Coupling Macro Dynamics and Micro States for Long-Horizon Social Simulation

Social network simulation aims to model collective opinion dynamics in large populations, but existing LLM-based simulators primarily focus on aggregate dynamics and largely ignore individual internal states. This limits their ability to capture opinion reversals driven by gradual individual shifts and makes them unreliable over long-horizon simulations. Unlike existing methods that collapse dynamics into macro-only updates, we propose **M²Sim**, a social simulation framework that tightly couples **m**acro-level collective dynamics with **m**icro-level individual states. We explicitly model micro-level opinion states with a state transition mechanism, coupling Macro-level collective dynamics with Micro-level states. This allows opinion states to evolve gradually, rather than triggering instant reactions, enabling the simulator to distinguish states close to or far from switching, capture opinion reversals, and maintain accuracy over long-horizon simulations. Across real-world events, M²Sim supports stable simulation of long-horizon social events with up to 40,000 interactions (compared to  300 in the baseline MF-LLM), while reducing long-horizon KL divergence by 75.3% (1.2490 → 0.3089) and reversal KL by 66.9% (1.6425 → 0.5434), significantly mitigating the drift observed in MF-LLM.