Superficial Self-Reflection (SSR) refers to the process by which an AI system improves its behavior or decision-making through simple feedback review and adjustment when performing a task. However, this reflection process is usually superficial and does not involve deep understanding or structural changes. It aims to make local adjustments to the AI system or model through immediate feedback to quickly optimize the performance of the current task or behavior.

The purpose of shallow self-reflection is to improve the effectiveness of an AI system or model in a specific situation through simple error backtracking and fine-tuning without making deep structural changes. This type of reflection focuses more on immediate, superficial improvements and is suitable for short-term task optimization rather than in-depth analysis or solving fundamental problems of the system.