MARS: Reverse Querying and Multi-modality Source Verification for Social Simulation

Social network simulation aims to characterize information diffusion through social networks and the decision-making processes of individuals during diffusion. Existing LLM-based simulators typically model diffusion as forward-only, text- only propagation. However, real-world diffusion is multi-modality and interactive: recipients may use visual evidence and query the source before deciding whether to propagate a claim. Modeling these two aspects is therefore important for capturing realistic verification behavior and reducing distortions in simulated diffusion. Their absence creates two limitations: I) without reverse querying, receiving agents cannot obtain additional source evidence before deciding whether to forward a claim; and II) without multi-modality evidence, simulators cannot capture how visual content affects the interpretation of recipients and propagation behavior. These limitations distort simulated diffusion and produce patterns that diverge from real-world behavior. To address these issues, we first construct MM-InfoBench, a multi-modality benchmark from real-world information diffusion data, providing aligned source text, visual evidence, and observed diffusion traces for studying evidence-aware diffusion processes. Then, we introduce Multi-modality Agent Reverse-querying Simulator (MARS), a social simulation framework that preserves claim–source correspondence throughout diffusion. MARS enables receiving agents to access associated multi-modality evidence and query the source before making propagation decisions, integrating source verification into information diffusion simulation. Extensive experiments demonstrate that MARS outperforms existing approaches in source preservation, agent behavior, and diffusion dynamics, increasing denial messages from 1.85% to 50.65% and reducing exposure to false claims from 5.06% to 0.42%. The dataset and source code will be released.