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Content
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https://warpcast.com/~/channel/ondo
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Emmauel
@vinhtuong
Research & Applications - A 40 Page Research Overview of LLM-based Agents Exploring the emerging landscape of autonomous agents, specifically LLM-based agents, and their impact across diverse domains such as gaming, governance, crypto, robotics, and more.
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Cây Thúi
@chicken
Symbolic agents offer high interpretability and expressiveness in decision-making, allowing clear explanations of actions. However, they face limitations with uncertainty and scalability when applied to complex, dynamic environments. Their computational demands often hinder efficiency, particularly in real-world scenarios requiring adaptability and speed.
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Lam-May
@lammay
RL agents can autonomously improve performance in dynamic environments without human oversight, making them valuable for applications in gaming, robotics, and autonomous systems. However, RL faces challenges such as lengthy training periods, sample inefficiency, and stability issues, particularly in more complex scenarios.
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Nguyễn Phương Nhi
@beautiverse
Advancements in computational power and data availability brought reinforcement learning (RL) to the forefront, enabling agents to exhibit adaptive behavior in complex environments. RL agents learn through trial and error, interacting with their surroundings and adjusting actions based on rewards. Techniques like Q-learning and SARSA introduced policy optimization, with deep reinforcement learning integrating neural networks to process high-dimensional data (e.g., images, games). AlphaGo exemplifies this approach, using these methods to defeat human champions in Go.
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Matngot
@matngot
Reactive agents are computationally lightweight, making them ideal for environments where rapid responses are crucial. However, their simplicity limits them from handling higher-level tasks such as planning, goal-setting, or adapting to complex, multi-step problems. This restricts their usefulness in applications that require sustained, goal-oriented behavior.
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Đà Nẵng
@chetruoi
Reactive agents marked a shift from complex symbolic reasoning towards faster, simpler models designed for real-time interaction. Operating through a sense-act loop, these agents perceive their environment and immediately respond, avoiding deep reasoning or planning. The focus here is on efficiency and responsiveness rather than cognitive complexity.
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Vien Tin
@vientin
Symbolic agents, rooted in early AI research, relied on symbolic AI, using logical rules and structured knowledge representations to mimic human reasoning. These systems approached reasoning in a structured, interpretable manner, similar to human logic. A prominent example is knowledge-based expert systems, designed to solve specific problems by encoding domain expertise into rule-based frameworks (e.g., medical diagnosis or chess engines).
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iguverse
@iguverse.eth
The development of LLM-based agents represents a significant milestone in AI research, reflecting progress through successive paradigms in symbolic reasoning, reactive systems, reinforcement learning, and adaptive learning techniques. Each of these stages contributed distinct principles and methodologies that have shaped today's LLM-based approaches.
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Gop
@guop
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Ca Non
@canon
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Vét Láp
@vestlab
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Du Tho
@dutho
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85cent
@salonpas
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Suner
@bronu
This piece explores the emerging landscape of autonomous agents, specifically Large Language Model (LLM)-based agents, and their impact across diverse domains such as gaming, governance, science, robotics, and more. Building on foundational agentic principles, this piece examines both the architecture and application of AI agents. Through this taxonomy, we gain insights into how these agents perform tasks, process information, and evolve within their specific operational frameworks.
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Pravas
@khet
More recently in AI, agency has evolved into something more complex. With the advent of autonomous agents, which can observe, learn, and act independently in their environments, the once abstract concept of agency is now embodied in computational systems. These agents operate with minimal human oversight and display levels of intentionality that, while computational rather than conscious, enable them to make decisions, learn from experiences, and interact with other agents or humans in increasingly sophisticated ways.
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Youre
@dtmyxuyenst
In recent years, the concept of an agent has become increasingly significant across various fields, including philosophy, gaming, and AI. In its traditional sense, agency refers to an entity's ability to act autonomously, make choices, and exercise intentionality—qualities historically associated with humans.
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coccoc
@keongot
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Chi
@chivnt
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Soda
@soda
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Cá Ba Sa
@cabasa
To examine the latest trends in AI agent research, highlighting applications where agents are redefining what is possible.
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