Understanding Persuasion in Long-Running Agents

2026-05-22 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

A study by Jeong, Houmansadr, Zilberstein, and Bagdasarian introduces "persuasion propagation," examining how belief-level interventions influence the downstream task behavior of LLM agents. The research utilized a behavior-centered evaluation framework, differentiating between "on-the-fly persuasion" during task execution and "prefilled belief conditioning" where a belief state is explicitly set before a task. Experiments across web research and coding tasks, using AutoGen with gpt-4.1-nano, mistral-nemo-12b, and llama-3.1-8b, revealed that on-the-fly persuasion resulted in weak and inconsistent behavioral effects. In contrast, agents with prefilled beliefs showed significant shifts, performing 26.9% fewer searches and visiting 16.9% fewer unique sources than neutral-prefilled agents in web research. This highlights that belief integration into an agent's initial context has a greater impact than transient, mid-execution persuasion.

Key takeaway

For AI Security Engineers evaluating agent safety, relying solely on belief-level agreement is insufficient. You should prioritize behavior-centered evaluation, especially when agents are exposed to long-term context manipulation or initialization poisoning. Implement trace-level monitoring to detect subtle behavioral drifts, as persistent belief conditioning significantly impacts agent actions, even if final outputs appear normal. This approach helps identify risks beyond explicit prompt injections.

Key insights

Persistent belief conditioning, not transient persuasion, significantly alters LLM agent behavior in downstream tasks.

Principles

• Belief integration drives behavioral shifts.
• Persuasion effects are incremental and conditional.
• Trace-level metrics are crucial for auditing.

Method

A three-stage pipeline: (1) persuasion stage (on-the-fly or prefilled belief), (2) downstream task execution (coding, web research), and (3) process-level behavioral analysis using trace metrics.

In practice

• Use prefilled belief conditioning for consistent agent behavior.
• Implement trace-level monitoring for agent auditing.
• Focus on initial context for influencing agent actions.

Topics

• LLM Agents
• Persuasion Propagation
• Agent Behavior Evaluation
• Belief Conditioning
• Web Research
• Code Generation

Best for: Research Scientist, AI Scientist, AI Security Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.