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Open nowPosted 191 days ago

【实习】面向复杂任务的数据智能体：程序推理与自适应演进研究-数据智能（北京/上海/杭州）

ByteDance19,261 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now【实习】面向复杂任务的数据智能体：程序推理与自适应演进研究-数据智能（北京/上海/杭州）ByteDance · Beijing, Chinese Mainland

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11.4% 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. 4.2%3 days
  3. 11.4%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 191 days ago

ByteDance median: 27 days open

The posting

团队介绍：专注大模型在数据智能方向的应用落地，横向支持公司多个数据产品的大模型解决方案。团队致力于跟进大模型前沿进展，结合领域模型的优化策略来设计端到端的算法优化和评估方案，实现数据产品的智能升级与应用效果提升。团队鼓励敏捷创新，知识共享的协作氛围，专注投入对数据智能课题的持续探索研究。

课题介绍： 大语言模型（LLM）驱动的智能Agent在各类任务中展现出强大的推理与交互能力，已成为AI领域的前沿热点。然而，现有大多数Agent设计隐含地基于“确定性假设”，即默认输入信息完全可靠、任务目标明确。这在现实场景中往往并不成立——数据不完整、外部工具执行失败、用户意图随时间演变，使Agent产生误导性甚至错误的结论。面对“信息不确定性”这一核心挑战：课题一方面聚焦知识冲突与推理鲁棒性，另一方面聚焦SQL逻辑依赖与演进维护。

课题挑战： 1、鲁棒Agent推理问题：检索增强生成（RAG）是获取外部知识主流机制，但存在知识冲突问题，当前LLM面对噪声检索表现仍有提升空间； 2、演进式SQL任务开发与维护问题：企业级数据治理中，SQL脚本面临底层数仓Schema变更和业务统计口径演进问题，传统方法无法提供深层语义偏差指导，现有模型在超100行存量SQL维护任务成功率较低。

课题价值： 1、鲁棒Agent推理技术路线：基于POMDP的决策规划，维护信念状态并计算最优行动策略；多策略协同，认知不确定性超阈值时切换并发探索模式；自适应响应，依据信念状态残余不确定性决定输出； 2、演进式SQL任务开发与维护技术路线：SynSQL-Evolution 数据工程，构建百万级合成语料；GRPO强化学习框架，设计复合奖励函数；计划驱动推理范式，输出“逻辑变更清单”；依赖感知Agent协同，结合拓扑规划器处理长序列需求并构建实时纠错循环。

1、2027届及以后毕业，博士在读，人工智能等相关专业优先（如计算机科学、数学、电子工程等）； 2、熟练使用Python、PyTorch等语言和代码库，熟悉大模型领域尤其是强化学习相关研究工作和算法，有大模型强化学习的研发基础，拥有高影响力会议论文(ICLR、NeurIPS、ICML等)、知名竞赛获奖经验者优先； 3、具备优秀的学术视野与判断力，拥有出色的问题提出、分析及解决能力，良好的团队协作精神和沟通意识。

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