Human Simulation Computation: A Human-Inspired Framework for Adaptive AI Systems

TL;DR

Human Simulation Computation (HSC) is a human-inspired framework that treats intelligence as a continuous, closed-loop process combining thinking, action, learning, reflection, and scheduling to adapt robustly in open environments. It embeds structured human thinking modes into LLM reasoning and emphasizes action as an active driver of learning and environment-grounded verification, not merely as task execution. The approach integrates systematic activity scheduling, on-time learning, and reflection to achieve self-improvement and long-term adaptability, arguing that language-data alone cannot realize such continual, embodied adaptation. The theoretical argument is supported by a formalized loop and reasoning strategies, highlighting the necessity of environment interaction for grounded reasoning, self-growth, and efficient search via difference-based focusing. Overall, HSC seeks to enable AI systems to live, adapt, and grow with their environments, extending beyond single-task performance to continuous self-improvement.

Abstract

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.