Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue

• URL: http://arxiv.org/abs/2605.12856v2
• Date: Thu, 14 May 2026 03:53:30 GMT
• Title: Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue
• Abstract summary: BOT-MOD is a framework that grounds detection in agent intent rather than traditional content level signals.<n>This work advances the foundation for scalable, intent-aware moderation of agents in open multi-agent environments.
• Score: 24.086774100072788
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The emergence of multi-agent systems introduces novel moderation challenges that extend beyond content filtering. Agents with 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 BOT-MOD (BOT-MODeration), a moderation framework that grounds detection in agent intent rather than traditional content level signals. BOT-MOD 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 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.

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