# Human-AI Collaborative Bot Detection in MMORPGs

## TL;DR

This paper introduces an unsupervised, explainable framework for detecting auto-leveling bots in MMORPGs by combining contrastive learning, clustering, and LLM-based validation to enhance detection accuracy and transparency.

## Contribution

It proposes a novel unsupervised detection method using contrastive learning and LLM validation, improving scalability and explainability in bot detection for MMORPGs.

## Key findings

- Effective unsupervised detection of leveling bots
- Enhanced explainability with growth curve visualization
- Improved moderation efficiency through collaborative validation

## Abstract

In Massively Multiplayer Online Role-Playing Games (MMORPGs), auto-leveling bots exploit automated programs to level up characters at scale, undermining gameplay balance and fairness. Detecting such bots is challenging, not only because they mimic human behavior, but also because punitive actions require explainable justification to avoid legal and user experience issues. In this paper, we present a novel framework for detecting auto-leveling bots by leveraging contrastive representation learning and clustering techniques in a fully unsupervised manner to identify groups of characters with similar level-up patterns. To ensure reliable decisions, we incorporate a Large Language Model (LLM) as an auxiliary reviewer to validate the clustered groups, effectively mimicking a secondary human judgment. We also introduce a growth curve-based visualization to assist both the LLM and human moderators in assessing leveling behavior. This collaborative approach improves the efficiency of bot detection workflows while maintaining explainability, thereby supporting scalable and accountable bot regulation in MMORPGs.