Collaborative Causal Inference and Multi-Agent Dynamic Intervention for “Dual Carbon” Public Opinion Driven by Reinforced Large Language Models and Diffusion Models

Abstract

Under the “Dual Carbon” goal, public opinion analysis is crucial for optimizing policy implementation and enhancing social consensus, yet it faces challenges such as insufficient multi-source data integration, limited causal modeling, and delayed interventions. This study proposes a collaborative framework integrating reinforcement learning-enhanced large language models (LLMs), diffusion models, and multi-agent systems (MASs). By constructing a four-dimensional causal network of “policy–technology–economy–public sentiment”, it analyzes multi-source data and simulates multi-agent interactions. The experimental results show that this framework outperforms Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), and Susceptible Infected Recovered (SIR) models in causal inference, dynamic intervention, and multi-agent collaboration. Reinforcement Learning from Human Feedback (RLHF) optimizes LLM outputs for reliable policy recommendations, with pass@10 showing strong correlations. This study provides scientific support for “Dual Carbon” policymaking and public opinion guidance, facilitating the green and low-carbon transition.