Abstract:The emergence of multi-agent systems introduces novel moderation challenges that extend beyond content filtering. Agents with {\em malicious intent} may contribute harmful content that appears benign to evade content-based moderation, while compromising the system through exploitative and malicious behavior manifested across their overall interaction patterns within the community. To address this, we introduce \textsc{\textbf{Bot-Mod}} (\textsc{\textbf{Bot-Mod}}eration), a moderation framework that grounds detection in agent intent rather than traditional content level signals. \method{} identifies the underlying intent by engaging with the target agent in a multi-turn exchange guided by Gibbs-based sampling over candidate intent hypotheses. This progressively narrows the space of plausible agent objectives to identify the underlying behavior. To evaluate our approach, we construct a dataset derived from Moltbook that encompasses diverse benign and malicious behaviors based on actual community structures, posts, and comments. Results demonstrate that \textsc{\textbf{Bot-Mod}} reliably identifies agent intent across a range of adversarial configurations, while maintaining a low false positive rate on benign behaviors. This work advances the foundation for scalable, intent-aware moderation of agents in open multi-agent environments.
| Subjects: | Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI) |
| Cite as: | arXiv:2605.12856 [cs.AI] |
| (or arXiv:2605.12856v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.12856 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ali Al Lawati [view email]
[v1]
Wed, 13 May 2026 01:04:16 UTC (1,209 KB)