MA$^{2}$P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion

2026-05-18 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

Researchers have developed MA$^{2}$P, a meta-cognitive autonomous intelligent agent framework designed to enhance complex persuasion in scenarios where a persuadee's internal states are not explicitly communicated. This framework addresses limitations in current approaches, which often yield generic responses, and tackles the performance variability of large language models (LLMs) across different domains. MA$^{2}$P features an autonomous multi-agent architecture that manages perception, infers latent mental states, executes strategies, maintains memory, and evaluates performance. To improve cross-domain consistency, it incorporates a meta-cognitive configurator that selects an optimal meta-strategy from a structured knowledge base, guiding subsequent reasoning and planning. Experimental results indicate that MA$^{2}$P achieves a higher persuasion success rate compared to existing baselines.

Key takeaway

For NLP Engineers developing persuasive dialogue systems, MA$^{2}$P offers a robust framework to overcome challenges in complex persuasion. Its ability to infer latent mental states and adapt strategies via a meta-cognitive configurator can significantly improve success rates. You should consider integrating similar multi-agent architectures and meta-strategy selection mechanisms to enhance the adaptability and effectiveness of your LLM-based persuasion applications, especially in domains requiring nuanced understanding of user intent.

Key insights

MA$^{2}$P is a meta-cognitive multi-agent framework improving complex persuasion by inferring latent states and adapting strategies.

Principles

• Persuasion requires interpreting latent mental states.
• Meta-strategies improve cross-domain performance.
• Multi-agent systems can coordinate complex tasks.

Method

MA$^{2}$P uses an autonomous multi-agent architecture for perception, mental-state inference, strategy execution, memory, and evaluation, guided by a meta-cognitive configurator selecting strategies from a knowledge base.

In practice

• Apply MA$^{2}$P for nuanced decision-making support.
• Use meta-strategies to guide LLM reasoning.
• Implement multi-agent systems for complex interactions.

Topics

• MA$^{2}$P Framework
• Meta-Cognitive Agents
• Complex Persuasion
• Multi-Agent Architecture
• Persuasive Dialogue

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.