字节跳动李航博士新作：AI智能体的通用框架

Core Viewpoint - The article discusses a general framework for AI agents proposed by Dr. Li Hang from ByteDance, which encompasses both software and hardware agents, emphasizing their task-oriented nature and reliance on large language models (LLMs) for reasoning and reinforcement learning for construction [3][4]. Group 1: Characteristics of AI Agents - AI agents are defined as "rational action machines" that interact with their environment, including humans, to achieve specific tasks with evaluative standards for success [6]. - They utilize text and multimodal data (including images, videos, and audio) as inputs and can produce text, multimodal data, or action data as outputs [7][8]. - The core of the AI agent framework is the LLM, which facilitates reasoning and decision-making, and the framework aligns with human brain information processing mechanisms [8][19]. Group 2: Framework Components - The proposed framework consists of multimodal large language models (MLLM), tools, memory (including long-term and working memory), multimodal encoders, decoders, and action decoders [11][12]. - Hardware agents (robots) require both MLLM and a multimodal-language-action model (MLAM) for high-level task planning and low-level action planning [12]. - The framework has a two-layer structure: the lower layer includes various components, while the upper layer manages overall information processing [12]. Group 3: Comparison with Human Brain - The framework of AI agents shows functional similarities to human brain information processing, exhibiting a dual-layer structure with serial and parallel processing capabilities [19]. - Both systems utilize symbolic and neural representations for information processing, indicating a shared approach in handling complex tasks [19][28]. Group 4: Future Research Directions - Key areas for future exploration include expanding data scale, enabling autonomous and continual learning, and enhancing safety and controllability of AI agents [30][31][32][34]. - The lack of sufficient training data is identified as a significant bottleneck, necessitating innovative data collection methods [31]. - The development of AI agents should focus on ensuring that reinforcement learning reward functions align with human values to mitigate risks [34].