# Difference-Guided Reasoning: A Temporal-Spatial Framework for Large Language Models

arXiv: https://arxiv.org/abs/2509.20713 · HTML: https://mlsanity.com/paper/2509.20713/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2025.

## Built on by (most cited first, 4 of 4)

- [A Layered Intuition -- Method Model with Scope Extension for LLM Reasoning](https://mlsanity.com/paper/2510.10592/) (2025)
- [Human Simulation Computation: A Human-Inspired Framework for Adaptive AI Systems](https://mlsanity.com/paper/2601.13887/) (2026)
- [Active Thinking Model: A Goal-Directed Self-Improving Framework for Real-World Adaptive Intelligence](https://mlsanity.com/paper/2511.00758/) (2025)
- [Method Decoration (DeMe): A Framework for LLM-Driven Adaptive Method Generation in Dynamic IoT Environments](https://mlsanity.com/paper/2512.21817/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) are important tools for reasoning and problem-solving, while they often operate passively, answering questions without actively discovering new ones. This limitation reduces their ability to simulate human-like thinking, where noticing differences is a key trigger for reasoning. Thus, in this paper we propose a difference-guided reasoning framework, which enables LLMs to identify and act upon changes across time and space. The model formalizes differences through feature extraction, prioritizes the most impactful and latest changes, and links them to appropriate actions. We further extend the framework with mechanisms for abnormal behavior detection and the integration of external information from users or sensors, ensuring more reliable and grounded reasoning. Verification results show that prompting LLMs with differences improves focus on critical issues, leading to higher alignment with desired reasoning outcomes compared to direct prompting.