Driving vision-language models (VLMs) must accurately understand scenes across diverse conditions defined by Operational Design Domains (ODDs), yet verification remains sparse: many slices are missing, making empirical failure rates unreliable. We propose SliceScorer, a deterministic scoring rule for missing-slice recommendation that combines (i) an exposure-based coverage prior to prioritize rare, under-tested regions, and (ii) a neighbor-failure prior that propagates risk from similar tested conditions. SliceScorer is deliberately simple - interpretable, auditable, and conservative - properties essential for safety-critical validation. For stress testing beyond the declared ODD, we embed SliceScorer within SliceNav, an LLM-orchestrated verification pipeline where the model interprets developer queries to select relevant operators (triage, scoring, acquisition, evaluation) and vocabulary extensions, composing verification workflows while keeping all scoring deterministic and auditable. Experiments on three driving VLMs (WiseAD, DriveMM, Cosmos-Reason2-2B) show that SliceNav surfaces high-risk coverage gaps more effectively than prior slice-discovery methods while maintaining diverse recommendations across the condition space. Ablations confirm both scoring components contribute, and qualitative analysis demonstrates end-to-end workflows from developer query to targeted evaluation.

翻译：摘要：驾驶视觉语言模型必须在由运行设计域（ODD）定义的多样化条件下准确理解场景，然而验证工作依然稀疏：许多切片缺失，导致经验故障率不可靠。我们提出SliceScorer，一种用于缺失切片推荐的确定性评分规则，该规则结合了（i）基于暴露的覆盖先验，优先处理罕见、测试不足的区域，以及（ii）邻域故障先验，从相似测试条件传播风险。SliceScorer刻意保持简洁——可解释、可审计且保守——这些特性对安全关键验证至关重要。为了对声明ODD之外进行压力测试，我们将SliceScorer嵌入到SliceNav中，这是一个由LLM编排的验证流水线，其中模型解释开发者查询以选择相关算子（分诊、评分、采集、评估）和词汇扩展，在保持所有评分确定性和可审计性的同时组合验证工作流。在三个驾驶VLM（WiseAD、DriveMM、Cosmos-Reason2-2B）上的实验表明，SliceNav比先前的切片发现方法更有效地呈现高风险覆盖盲区，同时在条件空间中保持多样化的推荐。消融实验证实两个评分组件均发挥作用，定性分析展示了从开发者查询到目标评估的端到端工作流。