从幻觉到扎根：通过CRISP诊断视觉空间智能
From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP
AI总结：
提出CRISP诊断范式，通过一致性评估解耦感知与推理，揭示闭源模型存在度量估计不准确和未利用隐式结构表示的问题，开源模型则受限于多跳组合推理能力。
摘要：
当前的视觉语言模型评估常常混淆语言先验与真正的空间推理。为了解决这个问题，我们引入了CRISP，一种新颖的结构诊断评估范式，通过一致性（即隐式感知与显式推理之间的对齐）来评估视觉空间智能。与传统的黑盒问答不同，CRISP利用度量3D场景图和一种预言机干预协议，将潜在推理能力与感知瓶颈解耦。这种细粒度诊断揭示了系统性的感知-推理脱节。关键的是，我们发现虽然闭源模型拥有强大的潜在推理引擎，但它们存在度量估计不准确以及未能利用其隐式结构表示的关键失败。相反，开源模型则根本上受限于缺乏多跳组合推理能力。通过将焦点从仅仅通过语言先验“猜对”转移到真正地“感知、验证和推理”，CRISP为超越端到端后训练的多模态对齐提供了严谨的路线图。代码和数据集可在以下网址获取：https://this https URL。
英文摘要：
Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, the alignment between implicit perception and explicit reasoning. Unlike traditional black-box QA, CRISP utilizes metric 3D Scene Graphs and an oracle intervention protocol to decouple latent reasoning capabilities from perceptual bottlenecks. This granular diagnosis uncovers a systematic perception-reasoning disconnect. Crucially, we reveal that while proprietary models possess robust latent reasoning engines, they suffer from inaccurate metric estimation and a critical failure to leverage their implicit structural representations. Conversely, open-source models remain fundamentally bottlenecked by their lack of multi-hop compositional reasoning. By shifting the focus from merely ``guessing correctly'' via language priors to genuinely ``perceiving, verifying, and reasoning,'' CRISP offers a rigorous roadmap for multimodal alignment beyond end-to-end post-training. The code and dataset are available at https://github.com/iiyamayuki/CRISP-Bench.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
