Moderation by Interrogation: Uncovering Concealed Intent in Agent Social Networks

The paper introduces Bot‑Mod, a moderation framework that detects malicious intent in multi‑agent systems by interrogating agents with diagnostic probes and updating a Bayesian posterior over candidate intents. Bot‑Mod starts with a content‑only prior and refines its inference using moderator‑assigned likelihoods until a stopping criterion is reached. Evaluations on the Molt‑Intent dataset, an external social engineering dataset, and three agent LLMs show that Bot‑Mod outperforms content‑only moderators, a fine‑tuned classifier, and judge‑based interrogators, achieving higher F1 scores both in‑distribution and out‑of‑distribution.

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• llms
• agents
• fine-tuning