发表机构
NVIDIA; University of Ottawa（英伟达; 渥太华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对部署的AI系统训练需数据管理的问题，提出TTCov方法，通过构建任务图谱和知识图谱分别表示覆盖与分布，选择预算训练集，应用于自动驾驶时能提升数据质量及性能，实现对新领域的无缝适应。
AI 中文摘要
部署的人工智能系统通常从广泛的候选数据池中进行训练，因此需要针对部署测试分布进行数据管理。然而，标准数据管理方法基于训练端标准评分，而非直接优化部署匹配度。我们引入了TTCov（测试时覆盖），这是一种数据级测试条件管理方法，在训练前使用测试端信息，而非在推理时更新模型权重。TTCov将基于部署条件的管理分解为覆盖和分布。为表示覆盖，它构建任务图谱，即基于语言模型的原子命题集合，描述与部署相关的概念，从开放任务知识中获取并通过从未标记部署样本中提取的不匹配原子命题进行扩展。为表示分布，它用频率实例化匹配的部署原子命题，生成知识图谱作为管理目标。TTCov随后选择一个预算训练集，其部署原子命题分布接近该目标。我们将TTCov应用于自动驾驶，在推理路径上不进行适配，同时选择与部署相关覆盖更大、知识图谱匹配更紧密且下游端到端驾驶性能更强的数据，包括通过城市间扩展对新领域的无缝适应性。
英文摘要
Deployed AI systems are often trained from broad candidate data pools, necessitating data curation towards the deployment test distribution. However, standard data curation methods score training-side criteria rather than directly optimizing deployment match. We introduce TTCov (Test-Time Coverage), a data-level test-conditioned curation method that uses test-side information before training instead of updating model weights at inference. TTCov decomposes deployment-conditioned curation into coverage and distribution. To represent coverage, it builds a task Atlas, a collection of LLM-based atomic propositions (APs) describing deployment-relevant concepts, seeded from open task knowledge and expanded with unmatched APs extracted from unlabeled deployment samples. To represent distribution, it instantiates the matched deployment APs with their frequencies, yielding a Knowledge Atlas (K-Atlas) that operationalizes the deployment distribution as a curation target. TTCov then selects a budgeted training set whose deployment APs distribution approximates this target. We apply TTCov towards autonomous driving (AD), keeping adaptation off the inference path while selecting data with greater deployment-relevant coverage, closer K-Atlas matching, and stronger downstream end-to-end driving performance than data-curation baselines, including seamless adaptability to novel domains via city-to-city expansion.
