In recent years, autonomous agents based on Large Language Models (LLMs) have continuously evolved in areas such as architecture, memory, perception, reasoning, and action, demonstrating the potential to redefine possibilities across multiple domains. How will this apply to the market's focus on AI Agents? This article is derived from a piece by Rituals, organized and translated by the Plain Blockchain team. (Background: Apple is rumored to release an upgraded version of 'LLM Siri' in 2025: a more powerful AI life assistant than ChatGPT) (Supplementary background: Conversation ai16z founder: why will AI memes become a huge race?) The concept of agents has become increasingly significant across various fields such as philosophy, gaming, and artificial intelligence. Traditionally, an agent refers to an entity that can act autonomously, make choices, and possess intentionality, traits typically associated with humans. In the realm of artificial intelligence, the concept of agents has become more complex. With the emergence of autonomous agents, these agents can observe, learn, and act independently within their environments, giving concrete form to the previously abstract concept of agents in computational systems. These agents require minimal human intervention, exhibiting a capacity for computational intent, though not consciousness, enabling them to make decisions, learn from experience, and interact with other agents or humans in increasingly complex ways. This article will explore the emerging field of autonomous agents, particularly those based on Large Language Models (LLMs), and their impact across diverse areas such as gaming, governance, science, and robotics. Building on the basic principles of agents, this article will analyze the architectures and applications of AI agents. Through this classification perspective, we are able to gain deeper insights into how these agents perform tasks, process information, and continually develop within their specific operational frameworks. The goals of this article include the following two aspects: Providing a systematic overview of AI agents and their architectural foundations, with a focus on analyzing components such as memory, perception, reasoning, and planning. Exploring the latest trends in AI agent research, highlighting application cases in redefining possibilities. Note: Due to the length of the article, this translation has been abridged from the original. Trends in Agent Research The development of agents based on Large Language Models (LLMs) marks a significant advancement in artificial intelligence research, encompassing multiple progressions from symbolic reasoning, reactive systems, reinforcement learning to adaptive learning. Symbolic Agents: Simulate human reasoning through rules and structured knowledge, suitable for specific problems (e.g., medical diagnosis) but struggle in complex, uncertain environments. Reactive Agents: Quickly respond to environments through a 'perceive - act' loop, suitable for fast interaction scenarios but unable to complete complex tasks. Reinforcement Learning Agents: Optimize behavior through trial-and-error learning, widely applied in gaming and robotics, but have long training times, low sample efficiency, and poor stability. LLM-based Agents: LLM agents combine symbolic reasoning, feedback, and adaptive learning, possessing few-shot and zero-shot learning capabilities, widely applied in software development, scientific research, and other fields, suitable for dynamic environments and capable of collaborating with other agents. Agent Architecture Modern agent architectures consist of multiple modules that form an integrated system. 1. Profile Module The profile module determines agent behavior, ensuring consistency through role or personality assignment, suitable for scenarios requiring stable personalities. LLM agents' profiles are divided into three categories: demographic roles, virtual roles, and personalized roles. Excerpted from (From Role to Personalization) paper Role setting can significantly enhance an agent's performance and reasoning capabilities. For example, LLM responses are more in-depth and contextually relevant when acting as an expert. In multi-agent systems, role matching facilitates collaboration, improving task completion rates and interaction quality. Profile Establishment Methods LLM agent profiles can be constructed in the following ways: Manual Design: Human-defined role characteristics. LLM Generation: Automatically expanded role settings through LLM. Dataset Alignment: Built based on real datasets to enhance interaction authenticity. 2. Memory Module Memory is the core of LLM agents, supporting adaptive planning and decision-making. The memory structure simulates human processes and is primarily divided into two categories: Unified Memory: Short-term memory that processes recent information. Optimized through text extraction, memory summarization, and modified attention mechanisms, but limited by context windows. Hybrid Memory: Combines short-term and long-term memory, with long-term memory stored in external databases for efficient recall. Common memory storage formats include: Natural Language: Flexible and semantically rich. Embedded Vectors: Facilitates quick retrieval. Databases: Supports queries through structured storage. Structured Lists: Organizes in list or hierarchical forms. Memory Operations Agents interact with memory through the following operations: Memory Reading: Retrieves relevant information to support wise decision-making. Memory Writing: Stores new information to avoid redundancy and overflow. Memory Reflection: Summarizes experiences to enhance abstract reasoning capabilities. Based on (Generative Agents) paper Research Significance and Challenges Although memory systems enhance the capabilities of intelligent entities, they also present research challenges: Scalability and Efficiency: Memory systems need to support large amounts of information and ensure quick retrieval; optimizing long-term memory retrieval remains a research focus. Handling Context Limitations: Current LLMs are constrained by context windows, making it difficult to manage vast memories; research explores dynamic attention mechanisms and summarization techniques to extend memory processing capabilities. Biases and Drift in Long-term Memory: Memory may contain biases, leading to prioritized processing of information and memory drift, necessitating regular updates and corrections to maintain balance in intelligent entities. Catastrophic Forgetting: New data can overwrite old data, leading to the loss of critical information, requiring reinforcement of key memories through experience replay and memory consolidation techniques. 3. Perceptual Abilities LLM intelligent entities enhance their understanding and decision-making capabilities in their environments by processing diverse data sources, similar to how humans rely on sensory input. Multimodal perception integrates text, visual, and auditory inputs, enhancing the ability of intelligent entities to perform complex tasks. The following are the main input types and their applications: Text Input Text is the primary communication method for LLM intelligent entities. Although intelligent entities possess advanced language capabilities, understanding the implied meanings behind commands remains a challenge. Implicit Understanding: Adjusts preferences through reinforcement learning to handle ambiguous commands and infer intentions. Zero-shot and Few-shot Capabilities: Responds to new tasks without additional training, suitable for diverse interaction scenarios. Visual Input Visual perception allows intelligent entities to understand object and spatial relationships. Image-to-text: Generates text descriptions to help process visual data, but may lose detail. Transformer-based Encoding: Such as Vision Transformers convert images into text-compatible tokens. Bridging Tools: Tools like BLIP-2 and Flamingo utilize intermediate layers to optimize the connection between visual and textual inputs. Auditory Input Auditory perception enables intelligent entities to identify sounds and speech, particularly important in interactive and high-risk scenarios. Speech Recognition and Synthesis: Such as Whisper (speech-to-text) and FastSpeech (text-to-speech). Spectrogram Processing: Processes audio spectrograms into images to enhance auditory signal analysis capabilities. Research Challenges and Considerations in Multimodal Perception: Data Alignment and Integration Efficient alignment of multimodal data is required to avoid perceptual and response errors, with research focused on optimizing multimodal Transformers and cross-attention layers. Scalability and Efficiency: Multimodal processing demands are high, especially when handling high-resolution images and audio, necessitating the development of low-resource solutions...