Metagent- P : A Neuro-Symbolic Planning Agent with Metacognition for Open Worlds (2025)

Abstract: The challenge of developing agents capable of open-world planning remains fundamental to artificial general intelligence (AGI). While large language models (LLMs) have made progress with their vast world knowledge, their limitations in perception, memory, and reliable reasoning still hinder LLM-based agents from achieving human-level performance in long-term tasks. Drawing inspiration from human cognitive-metacognitive collaboration, we propose Metagent-P , integrating the world knowledge of LLMs, the symbolic reasoning capabilities of cognitive architectures, and the self-reflection characteristic of metacognition to construct a “planning-verification-execution-reflection” framework. Metagent-P improves experience utilization through multimodal memory integration. It uses a neural-symbolic hierarchical representation structure to ensure the plan’s reasoning correctness in advance. Finally, it actively adapts the agent to dynamic environments through monitoring, evaluation, and regulation mechanisms. Experimental results show Metagent-P significantly outperforms current state-of-the-art methods in Minecraft. In long-term tasks, Metagent-P reduces the average replanning counts by 34% and exceeds the average human success rate by 18.96% . Additionally, Metagent-P also demonstrates self-evolution through step-by-step open-world exploration.

## introduction planning @xcite in open-world environments remains a key challenge in artificial intelligence. recent advances in large language models (llms) @xcite show promising potential through their world knowledge and language processing capabilities in open-world planning (baai, 2023; wang et al., * equal contribution. human cognition theory suggests a solution through dual cognitive-metacognitive mechanisms. the cognitive domain @xcite primarily handles task decomposition and action planning, while the metacognition domain @xcite monitors and optimizes these processes, enabling adaptive planning in changing environments. cognitive architectures (cas) @xcite simulate human cognition through semantic memory (general knowledge), episodic memory (past experiences), and procedural memory (skills knowledge stored as condition-action rules). procedural memory drives cas' core symbolic reasoning by matching rules with current states and inferring actions to generate reliable plans @xcite . however, their dependence on pre-encoded domain knowledge limits knowledge acquisition and generalization in open-world environments @xcite . moreover, with increasing task complexity and environmental uncertainty, basic cognitive capabilities become insufficient @xcite . human higher-order metacognition @xcite can address this by monitoring, evaluating, and regulating cognitive processes, enhancing adaptability by detecting failures, and adjusting strategies for more flexible performance in uncertain environments. based on our analysis ( feature cas llms human symbolic reasoning ++ -+ ++ perception ++ -++ memory accumulation ++ -++ language processing -+ ++ -+ world knowledge -+ ++ -+ self-reflection (metacognition) --+ ++ to address these limitations, we propose metagent-p, a neuro-symbolic planning agent with metacognition, consisting of four components: planner, verifier, controller, and reflector. as shown in figure 1 , it combines the world knowledge of llms, the symbolic reasoning capabilities of cas, and the self-reflection characteristic of metacognition, creating a "planning-verification-executionreflection" framework for open-world tasks. to improve the agent's situation awareness and experience utilization, we propose an experience-enhanced planner with multimodal memory that encodes both successful and failed cases. successful cases guide task decomposition, while failed cases inform the reflector's decision-making. to preemptively detect reasoning errors and improve efficiency, we design a symbol-reasoning driven verifier with a neuro-symbolic knowledge hierarchical representation structure. the top layer encodes explicit rules for reliable reasoning, while the bottom layer embeds llms for implicit world knowledge. through novel knowledge representation, it enhances verifier reasoning. the verifier achieves autonomous rule learning via bidirectional top-down guidance and bottom-up learning, without any manual rule definition. we design a metacognitive reflector inspired by human metacognition that enhances the agent's adaptability through three steps: (1) monitoring: detects execution anomalies and dynamically triggers evaluation. (2) evaluation: similar to human self-examination, it evaluates plan rationality with quantitative scoring, by considering current observation and past experience. (3) regulation: optimizes plans based on evaluation feedback to enable agent adaptation to dynamic environments. we evaluate metagent-p in minecraft, a popular open-world game environment, showing significantly outperforms current state-of-the-art methods. in long-term tasks, it has a 2 to 25 times performance improvement, reduces the average replanning counts by 34%, and exceeds the human success rate by 18.96%. through step-by-step exploration, metagent-p demonstrates self-evolution by actively adapting to dynamic environments. the main contributions of this paper are: ## method in this section, we first give an overview of our metagent-p. next, we present detailed discussions of metagent-p's key components: the experience-enhanced planner, the symbolic reasoning-driven verifier, and the metacognitive reflector. ## conclusion we propose metagent-p for open-world planning, consisting of four components: planner, verifier, controller, and reflector. it integrates the world knowledge of llms, the symbolic reasoning capabilities of cognitive architectures, and the self-reflection characteristic of metacognition, forming a "planning-verification-executionreflection" framework. experimental results show that metagent-p outperforms current state-of-theart agents across 50 minecraft tasks. in longterm tasks, it improves performance by 2 to 25 times, reduces average replanning counts by 34%, and exceeds the average human success rate by 18.96%. through step-by-step exploration, metagent-p demonstrates self-evolution, achieving more reliable and efficient adaptive planning. metagent-p's symbol-neural integration and metacognitive enhancement offer valuable insights for artificial general intelligence research and applications, bringing us closer to human intelligence.