Human Simulation Computation: A Human-Inspired Framework for Adaptive AI Systems
This addresses the problem of AI systems lacking adaptability and verification in open environments, but it is incremental as it builds on existing human-inspired approaches without empirical validation.
The paper tackles the limitation of large language models in adapting to dynamic real-world environments by proposing Human Simulation Computation (HSC), a human-inspired framework that models intelligence as a closed-loop process involving thinking, action, and learning, resulting in a theoretical argument for improved robustness and adaptation.
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.