Traditional in-vehicle agents mainly follow an instruction-following paradigm, where actions are executed only after explicit user commands. However, real-world cabin scenarios often require agents to proactively determine whether intervention is necessary by jointly reasoning over visual and state inputs. To address this gap, we propose CabinProAct, a multimodal benchmark for proactive intelligent cabin agents. In this setting, agents must jointly reason over in-cabin and out-cabin visual information, vehicle states, and executable APIs to autonomously decide whether to intervene and generate corresponding actions. We further introduce an execution-verification-based synthetic data generation framework to automatically construct execution-consistent proactive interaction data. We evaluate six state-of-the-art multimodal models. The results indicate that execution-grounded synthetic supervision effectively transfers to real-world in-cabin settings, enabling more robust proactive responses in real-world cabin scenarios.

“本文介绍了本队伍在CCL-2023汉语学习者文本纠错评测大赛赛道一中提交的参赛系统。近年来,大规模的中文预训练模型在各种任务上表现出色,而不同的预训练模型在特定任务上也各有优势。然而,由于汉语学习者文本纠错任务存在语法错误复杂和纠错语料稀缺等特点,因此采用基于序列标记的预训练文本纠错模型来解决问题是自然的选择。我们的团队采用了序列到序列的纠错模型,并采取了两阶段训练策略,设计了一套基于序列到序列文本纠错的pipeline。首先,我们对训练集数据进行了清洗处理;在第一阶段训练中,我们在训练集上使用数据增强技术;在第二阶段,我们利用验证集进行微调,并最终采用多个模型投票集成的方式完成后处理。在实际的系统测评中,我们提交的结果在封闭任务排行榜上超出baseline模型17.01分(40.59->57.6)。”