From LLM narratives to parameterized cooperation policies in multi-agent systems.

Introduction: Large language models (LLMs) can generate persuasive narratives that shift agent behavior in multi-agent systems, but deploying raw, unstructured text as an influence mechanism offers no formal guarantees on effectiveness, interpretability, or controllability. Methods: We introduce the LLM Influence Compiler (LIC), a solver-critic pipeline that compiles natural-language cooperation directives into structured, parameterized influence policies defined over a five-field schema: network targeting, narrative theme, intensity, deployment timing, and compiler confidence. Each compiled policy is diffused through a network of numerical agents via an exposure model incorporating fatigue decay, susceptibility heterogeneity, and backlash. Evaluation spans nine controlled experiment blocks comprising more than 200 simulation runs across four topologies (Barabási-Albert, small-world, Erdős-Rényi, modular SBM), four network sizes ( Results: Compiled policies raise the mean cooperation rate to 0.826 ± 0.010 (95% bootstrap CI [0.819, 0.834]), an 8.6% relative improvement over the unstructured baseline (0.760 ± 0.005, Mann-Whitney Discussion: The compiler abstraction converts an opaque generative process into a decomposable, auditable policy object whose components can be independently attributed, compared, and governed, laying the groundwork for constitutional oversight of LLM-mediated influence in artificial societies.