🤖 AI Summary
Existing LLM-based multi-agent systems perform well on isolated tasks but lack key cognitive capabilities inherent in human teams—namely, knowledge sharing, recursive reasoning, structured critical evaluation, and theory of mind (ToM)-driven mental state inference—hindering high-order cognitive collaboration. This paper proposes a novel framework integrating adaptive ToM with systematic critical assessment: it dynamically models agents’ beliefs and intentions, supports multi-level recursive perspective-taking, and embeds formal logical flaw detection and bias mitigation mechanisms to enable deep inter-agent collaboration. Experiments demonstrate significant improvements in reasoning coherence, knowledge integration efficiency, and collective rigor across complex decision-making tasks, outperforming baseline models on multiple collaborative reasoning benchmarks. The core contribution is the first unified modeling of evolvable ToM and formal critical evaluation, establishing a new paradigm for building multi-agent systems capable of genuine cognitive synergy.
📝 Abstract
Recently, the field of Multi-Agent Systems (MAS) has gained popularity as researchers are trying to develop artificial intelligence capable of efficient collective reasoning. Agents based on Large Language Models (LLMs) perform well in isolated tasks, yet struggle with higher-order cognition required for adaptive collaboration. Human teams achieve synergy not only through knowledge sharing, but also through recursive reasoning, structured critique, and the ability to infer others' mental states. Current artificial systems lack these essential mechanisms, limiting their ability to engage in sophisticated collective reasoning. This work explores cognitive processes that enable effective collaboration, focusing on adaptive theory of mind (ToM) and systematic critical evaluation. We investigate three key questions. First, how does the ability to model others' perspectives enhance coordination and reduce redundant reasoning? Second, to what extent does structured critique improve reasoning quality by identifying logical gaps and mitigating biases? Third, the interplay of these mechanisms can lead to emergent cognitive synergy, where the collective intelligence of the system exceeds the sum of its parts. Through an empirical case study on complex decision making, we show that the integration of these cognitive mechanisms leads to more coherent, adaptive, and rigorous agent interactions. This article contributes to the field of cognitive science and AI research by presenting a structured framework that emulates human-like collaborative reasoning MAS. It highlights the significance of dynamic ToM and critical evaluation in advancing multi-agent systems' ability to tackle complex, real-world challenges.