Large language models (LLMs) have demonstrated strong capabilities in knowledge representation and reasoning based on textual data. However, their reliance on language material alone limits their ability to adapt, verify reasoning outcomes, and operate effectively in open and dynamic real-world environments. In this paper, we propose Human Simulation Computation (HSC), a human-inspired computational framework that models intelligence as a continuous, closed-loop process involving thinking, action, learning, reflection, and activity scheduling, collectively referred to as the internal reasoning process. HSC emphasizes active participation both within the internal reasoning process and in interactions with the environment, where actions are used not only to achieve goals but also to automatically refine and improve internal reasoning mechanisms without external intervention. Furthermore, HSC incorporates commonly used human thinking strategies across all stages of the internal reasoning process, such as main-feature-oriented reasoning, scope expansion through action, and on-time learning driven by environmental feedback. Through theoretical analysis, we argue that human simulation strategies cannot be fully learned from language material alone, and that human-like reasoning processes and action-grounded reasoning methods are essential for robust adaptation and effective interaction with real-world environments.

翻译：大型语言模型（LLM）在基于文本数据的知识表示与推理方面展现出强大能力。然而，其仅依赖语言材料的特性限制了其在开放动态的真实环境中进行适应、验证推理结果及有效运作的能力。本文提出人类模拟计算（HSC），这是一种受人类启发的计算框架，将智能建模为一个持续、闭环的过程，涉及思考、行动、学习、反思与活动调度，这些环节统称为内部推理过程。HSC强调在内部推理过程及与环境交互中的主动参与，其中行动不仅用于实现目标，还能在无需外部干预的情况下自动优化和改进内部推理机制。此外，HSC在内部推理过程的各阶段整合了人类常用的思维策略，例如以主要特征为导向的推理、通过行动扩展范围，以及由环境反馈驱动的即时学习。通过理论分析，我们认为人类模拟策略无法仅从语言材料中完全习得，而类人的推理过程及基于行动的推理方法对于实现稳健适应并与真实环境有效交互至关重要。