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

Problem
This paper addresses the gap in the literature regarding effective persuasive dialogue generation, particularly in complex scenarios where the persuadee’s internal states are not explicitly communicated. Current methodologies often yield generic responses that lack grounding in the persuadee’s context, leading to suboptimal persuasion outcomes. The authors highlight that while large language models (LLMs) can generate persuasive content, their efficacy is inconsistent across different domains due to uneven knowledge coverage and limited reasoning capabilities. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors introduce MA$^{2}$P, a meta-cognitive autonomous intelligent agent framework designed for complex persuasion tasks. The architecture consists of multiple autonomous agents that collaboratively manage several key functions: perception management, mental-state inference, strategy execution, memory maintenance, and performance evaluation. A notable feature of MA$^{2}$P is the meta-cognitive configurator, which selects an appropriate meta-strategy from a structured knowledge base at the beginning of the interaction. This configurator is crucial for guiding the agents’ reasoning and planning processes, thereby enhancing the overall effectiveness of the persuasion strategy. The paper does not disclose specific details regarding the training compute or datasets used.

Results
Experimental evaluations demonstrate that MA$^{2}$P significantly outperforms baseline models in terms of persuasion success rates. The authors report a success rate improvement of approximately 15% over traditional LLM-based approaches on a custom-designed persuasion benchmark. This benchmark evaluates the agents’ ability to infer latent mental states and execute targeted strategies effectively. The results indicate that the meta-cognitive approach not only enhances the quality of generated responses but also improves the agents’ adaptability across different persuasive contexts.

Limitations
The authors acknowledge several limitations, including the potential for the meta-cognitive configurator to be overly reliant on the structured knowledge base, which may not encompass all relevant contexts. Additionally, the framework’s performance may degrade in highly dynamic environments where rapid adaptation is required. The paper does not address the computational efficiency of the multi-agent architecture, which could be a concern in real-time applications. Furthermore, the generalizability of the findings to other forms of dialogue beyond persuasion remains untested.

Why it matters
The MA$^{2}$P framework has significant implications for advancing the field of persuasive dialogue systems, particularly in applications such as negotiation, counseling, and behavior change interventions. By effectively inferring and responding to the persuadee’s latent mental states, this approach could lead to more nuanced and contextually relevant interactions. The integration of meta-cognitive strategies may also inspire future research into adaptive dialogue systems that can better handle complex human interactions across various domains. This work lays the groundwork for further exploration into the intersection of cognitive modeling and dialogue generation.

Authors: Dingyi Zhang, Ziqing Zhuang, Linhai Zhang, Ziyang Gao, Deyu Zhou
Source: arXiv:2605.18572
URL: https://arxiv.org/abs/2605.18572v1