory%20Detection)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=Overview%20of%20PAN%202026%3A%20Voight-Kampff%20Generative%20AI%20Detection%2C%20Text%20Watermarking%2C%20Multi-Author%20Writing%20Style%20Analysis%2C%20Generative%20Plagiarism%20Detection%2C%20and%20Reasoning%20Trajectory%20Detection)
- Semantic Scholar: Semantic Scholar paper page (https://www.semanticscholar.org/paper/fc239d57f47fb2c0d15e57d8097c1879180e6930)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Overview%20of%20PAN%202026%3A%20Voight-Kampff%20Generative%20AI%20Detection%2C%20Text%20Watermarking%2C%20Multi-Author%20Writing%20Style%20Analysis%2C%20Generative%20Plagiarism%20Detection%2C%20and%20Reasoning%20Trajectory%20Detection+paper+explained)