arXiv:2606.12402v1 Announce Type: cross Abstract: 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
The proliferation of high-latency, high-cost Vision-Language Models in embodied AI necessitates immediate solutions for efficient compute allocation.
Optimizing test-time compute for embodied planners directly impacts the economic viability and practical deployment of AI agents in real-world scenarios.
The focus in embodied AI shifts from merely increasing compute to strategically managing it, enabling more efficient and widespread adoption of VLM-driven agents.
- · AI agent developers
- · Robotics companies
- · Cloud computing providers offering optimization tools
- · Industries deploying embodied AI
- · Developers solely focused on large, unoptimized VLM models
Embodied AI systems become more practical and cost-effective to deploy in diverse applications.
This leads to an acceleration in the adoption and integration of autonomous agents across various industries.
The increased efficiency in compute utilization could contribute to a re-evaluation of energy demands for advanced AI, though likely a minor impact initially.
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Read at arXiv cs.AI