Evolving Collective Cognition in Human-Agent Hybrid Societies: How Agents Form Stances and Boundaries

Large language models have been widely used to simulate credible human social behaviors. However, it remains unclear whether these models can demonstrate stable capacities for stance formation and identity negotiation in complex interactions, as well as how they respond to human interventions. We propose a computational multi-agent society experiment framework that integrates generative agent-based modeling with virtual ethnographic methods to investigate how group stance differentiation and social boundary formation emerge in human-agent hybrid societies. Across three studies, we find that agents exhibit endogenous stances, independent of their preset identities, and display distinct tonal preferences and response patterns to different discourse strategies. Furthermore, through language interaction, agents actively dismantle existing identity-based power structures and reconstruct self-organized community boundaries based on these stances. Our findings suggest that preset identities do not rigidly determine the agents' social structures. For human researchers to effectively intervene in collective cognition, attention must be paid to the endogenous mechanisms and interactional dynamics within the agents' language networks. These insights provide a theoretical foundation for using generative AI in modeling group social dynamics and studying human-agent collaboration.

ITCMA: A Generative Agent Based on a Computational Consciousness Structure

Large Language Models (LLMs) still face challenges in tasks requiring understanding implicit instructions and applying common-sense knowledge. In such scenarios, LLMs may require multiple attempts to achieve human-level performance, potentially leading to inaccurate responses or inferences in practical environments, affecting their long-term consistency and behavior. This paper introduces the Internal Time-Consciousness Machine (ITCM), a computational consciousness structure. We further propose the ITCM-based Agent (ITCMA), which supports behavior generation and reasoning in open-world settings. ITCMA enhances LLMs' ability to understand implicit instructions and apply common-sense knowledge by considering agents' interaction and reasoning with the environment. Evaluations in the Alfworld environment show that trained ITCMA outperforms the state-of-the-art (SOTA) by 9% on the seen set. Even untrained ITCMA achieves a 96% task completion rate on the seen set, 5% higher than SOTA, indicating its superiority over traditional intelligent agents in utility and generalization. In real-world tasks with quadruped robots, the untrained ITCMA achieves an 85% task completion rate, which is close to its performance in the unseen set, demonstrating its comparable utility in real-world settings.