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, and we can budget inference at the planning level. We introduce DIRECT, a routing framework that uses multimodal scene context to allocate compute at each step of a closed-loop agent, selecting a p...
| Authors | Jadelynn Dao, Milan Ganai, Yasmina Abukhadra et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 June 2026 |
| Last updated at source | 13 October 2026 |
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