DIRECT 解决了具身规划中测试时计算资源浪费的问题，做机器人部署和 VLM 应用的团队可以直接参考其路由策略，在降低成本的同时保持性能。
DIRECT 是一个路由框架，利用多模态场景上下文为每个提示分配测试时计算资源，以改善成功-成本帕累托前沿。研究发现，在链式思维深度、模型大小和记忆历史三个缩放轴上，测试时计算并非均匀杠杆，不同轴带来不同能力增益。在 VLABench 和 RoboMME 上的实验表明，DIRECT 在物理 Franka 机械臂上匹配或超越更强模型的成功率，同时平均延迟降低高达 65%。该工作揭示了朴素缩放测试时计算的浪费性，为具身代理的部署提供了更高效的方案。
DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
Vision-Language Models (VLMs) are increasingly deployed as high-level planners for embodied agents, with an emerging strategy of scaling test-time compute to improve capability. However, we observe that doing so increases latency, token usage, and FLOPs while yielding uneven, often diminishing gains in downstream success, limiting where embodied agents can be deployed. We argue that choosing when and where to spend test-time compute is central to bringing frontier performance to the real world. We introduce DIRECT, a routing framework that uses multimodal scene context to allocate compute per prompt, improving the success--cost Pareto frontier over fixed model selection. Across three dominant scaling axes, namely chain-of-thought depth, model size, and memory history, our experiments on VLABench and RoboMME show that test-time compute is not a uniform lever: different axes yield qualitatively distinct capability gains. We validate these insights on a physical Franka arm in a DROID setup spanning zero-shot manipulation and long-horizon chaining, where our router matches or exceeds a stronger model's success rate at up to 65% lower average latency. Ultimately, our results show that naively scaling test-time compute is wasteful, and that DIRECT can provide frontier-level embodied planning in robotic systems at a fraction of the cost. Project page can be found at jadee-dao.github.io/direct/.