CASCADE: A Cascading Architecture for Social Coordination with Controllable Emergence at Low Cost
Yizhi Xu

TL;DR
CASCADE introduces a three-layer architecture enabling scalable, controllable social coordination in game worlds by combining macro-level management, domain-specific modules, and on-demand LLM-driven NPC responses.
Contribution
The paper presents CASCADE, a novel modular architecture that balances authorial control and computational efficiency for social simulation in games.
Findings
Shared macro events produce differentiated NPC behaviors.
CASCADE reduces LLM usage by localizing it to player-facing interactions.
The architecture supports scalable and believable social simulation.
Abstract
Creating scalable and believable game societies requires balancing authorial control with computational cost. Existing scripted NPC systems scale efficiently but are often rigid, whereas fully LLM-driven agents can produce richer social behavior at a much higher runtime cost. We present CASCADE, a three-layer architecture for low-cost, controllable social coordination in sandbox-style game worlds. A Macro State Director (Level 1) maintains discrete-time world-state variables and macro-level causal updates, while a modular Coordination Hub decomposes state changes through domain-specific components (e.g., professional and social coordination) and routes the resulting directives to tag-defined groups. Then Tag-Driven NPCs (Level 3) execute responses through behavior trees and local state/utility functions, invoking large language models only for on-demand player-facing interactions. We…
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