让多个LLM模块异步协作，实现自适应智能行为。
A Concurrent Modular Agent: Framework for Autonomous LLM Agents
- 多个LLM模块并行运行，通过语言交互协同工作。
- 系统在真实任务中表现出自适应与容错能力。
- 适合研究智能涌现与自主代理的开发者参考。
我们提出并发模块化智能体（CMA）框架，通过多个基于大语言模型（LLM）的模块异步运行，同时保持行为连贯性与容错性。该框架通过自主进程间的语言交互，使意图自然涌现，实现灵活、自适应、上下文相关的决策。其核心机制包括：并行执行的模块、模块间通信以及共享全局状态。该设计可视为明斯基‘心智社会’理论的实践体现。我们在两个实际用例中验证了系统的可行性。系统展现出的涌现特性表明，复杂认知现象如自我意识可能源于简单过程的有序交互，支持心智社会理论，并为人工智能研究开辟新路径。代码已开源：https://github.com/AlternativeMachine/concurrent-modular-agent。
原文摘要 · Abstract (English)
We introduce the Concurrent Modular Agent (CMA), a framework that orchestrates multiple Large-Language-Model (LLM)-based modules that operate fully asynchronously yet maintain a coherent and fault-tolerant behavioral loop. This framework addresses long-standing difficulties in agent architectures by letting intention emerge from language-mediated interactions among autonomous processes. This approach enables flexible, adaptive, and context-dependent behavior through the combination of concurrently executed modules that offload reasoning to an LLM, inter-module communication, and a single shared global state.We consider this approach to be a practical realization of Minsky's Society of Mind theory. We demonstrate the viability of our system through two practical use-case studies. The emergent properties observed in our system suggest that complex cognitive phenomena like self-awareness may indeed arise from the organized interaction of simpler processes, supporting Minsky-Society of Mind concept and opening new avenues for artificial intelligence research. The source code for our work is available at: https://github.com/AlternativeMachine/concurrent-modular-agent.
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