Beyond Large Language Models: A Mental World Model Framework for Mental Modeling, Simulation, Evolution, and Interpretability· 超越大语言模型：心理世界模型框架
Understanding and simulating how subjective cognition and mental states emerge, evolve, and shape behavior is essential for advancing artificial intelligence toward deeper social cognition and general intelligence. Existing large language models (LLMs) and Theory of Mind (ToM) methods primarily infer mental states from linguistic information, lacking unified mechanisms for subjective world construction, cognitive-psychological interactions, dynamic evolution, and historical dependencies. This paper introduces the Mental World Model (MWM) and its implementation, the Multi-Branch Mental Evolution Architecture (MMEA), which explicitly constructs subjective worlds, models interactions between cognition and mental states, and simulates continuous mental evolution shaped by historical experience
提出心理世界模型框架，实现主观世界构建与心理状态演化模拟。
- 核心方法
- MWM框架通过显式构建主观世界，模拟认知与心理状态之间的互动，并通过MMEA架构实现由历史经验塑造的连续心理状态演化。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 现有大语言模型和心理理论方法主要从语言信息推断心理状态，缺乏构建主观世界、认知心理互动、动态演化和历史依赖性的统一机制。
- 关键实验
- 未提供
- 主要贡献
- 提供了统一的心理状态建模、认知互动机制和动态演化的解决方案，推动了社会认知和通用智能的发展。
- 意义与局限
- 该框架为AI理解和模拟人类心理状态提供了新的方向，有助于开发更具有社会交互能力和灵活性的AI系统，但具体实现细节和应用场景仍需进一步研究。