Unified Mind Model: Reimagining Autonomous Agents in the LLM Era
- URL: http://arxiv.org/abs/2503.03459v2
- Date: Thu, 06 Mar 2025 03:32:45 GMT
- Title: Unified Mind Model: Reimagining Autonomous Agents in the LLM Era
- Authors: Pengbo Hu, Xiang Ying,
- Abstract summary: Large language models (LLMs) have recently demonstrated remarkable capabilities across domains, tasks, and languages.<n>We propose a novel theoretical cognitive architecture, the Unified Mind Model (UMM), which offers guidance to facilitate the rapid creation of autonomous agents.
- Score: 1.3812010983144802
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) have recently demonstrated remarkable capabilities across domains, tasks, and languages (e.g., ChatGPT and GPT-4), reviving the research of general autonomous agents with human-like cognitive abilities. Such human-level agents require semantic comprehension and instruction-following capabilities, which exactly fall into the strengths of LLMs. Although there have been several initial attempts to build human-level agents based on LLMs, the theoretical foundation remains a challenging open problem. In this paper, we propose a novel theoretical cognitive architecture, the Unified Mind Model (UMM), which offers guidance to facilitate the rapid creation of autonomous agents with human-level cognitive abilities. Specifically, our UMM starts with the global workspace theory and further leverage LLMs to enable the agent with various cognitive abilities, such as multi-modal perception, planning, reasoning, tool use, learning, memory, reflection and motivation. Building upon UMM, we then develop an agent-building engine, MindOS, which allows users to quickly create domain-/task-specific autonomous agents without any programming effort.
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