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

arXiv: https://arxiv.org/abs/2601.13887 · HTML: https://mlsanity.com/paper/2601.13887/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 13 of 13)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Cognitive Architectures for Language Agents](https://mlsanity.com/paper/2309.02427/) (2023)
- [Applying Large Language Models and Chain-of-Thought for Automatic Scoring](https://mlsanity.com/paper/2312.03748/) (2023)
- [Vision-Language Models Can Self-Improve Reasoning via Reflection](https://mlsanity.com/paper/2411.00855/) (2024)
- [SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning](https://mlsanity.com/paper/2506.01713/) (2025)
- [Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation](https://mlsanity.com/paper/1911.07450/) (2019)
- [Consistency in Language Models: Current Landscape, Challenges, and Future Directions](https://mlsanity.com/paper/2505.00268/) (2025)
- [Out of the Box: Embodied Navigation in the Real World](https://mlsanity.com/paper/2105.05873/) (2021)
- [Difference-Guided Reasoning: A Temporal-Spatial Framework for Large Language Models](https://mlsanity.com/paper/2509.20713/) (2025)
- [Human-Inspired Learning for Large Language Models via Obvious Record and Maximum-Entropy Method Discovery](https://mlsanity.com/paper/2512.12608/) (2025)
- [Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurements](https://mlsanity.com/paper/2601.04235/) (2026)
- [Scatter-Based Innovation Propagation in Large Language Models for Multi-Stage Process Adaptation](https://mlsanity.com/paper/2506.17949/) (2025)

## Built on by (most cited first, 3 of 3)

- [Autonomous Question Formation for Large Language Model-Driven AI Systems](https://mlsanity.com/paper/2602.01556/) (2026)
- [Human-Inspired Continuous Learning of Internal Reasoning Processes: Learning How to Think for Adaptive AI Systems](https://mlsanity.com/paper/2602.11516/) (2026)
- [Simulating Human Cognition: Heartbeat-Driven Autonomous Thinking Activity Scheduling for LLM-based AI systems](https://mlsanity.com/paper/2604.14178/) (2026)

## Abstract (arXiv)

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.