AI Agent Intensive Co-Learning Group Proposal
TL;DR This proposal aims to launch a 3-week intensive co-learning group focused on deeply studying key AI Agent research papers. The goal is to foster a thorough understanding of AI Agent concepts, core capabilities, frameworks, and challenges, with an emphasis on individual critical analysis and summarization of observations from the readings.
Background AI Agents, powered by Large Language Models (LLMs), represent a rapidly advancing frontier in artificial intelligence. They possess the ability to autonomously plan, use tools, and complete tasks, promising transformative impacts across various domains. To move beyond a superficial understanding and truly grasp the intricacies of this field, a structured and intensive engagement with foundational and cutting-edge research is essential. This co-learning initiative, inspired by the curriculum of UIUC’s CS598 Topics in LLM Agents, aims to provide such a focused environment for deep learning and discussion, under the “Intensive Co-Learning” (残酷共学) model championed by LXDAO.
Description We propose a 3-week intensive co-learning program dedicated to the study of AI Agents, scheduled from June 9, 2025, to June 29, 2025. Registration will be open from June 1 to June 8, 2025.
This program is designed for developers, product managers, researchers, and practitioners who wish to systematically deepen their understanding of the AI Agent technology stack and its research landscape. The core activity will be the intensive reading and critical analysis of selected seminal and recent research papers, followed by the summarization of individual observations and insights.
The weekly curriculum is structured as follows, drawing primarily from the “required readings” of the UIUC CS598 course:
- Week 1: AI Agent Foundations & Core Abilities (Reasoning, Planning, Memory)
- Focus: Understanding the fundamental concepts of AI Agents, their current landscape, and the key capabilities that constitute their intelligence.
- Key Papers: Works on Language Agent foundations (e.g., “Language Agents: Foundations, Prospects, and Risks”), AGI perspectives, ReAct, Tree of Thoughts, LLM+P, LATS, Cognitive Architectures for Language Agents, HippoRAG.
- Week 2: Expanding Agent Abilities (Multimodal), Frameworks (Tool Use, RAG, Multi-Agent Systems) & Evaluation
- Focus: Exploring multimodal understanding, key frameworks for building and enhancing agents, and methods for evaluating agent performance.
- Key Papers: Works on multimodal LLM shortcomings (e.g., “Eyes Wide Shut?”), VisualWebArena, ToolLLM, Gorilla, Adaptive-RAG, Corrective RAG, AutoGen, CAMEL, and agent evaluation platforms like Chatbot Arena.
- Week 3: Agent Applications, Challenges & Future Outlook
- Focus: Examining specific applications of AI Agents, delving into critical challenges (data, safety, alignment), and contemplating future developmental paths towards AGI.
- Key Papers: Works on applications like ResearchTown, coding agents surveys (e.g., “If LLM Is the Wizard, Then Code Is the Wand”), Generative Agents, Voyager, and papers addressing challenges in data acquisition (e.g., BAGEL), safety (e.g., adversarial attacks, DecodingTrust), and alignment (e.g., RLHF, DPO).
Participants are expected to dedicate significant time each week to reading the assigned papers, formulating their own analyses and questions, and optionally engaging in discussions within the LXDAO community. The emphasis is on “intensive study and summarization of personal observations.”
Key Results Upon completion of this co-learning program, participants are expected to:
- Deepened Understanding: Gain a comprehensive and nuanced understanding of the core concepts, mainstream methods, and research frontiers in AI Agents.
- Critical Analysis Skills: Enhance their ability to critically read, analyze, and interpret complex research papers, and to articulate their own informed observations and insights.
- Knowledge Synthesis: Produce individual summaries or reflections based on the weekly readings, solidifying their learning and contributing to a shared pool of knowledge (if they choose to share).
- Community Building: Foster a vibrant sub-community within LXDAO passionate about AI Agent research, encouraging ongoing learning and collaboration.
- Future Perspective: Develop a well-grounded perspective on the current limitations and future potential of AI Agent technology, including its path towards AGI.
中文版本
AI Agent 残酷共学提案
TL;DR 本提案旨在发起一个为期三周的AI Agent主题残酷共学小组，专注于深度研读AI Agent领域的关键研究论文。目标是促进对AI Agent核心概念、关键能力、主流框架及未来挑战的透彻理解，并特别强调成员对阅读材料进行批判性分析和个人观察的总结。
Background / 背景 由大型语言模型（LLM）驱动的AI Agent代表了人工智能领域一个飞速发展的前沿方向。它们具备自主规划、调用工具并完成任务的能力，预示着将在各行各业带来变革性的影响。为了超越浅层理解，真正把握该领域的复杂性，对基础性和前沿性的研究进行系统且高强度的学习至关重要。本次残酷共学活动，灵感来源于UIUC（伊利诺伊大学厄巴纳-香槟分校）的课程 CS598 Topics in LLM Agents 的内容，旨在LXDAO所倡导的“残酷共学”模式下，提供这样一个专注深度学习与研讨的环境。
Description / 描述 我们提议举办一个为期三周的AI Agent主题残酷共学项目，计划时间为 2025年6月9日至2025年6月29日。报名通道将于2025年6月1日至6月8日开放。
本项目专为希望系统性深化对AI Agent技术栈及其研究图谱理解的开发者、产品经理、研究人员和实践者设计。核心活动将是高强度阅读和批判性分析精选的开创性及最新研究论文，随后总结个人观察与洞见。
每周学习计划结构如下，主要参考UIUC CS598课程的“必读文献”：
- 第一周：AI Agent 基础、核心能力（推理、规划与记忆）
- 核心议题：理解 AI Agent 的基本概念、发展现状以及构成 Agent 智能的关键能力。
- 关键论文示例：关于语言Agent基础（如 “Language Agents: Foundations, Prospects, and Risks”）、AGI视角探讨、ReAct、Tree of Thoughts、LLM+P、LATS、Cognitive Architectures for Language Agents、HippoRAG等。
- 第二周：Agent 能力拓展（多模态）、框架（工具使用、RAG、多智能体）与评估
- 核心议题：探索 Agent 在多模态理解方面的能力，学习构建和增强 Agent 的关键框架，并了解如何评估 Agent 的性能。
- 关键论文示例：关于多模态LLM视觉短板（如 “Eyes Wide Shut?”）、VisualWebArena、ToolLLM、Gorilla、Adaptive-RAG、Corrective RAG、AutoGen、CAMEL以及Chatbot Arena等Agent评估平台的研究。
- 第三周：Agent 应用、挑战与未来展望
- 核心议题：了解 AI Agent 在不同领域的具体应用，深入探讨其面临的关键挑战（如数据、安全、对齐），并展望其向 AGI 发展的路径。
- 关键论文示例：关于ResearchTown等应用、代码智能体综述（如 “If LLM Is the Wizard, Then Code Is the Wand”）、Generative Agents、Voyager的研究，以及探讨数据获取（如BAGEL）、安全性（如对抗攻击、DecodingTrust）和对齐（如RLHF、DPO）等挑战的论文。
我们期望参与者每周投入充足的时间阅读指定论文，形成自己的分析和问题，并（可选）在LXDAO社群中参与讨论。本次共学的重点是“高强度学习和总结个人观察”。
Key Result / 主要成果 完成本次共学项目后，期望参与者能够达成：
- 深化理解： 对AI Agent领域的核心概念、主流方法和研究前沿获得全面而深刻的理解。
- 批判性分析能力： 提升批判性阅读、分析和解读复杂研究论文的能力，并能清晰表达自己基于信息的观察与洞见。
- 知识内化与产出： 基于每周的阅读材料，形成个人总结或反思，巩固学习成果，并（若选择分享）为社群贡献知识沉淀。
- 社群建设： 在LXDAO内部培养一个对AI Agent研究充满热情的子社群，鼓励持续学习与合作。
- 未来视野： 对AI Agent技术的当前局限性和未来潜力，包括其通往AGI的路径，形成更为坚实的认知和展望。