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Open nowPosted 40 days ago

大语言模型benchmark科研实习生

sii-czxy/campus170 open roles

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  6. 44.6%60 days
This job: posted 40 days ago

The posting

1. 调研 Agent Coding 前沿技术：阅读并整理 Coding Agent、多智能体协作、Agent Harness、长程任务规划、大语言模型后训练等方向的论文和开源系统，跟踪领域最新进展。 2. 参与小游戏开发智能体构建：搭建和优化面向微信小游戏研发流程的多智能体系统，使策划、美术、编程、测试等不同角色 Agent 能够协同完成游戏开发任务。 3. 开发 Agent 协作基础设施：实现并维护任务 Schema、工具接口、Agent 交接协议、Sandbox 运行环境以及自动修复机制，提升智能体执行复杂任务的可靠性。 4. 开展模型评测与实验分析：运行不同大模型和 Agent 组织方式下的小游戏生成实验，分析长任务执行失败、工具调用错误、代码与素材不一致等问题，并提出优化方案。 5. 参与 Agent 后训练研究：基于智能体执行过程中的成功与失败案例，探索数据构建、反馈优化和模型后训练方法，提升 Agent 的任务规划、代码生成和自主修复能力。 6. 参与科研实验与成果整理：设计新的 Agent 工作流和实验方案，完成实验记录、结果分析、技术文档整理及组会汇报。

1. 招募对象：本科生、硕士研究生。 2. 计算机科学、人工智能、软件工程、自动化等相关专业背景优先。 3. 具备较好的 Python 编程能力，有 JavaScript/TypeScript、Web 开发或小游戏开发经验者优先。 4. 了解大语言模型（LLM）、智能体（Agent）、机器学习或深度学习基本概念，能够阅读英文论文和技术文档。 5. 有 Coding Agent、多智能体系统、LLM 应用开发、模型微调、强化学习或开源项目经验者优先。 6. 对游戏开发、AI Agent 或自动化软件研发具有兴趣，有游戏体验或开发经历者优先。 7. 具备较强的问题拆解能力、工程实践能力和快速学习能力，能够主动探索并解决实验中的技术问题。 8. 不要求已有正式论文或科研经历，更关注编程能力、学习能力、动手能力以及对 Agent 研究的持续兴趣。 9. 实习安排：线上线下结合；周期3–6个月；每周投入10–20小时。

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