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

• URL: http://arxiv.org/abs/2605.18572v1
• Date: Mon, 18 May 2026 15:53:12 GMT
• Title: MA$^{2}$P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion
• Abstract summary: We propose MA$2$P, a meta-cognitive autonomous intelligent agent framework for complex persuasion.<n>Specifically, we develop an autonomous multi-agent architecture that coordinates perception management, mental-state inference, strategy execution, memory maintenance, evaluation.<n>Our approach achieves a higher persuasion success rate than baselines.
• Score: 34.431254321873794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Persuasive dialogue generation plays a vital role in decision-making, negotiation, counseling, and behavior change, yet it remains a challenging problem. In complex persuasion where the persuadee's internal states are not expressed clearly, the persuader must interpret responses, infer the persuadee's latent mental states (e.g., beliefs and desires), and translate them into targeted, strategy-consistent actions; however, current approaches often produce generic or weakly grounded responses even when such cues are identified. Moreover, although large language models (LLMs) can generate persuasive content, their performance varies substantially across domains due to uneven knowledge coverage and limited reasoning generalization. To address these challenges, we propose MA$^{2}$P, a meta-cognitive autonomous intelligent agent framework for complex persuasion. Specifically, we develop an autonomous multi-agent architecture that coordinates perception management, mental-state inference, strategy execution, memory maintenance, and performance evaluation. To mitigate cross-domain performance variation, we further design a meta-cognitive configurator that selects an appropriate meta-strategy from a structured knowledge base at the outset, thereby guiding subsequent reasoning and planning. Experimental results show that our approach achieves a higher persuasion success rate than baselines.

Related papers

• Think Thrice Before You Speak: Dual knowledge-enhanced Theory-of-Mind Reasoning for Persuasive Agents [11.724088361457419]
  We introduce the ToM-based Persuasive Dialogue (ToM-PD) task, grounded in the Belief-Desire-Intention (BDI) framework.<n>We also propose Think Thrice Before You Speak (TTBYS), a knowledge-enhanced stepwise reasoning framework.
  arXiv  Detail & Related papers  (2026-05-21T15:15:51Z)
• A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms [49.66022971508878]
  We argue that reasoning should be elevated from a modular component to the system's cognitive core.<n>We derive and systematize seven core reasoning challenges, such as the responsiveness-reasoning trade-off and social-game reasoning.<n>We identify a fundamental and unresolved tension between the high-latency, deliberative nature of LLM-based reasoning and the millisecond-scale, safety-critical demands of vehicle control.
  arXiv  Detail & Related papers  (2026-03-11T07:40:53Z)
• Enhancing Persuasive Dialogue Agents by Synthesizing Cross-Disciplinary Communication Strategies [5.729038915697267]
  We develop a framework for designing persuasive dialogue agents that draws on proven strategies from social psychology, behavioral economics, and communication theory.<n>We validated our proposed framework through experiments on two distinct datasets.
  arXiv  Detail & Related papers  (2026-02-26T07:18:45Z)
• Agentic Reasoning for Large Language Models [122.81018455095999]
  Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making.<n>Large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, but struggle in open-ended and dynamic environments.<n>Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction.
  arXiv  Detail & Related papers  (2026-01-18T18:58:23Z)
• Real-Time Reasoning Agents in Evolving Environments [52.21796134114843]
  We introduce real-time reasoning as a new problem formulation for agents in evolving environments.<n>Our work establishes real-time reasoning as a critical testbed for developing practical agents.
  arXiv  Detail & Related papers  (2025-11-07T00:51:02Z)
• LLMs as Strategic Agents: Beliefs, Best Response Behavior, and Emergent Heuristics [0.0]
  Large Language Models (LLMs) are increasingly applied to domains that require reasoning about other agents' behavior.<n>We show that current frontier models exhibit belief-coherent best-response behavior at targeted reasoning memorization.<n>Under increasing complexity, explicit recursion gives way to internally generated rules of choice that are stable, model-specific, and distinct from known human biases.
  arXiv  Detail & Related papers  (2025-10-12T21:40:29Z)
• Disagreements in Reasoning: How a Model's Thinking Process Dictates Persuasion in Multi-Agent Systems [49.69773210844221]
  This paper challenges the prevailing hypothesis that persuasive efficacy is primarily a function of model scale.<n>Through a series of multi-agent persuasion experiments, we uncover a fundamental trade-off we term the Persuasion Duality.<n>Our findings reveal that the reasoning process in LRMs exhibits significantly greater resistance to persuasion, maintaining their initial beliefs more robustly.
  arXiv  Detail & Related papers  (2025-09-25T12:03:10Z)
• Must Read: A Systematic Survey of Computational Persuasion [60.83151988635103]
  AI-driven persuasion can be leveraged for beneficial applications, but also poses threats through manipulation and unethical influence.<n>Our survey outlines future research directions to enhance the safety, fairness, and effectiveness of AI-powered persuasion.
  arXiv  Detail & Related papers  (2025-05-12T17:26:31Z)
• The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends [23.68567141617251]
  We present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies. We propose a new system architecture by incorporating the formalized definition to lay the foundation of CogAgent.
  arXiv  Detail & Related papers  (2024-02-07T07:28:34Z)
• From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning [66.98861219674039]
  Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
  arXiv  Detail & Related papers  (2023-10-24T19:46:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.