A Comprehensive Survey of LLM-Driven Collective Intelligence: Past, Present, and Future

Collective intelligence (CI) has emerged as a cornerstone for advancing toward Artificial General Intelligence (AGI), with Large Language Models (LLMs) now serving as the primary driver. In this study, we present the first systematic framework to characterize the evolutionary trajectory of CI, and conduct a comprehensive review of the transformative role of LLMs in driving CI. Historically, CI went through two phases: the initial Collective Intelligence 1.0 Era ("Collective but Unintelligent") introduces various bio-inspired algorithms which demonstrates emergent behaviors but no true cognitive capabilities, while the subsequent Collective Intelligence 2.0 Era ("Specialized yet Disconnected") achieves domain-specific expertise within multi-agent systems but remained isolated across applications. Currently, Collective Intelligence 3.0 Era ("Collectively to Enhance Intelligence") is being shaped by LLMs, with remarkable breakthroughs achieved in both foundation-oriented and application-oriented researches. At the foundational level, by enhancing data, model, and the inference process, LLM-driven CI has made remarkable progress in terms of performance optimization and efficiency improvement. In applications, LLM-driven CI excels in complex, long-horizon tasks, such as scientific discovery and autonomous research, delivering innovative solutions to real-world problems. In the future, in order to solve more intricate and even unexplored real-world tasks and scenarios, there is an urgent necessity to transition to Collective Intelligence 4.0 Era ("Collectively to Create Intelligence"). To achieve this, the following aspects can be considered including fundamental capability construction, capability differentiation, cognitive fusion, dynamic organization, and co-evolution, and several challenges such as unclear emergence mechanisms, bottlenecks in continuous learning, interoperability issues among systems, and high demand for computational resources should be solved. The collaborative efforts of the research communities are expected to accelerate the maturation of LLM-driven CI, create immense value for society and drive leapfrog development across various fields, and steadily march towards the AGI goal.