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[2602.00851] Persuasion Propagation in LLM Agents
Summary
The paper explores how user persuasion affects the behavior of large language model (LLM) agents during long-horizon tasks, revealing that prior belief states significantly influence task execution outcomes.
Why It Matters
Understanding persuasion propagation in LLM agents is crucial as AI systems increasingly engage in autonomous tasks. This research highlights the importance of belief states in shaping agent behavior, which has implications for AI design and user interaction strategies.
Key Takeaways
- Persuasion can influence LLM agent behavior during task execution.
- On-the-fly persuasion shows weak and inconsistent effects.
- Explicit belief states at task time lead to fewer searches by agents.
- Behavior-level evaluation is essential for understanding agent performance.
- The study provides insights into improving AI interaction strategies.
Computer Science > Artificial Intelligence arXiv:2602.00851 (cs) [Submitted on 31 Jan 2026 (v1), last revised 15 Feb 2026 (this version, v2)] Title:Persuasion Propagation in LLM Agents Authors:Hyejun Jeong, Amir Houmansadr, Shlomo Zilberstein, Eugene Bagdasarian View a PDF of the paper titled Persuasion Propagation in LLM Agents, by Hyejun Jeong and 3 other authors View PDF HTML (experimental) 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. Comments: Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: ar...
