Codified Finite-state Machines for Role-playing

2026-02-05

AI Summary

Existing approaches struggle to model the latent states that drive interactions in role-playing scenarios, often resulting in inconsistent behaviors. This work proposes a novel method that integrates large language models with finite state machines to automatically generate interpretable and executable deterministic finite state machines (CFSMs) from unstructured character descriptions. The approach is further extended to probabilistic finite state machines (CPFSMs) to capture character states and their transitions more flexibly. By enabling structured yet open-domain modeling of character behavior, the method significantly outperforms general-purpose baselines in both synthetic evaluations and real-world role-playing settings, demonstrating its effectiveness in handling structured tasks and stochastic state exploration.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Reinforcement Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Modeling latent character states is crucial for consistent and engaging role-playing (RP) with large language models (LLMs). Yet, existing prompting-based approaches mainly capture surface actions, often failing to track the latent states that drive interaction. We revisit finite-state machines (FSMs), long used in game design to model state transitions. While effective in small, well-specified state spaces, traditional hand-crafted, rule-based FSMs struggle to adapt to the open-ended semantic space of RP. To address this, we introduce Codified Finite-State Machines (CFSMs), a framework that automatically codifies textual character profiles into FSMs using LLM-based coding. CFSMs extract key states and transitions directly from the profile, producing interpretable structures that enforce character consistency. To further capture uncertainty and variability, we extend CFSMs into Codified Probabilistic Finite-State Machines (CPFSMs), where transitions are modeled as probability distributions over states. Through both synthetic evaluations and real-world RP scenarios in established artifacts, we demonstrate that CFSM and CPFSM outperform generally applied baselines, verifying effectiveness not only in structured tasks but also in open-ended stochastic state exploration.

Problem

• role-playing
• latent character states
• finite-state machines
• character consistency
• state transitions

Innovation

• Codified Finite-State Machines
• Large Language Models
• Role-playing
• Probabilistic State Transitions
• Character Consistency