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Open nowPosted 190 days ago

大模型Agent工具体系的自动化演进框架：本体自动构建与迭代优化-集团信息系统（深圳）

ByteDance19,260 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now大模型Agent工具体系的自动化演进框架：本体自动构建与迭代优化-集团信息系统（深圳）ByteDance · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

11.3% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 27 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.3%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 190 days ago

ByteDance median: 27 days open

The posting

团队介绍：集团信息系统部（Corporate Information System）负责字节跳动信息系统建设。通过构建领先的信息系统，助力公司业务和组织的长期高效经营与发展。与此同时，在安全、隐私、合规层面持续治理，为公司的稳健发展保驾护航。字节跳动的业务模式复杂，用户规模大。到现在，集团信息系统部已经覆盖人事、财务、法务、采购、审批、职场等多个领域，隐私安全工作同步开展，AIGC创新孵化也在逐步落地实现。

课题介绍： 随着大模型Agent能力的快速发展，企业级AI助手正从单轮问答走向多工具协同的复杂任务执行。公司内部已接入大量内部MCP服务并封装为Skill供Agent调用。然而，当Skill规模从数十增长至数百甚至上千时，模型的工具选择准确率和参数填充质量显著下降。现有学术研究（ToolLLM、ToolBench等）均假设工具描述由人工编写且固定不变，忽略了两个关键优化维度：一是Skill语义描述本身可作为可优化变量，通过自动化迭代提升模型理解；二是Skill的粒度划分（合并、拆分、重组）直接影响工具选择的搜索空间复杂度。本课题提出SkillForge框架，借鉴Autotool、Automl的自动化实验循环思想，研究大规模Skill体系下描述优化与拓扑结构联合搜索的方法。

课题挑战： 1、Skill间语义边界模糊导致路由冲突，单点优化引发全局回归； 2、合并/拆分涉及Schema兼容与权限约束，搜索空间组合爆炸； 3、缺乏标准化的端到端评估体系与真实场景Benchmark。

课题价值： 1、技术价值：填补Tool Description自动优化与Tool Ontology联合搜索的学术空白，预期产出顶会论文； 2、业务价值：直接提升企业Agent的工具调用准确率，降低Skill运营的人工维护成本。

1、2027届毕业，获得博士学位，有人工智能、计算机、软件工程等相关专业的学术研究经历优先； 2、扎实的算法和数据结构基础，熟练掌握Python/Go/C++等至少一种编程语言； 3、动手能力强，熟悉NLP/CV领域的理论基础，掌握统计模型、机器学习相关原理，具有实战经验，参与过相关项目； 4、学习能力优秀，有良好的团队合作精神，有责任心； 5、熟悉业界领先大语言模型（GPT系列、LLaMA、ChatGLM等）的原理与训练方法，有NLP、计算机视觉、多模态大模型等科研经历或者对LLM有实践应用经历优先； 6、在顶级学术会议（如CVPR、ICCV、ECCV等）发表论文者优先；在Kaggle，COCO，ImageNet，ActivityNet，ICPC，NOI/IOI等比赛获奖者优先。

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