Persuasion Propagation in LLM Agents

• URL: http://arxiv.org/abs/2602.00851v1
• Date: Sat, 31 Jan 2026 18:33:14 GMT
• Title: Persuasion Propagation in LLM Agents
• Authors: Hyejun Jeong, Amir Houmansadr, Shlomo Zilberstein, Eugene Bagdasarian,
• Abstract summary: We study how belief-level intervention can influence downstream task behavior.<n>Across web research and coding tasks, we find that on-the-fly persuasion induces weak and inconsistent behavioral effects.<n>When the belief state is explicitly specified at task time, belief-prefilled agents conduct on average 26.9% fewer searches and visit 16.9% fewer unique sources than neutral-prefilled agents.
• Score: 23.64887423923855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern AI agents increasingly combine conversational interaction with autonomous task execution, such as coding and web research, raising a natural question: what happens when an agent engaged in long-horizon tasks is subjected to user persuasion? We study how belief-level intervention can influence downstream task behavior, a phenomenon we name \emph{persuasion propagation}. We introduce a behavior-centered evaluation framework that distinguishes between persuasion applied during or prior to task execution. Across web research and coding tasks, we find that on-the-fly persuasion induces weak and inconsistent behavioral effects. In contrast, when the belief state is explicitly specified at task time, belief-prefilled agents conduct on average 26.9\% fewer searches and visit 16.9\% fewer unique sources than neutral-prefilled agents. These results suggest that persuasion, even in prior interaction, can affect the agent's behavior, motivating behavior-level evaluation in agentic systems.

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