用迷宫测试评估大模型的类意识行为，发现其缺乏持续自我的统一感知。
Assessing Consciousness-Related Behaviors in Large Language Models Using the Maze Test
- 设计第一人称迷宫任务，测试空间、视角、目标和时间序列等意识特征。
- 深求-1（DeepSeek-R1）部分路径准确率达80.5%，但完整路径仅52.9%。
- 模型虽有推理能力，但无法保持连贯的自我认知，仍无真正意识。
我们通过迷宫测试研究大型语言模型（LLMs）的类意识行为，要求模型从第一人称视角导航迷宫。该测试同时考察空间感知、换位思考、目标导向行为和时间序列理解——这些是与意识相关的关键特征。将意识理论归纳为13个核心特性后，我们在零样本、单样本和少样本学习场景下评估了12个主流LLM。结果表明，具备推理能力的模型显著优于标准版本，其中Gemini 2.0 Pro达成52.9%的完整路径准确率，DeepSeek-R1达到80.5%的部分路径准确率。两者的差距揭示了模型在解题过程中难以维持连贯的自我模型——这是意识的核心特征。尽管通过推理机制展现出进步，但模型仍缺乏整合且持续的自我意识。
原文摘要 · Abstract (English)
We investigate consciousness-like behaviors in Large Language Models (LLMs) using the Maze Test, challenging models to navigate mazes from a first-person perspective. This test simultaneously probes spatial awareness, perspective-taking, goal-directed behavior, and temporal sequencing-key consciousness-associated characteristics. After synthesizing consciousness theories into 13 essential characteristics, we evaluated 12 leading LLMs across zero-shot, one-shot, and few-shot learning scenarios. Results showed reasoning-capable LLMs consistently outperforming standard versions, with Gemini 2.0 Pro achieving 52.9% Complete Path Accuracy and DeepSeek-R1 reaching 80.5% Partial Path Accuracy. The gap between these metrics indicates LLMs struggle to maintain coherent self-models throughout solutions -- a fundamental consciousness aspect. While LLMs show progress in consciousness-related behaviors through reasoning mechanisms, they lack the integrated, persistent self-awareness characteristic of consciousness.
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