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arXiv:2608.07533v1 Announce Type: new Abstract: An embodied agent is an intelligent entity that interacts with its environment through a physical body. Currently, the evaluation of embodied agents primarily relies on tw
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当前，具身智能体的评估主要依赖两类方法：在模拟器中运行特定任务，或使用静态基准数据集进行测试。然而，这些方法往往无法充分检验智能体在动态、多变环境下的空间理解能力。MetaSpace 的核心思路是通过蜕变测试来生成多样化的测试用例。蜕变测试是一种通过检查程序多次执行之间是否满足必要关系（蜕变关系）来发现缺陷的技术。在这项工作中，研究者将其应用于空间认知领域，通过系统性地变换环境配置或任务参数，观察智能体的行为是否遵循应有的空间逻辑一致性，从而更全面地揭示其能力的边界与潜在缺陷。
在相关研究中，空间理解与多智能体协作正成为推动具身智能发展的关键方向。一项同期发布的工作提出了连续度量场框架，通过单一因果对比损失函数将场景编码为固定系数，使智能体能够学习跨维度的几何表征，这为空间认知提供了新的底层建模思路。而在应用层面，卡内基梅隆大学的研究团队发布了 CMU-Drive 与 V2V-VLA 模型，专注于车与车之间的视觉-语言-动作协同，通过推理基准测试推动多智能体在自动驾驶中的统一协作。这些进展从不同侧面反映出，无论是单智能体的空间感知深度，还是多智能体间的空间协同推理，都迫切需要像 MetaSpace 这样更严格、更系统的评估手段来验证其真实水平。
此外，评估方法论的革新也是人工智能领域当下的一个热点。同期发表的关于“评估式人工智能”的研究主张，AI 系统不应只给出单一建议，而应通过呈现正反论据来辅助人类决策。这与 MetaSpace 不满足于简单“通过/失败”判断、转而深入探查智能体空间推理一致性的理念不谋而合。尽管原文未提供 MetaSpace 框架的具体实验数据与蜕变关系定义细节，但其提出的方向为构建更可信、更鲁棒的具身智能体提供了重要的测试基准设计思路。
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arXiv:2608.07533v1 Announce Type: new Abstract: An embodied agent is an intelligent entity that interacts with its environment through a physical body. Currently, the evaluation of embodied agents primarily relies on two paradigms: (1) manually annotated Visual Question Answering (VQA) pairs and (2) high-level task completion metrics, such as success in navigation or manipulation. The former is labor-intensive and subject to variability in annotation quality. The latter may obscure critical vulnerabilities, allowing agents to complete tasks through suboptimal means or safety violations, thereby concealing safety risks and inefficiencies. Given that spatial cognition is the cornerstone for executing embodied tasks, there is a pressing need to assess whether embodied agents possess robust spatial cognition during task execution. Inspired by metamorphic testing principles in software engineering, we propose MetaSpace, a novel framework designed to evaluate the spatial cognition of agents. By leveraging spatiotemporal multimodal states derived from real execution trajectories, MetaSpace automatically generates test cases based on predefined metamorphic relations (MRs) grounded in logical rules and physical laws. Crucially, we encode these MRs as executable rules in a logic programming language (Prolog). Violations of these relations indicate failures in spatial cognition. Our empirical evaluation across three embodied scenarios demonstrates that MetaSpace successfully detects 90,422 spatial cognition errors in state-of-the-art (SOTA) MLLM-driven agents. We introduce the Spatial Cognition (SC) score to quantify performance. Results indicate that all SOTA agents achieve average scores between 0.44 and 0.52, significantly lower than the human benchmark of 0.96.