Pod 提案：AI Agent 残酷共学小组
1. 基础信息
- 项目名称： AI Agent 残酷共学小组 (AI Agent Intensive Co-Learning Group)
- 负责人： Dex | Telegram: @dexhunt3r (GitHub: dexhunter)
- 关联主线： LX BuildPath 生态 (2.4) - Co-learning 课程设计
- 钱包地址： 无资金申请
2. 项目描述
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What (项目内容)：
本提案旨在发起一个为期三周（计划2025年6月9日至2025年6月29日，报名时间2025年6月1日至6月8日）的AI Agent主题残酷共学小组。核心活动是围绕顶尖课程 CS598 Topics in LLM Agents（UIUC）涵盖的前沿研究论文及最新进展，进行高强度的论文研读、总结与讨论。本次共学将特别强调论文的精读、理解与个人观察的总结。
共学社群依托 LXDAO Telegram：https://t.me/LXDAO/。 -
Why (项目缘由)：
AI Agent是由大型语言模型（LLM）驱动、能自主规划、调用工具并完成任务的新一代智能体，代表了人工智能领域的前沿。为帮助社区成员系统性学习并深入理解Agent技术：- 深入理解：系统性学习并掌握 LLM Agent 在推理、记忆、规划、工具调用、多模态处理等关键模块的核心论文和主流方法。
- 总结观察：促进成员针对每周研读的论文独立思考，总结个人观察、批判性见解和潜在启发。
- 批判与展望：基于论文学习，评估当前 Agent 技术的局限性（如数据、对齐、安全、人机协作等），并对下一代 Agent / AGI 的发展方向形成个人思考与展望。
本项目面向对Agent前沿研究感兴趣的开发者、产品经理、研究者及实践者，尤其是喜欢高密度论文阅读、深度思考与总结提炼的共学型成员。
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How (实施方案)：
共学为期三周，每周有核心议题和必读/选读论文清单，主要学习材料源自UIUC CS598课程并结合最新研究：- Week 1: 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。
- 关键领域：Overview & Foundations, Reasoning, Planning, Memory。
- Week 2: 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。
- 关键领域：Multimodal Understanding, Tool Use, RAG, Multi-Agent Systems, Agent Evaluation。
- Week 3: 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，并展望其向 AGI 发展的路径。
- 关键领域：Agent Application (Auto-research, Coding, Social, Gaming), Challenges (Data, Safety, Alignment), Future Perspectives。
参与者需每周投入充足时间精读指定论文，完成个人思考与观察总结，并积极参与社群讨论。
- Week 1: AI Agent 基础、核心能力（推理、规划与记忆）
3. 资金申请
- 首期申请： 0 USDT
- 详细说明：
- 本次共学小组初期主要依赖成员自发学习和研讨，由发起人Dex组织，暂无直接资金需求。
- 若未来考虑设置优秀学习笔记激励、邀请外部嘉宾分享或组织线下研讨等，可能会有小额预算需求，届时将另行提案。
4. Milestone计划
| 阶段 | 时间 | 交付物 | 验证标准 |
|---|---|---|---|
| 筹备与招募 | 2025/6/1 - 2025/6/8 | 1. 详细共学计划（含论文清单、学习指引）发布 2. 招募信息在LXDAO及相关社群推广 3. 完成学员报名与分组（如有） |
1. 共学计划文档在社群共享 2. 招募帖获得有效曝光 3. 至少 [例如: 15] 位合格成员确认参与 |
| 第一周学习 | 2025/6/9 - 2025/6/15 | 1. 完成第一周主题论文精读 2. 成员提交个人学习笔记/观察总结（形式不限，鼓励分享） |
1. 多数参与者确认完成阅读任务 2. 社群内产生围绕第一周内容的有效讨论或至少 [例如: 5] 份学习总结分享 |
| 第二周学习 | 2025/6/16 - 2025/6/22 | 1. 完成第二周主题论文精读 2. 成员提交个人学习笔记/观察总结 |
1. 多数参与者确认完成阅读任务 2. 社群内产生围绕第二周内容的有效讨论或至少 [例如: 5] 份学习总结分享 |
| 第三周学习 | 2025/6/23 - 2025/6/29 | 1. 完成第三周主题论文精读 2. 成员提交个人学习笔记/观察总结 |
1. 多数参与者确认完成阅读任务 2. 社群内产生围绕第三周内容的有效讨论或至少 [例如: 5] 份学习总结分享 |
| 总结与沉淀 | 2025/6/30 之后 | 1. （可选）组织一次线上总结分享会 2. 汇总优质学习笔记/观察总结，形成共学成果沉淀 3. 收集反馈，评估共学效果 |
1. 分享会顺利举办，参与度良好 2. 形成一份可公开的共学成果文档或知识库条目 3. 收到至少 [例如: 70%] 参与者的反馈 |
5. 项目需求
- 学习资源： 由发起人Dex基于UIUC CS598 Topics in LLM Agents 课程材料及最新研究进展整理并提供核心论文清单及辅助材料。
- 协作平台： 主要使用 LXDAO Telegram 群组 (https://t.me/LXDAO/) 进行日常交流、资料分享、打卡和讨论。
- 传播资源： 希望 LXDAO 官方渠道（如Forum, Twitter, 公众号等）协助宣传共学小组的招募信息及后续成果。
- 组织支持： 发起人Dex负责整体的组织协调、内容引导和社群维护。
Pod Proposal: AI Agent Intensive Co-Learning Group
1. Basic Information
- Project Name: AI Agent Intensive Co-Learning Group
- Person in Charge: Dex | Telegram: @dexhunt3r (GitHub: dexhunter)
- Related Mainline: LX BuildPath Ecosystem (2.4) - Co-learning course design
- Wallet Address: No funding required
2. Project Description
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What:
This proposal aims to launch a 3-week intensive co-learning group (planned from June 9, 2025, to June 29, 2025, with registration from June 1 to June 8, 2025) focused on AI Agents. The core activity will be high-intensity reading, summarization, and discussion of cutting-edge research papers, primarily from the CS598 Topics in LLM Agents course (UIUC) and recent advancements. This co-learning will particularly emphasize in-depth reading, comprehension, and summarization of personal observations from the papers.
The community will leverage the LXDAO Telegram group: https://t.me/LXDAO/. -
Why:
AI Agents, powered by Large Language Models (LLMs), are a new generation of intelligent entities capable of autonomous planning, tool use, and task completion, representing the forefront of AI. To help community members systematically learn and deeply understand Agent technology:- Deepen Understanding: Systematically learn and master core papers and mainstream methods in key LLM Agent modules such as reasoning, memory, planning, tool use, and multimodal processing.
- Summarize Observations: Encourage members to think independently about the papers read each week, summarizing personal observations, critical insights, and potential inspirations.
- Critique and Envision: Based on paper studies, evaluate the limitations of current Agent technologies (e.g., data, alignment, safety, human-computer collaboration) and form personal thoughts and outlooks on the development direction of next-generation Agents / AGI.
This program targets developers, product managers, researchers, and practitioners interested in cutting-edge Agent research, especially those who enjoy high-density paper reading, deep thinking, and synthesis.
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How:
The co-learning will span three weeks, each with core topics and a list of required/optional readings, primarily sourced from the UIUC CS598 course and supplemented with recent research:- 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 Areas: Overview & Foundations, Reasoning, Planning, Memory.
- 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 Areas: Multimodal Understanding, Tool Use, RAG, Multi-Agent Systems, Agent Evaluation.
- Week 3: Agent Applications, Challenges & Future Outlook
- Focus: Examining specific applications of AI Agents, delving into critical challenges, and contemplating future developmental paths towards AGI.
- Key Areas: Agent Application (Auto-research, Coding, Social, Gaming), Challenges (Data, Safety, Alignment), Future Perspectives.
Participants are expected to dedicate significant time each week to in-depth reading of assigned papers, complete personal reflections and observation summaries, and actively engage in community discussions.
- Week 1: AI Agent Foundations & Core Abilities (Reasoning, Planning, Memory)
3. Funding Request
- Initial Request: 0 USDT
- Details:
- Initially, this co-learning group will rely on members’ self-driven learning and discussion, organized by the initiator Dex, with no direct funding requirements.
- If future considerations include rewards for outstanding study notes, inviting external guest speakers, or organizing offline workshops, a small budget might be requested via a separate proposal.
4. Milestone Plan
| Phase | Timeline | Deliverables | Verification Criteria |
|---|---|---|---|
| Preparation & Recruitment | 2025/6/1 - 2025/6/8 | 1. Detailed co-learning plan (incl. paper list, study guide) released 2. Recruitment info promoted in LXDAO & related communities 3. Participant registration completed |
1. Plan document shared in community 2. Recruitment post achieves effective reach 3. At least [e.g., 15] qualified members confirmed |
| Week 1 Learning | 2025/6/9 - 2025/6/15 | 1. Completion of Week 1 themed paper in-depth reading 2. Members submit personal study notes/observation summaries (flexible format, sharing encouraged) |
1. Majority of participants confirm task completion 2. Meaningful discussions on Week 1 topics or at least [e.g., 5] shared summaries in community |
| Week 2 Learning | 2025/6/16 - 2025/6/22 | 1. Completion of Week 2 themed paper in-depth reading 2. Members submit personal study notes/observation summaries |
1. Majority of participants confirm task completion 2. Meaningful discussions on Week 2 topics or at least [e.g., 5] shared summaries in community |
| Week 3 Learning | 2025/6/23 - 2025/6/29 | 1. Completion of Week 3 themed paper in-depth reading 2. Members submit personal study notes/observation summaries |
1. Majority of participants confirm task completion 2. Meaningful discussions on Week 3 topics or at least [e.g., 5] shared summaries in community |
| Wrap-up & Knowledge Sharing | Post 2025/6/30 | 1. (Optional) Organize an online wrap-up session 2. Compile high-quality notes/summaries into a co-learning knowledge base 3. Collect feedback & evaluate effectiveness |
1. Session successfully held with good attendance 2. A public co-learning outcome document or knowledge base entry created 3. Feedback received from at least [e.g., 70%] of participants |
5. Project Needs
- Learning Resources: Initiator Dex will curate and provide a core paper list and supplementary materials based on the UIUC CS598 Topics in LLM Agents course and recent research.
- Collaboration Platform: Primarily use the LXDAO Telegram group (https://t.me/LXDAO/) for daily communication, material sharing, check-ins, and discussions.
- Promotion Resources: Request assistance from LXDAO official channels (e.g., Forum, Twitter) for promoting the co-learning group’s recruitment and subsequent achievements.
- Organizational Support: Initiator Dex will be responsible for overall organization, content guidance, and community facilitation.