EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation
Complex and long-horizon agent tasks often rely on frontier models deployed in the cloud to achieve strong performance. However, using these models for every step of a long-horizon trajectory incurs substantial inference cost, even though not all steps may require the same level of model capability. In this paper, we find that allocating the LLM to only a small subset of key st
Reading in Plain English mode — toggle below to switch
Complex and long-horizon agent tasks often rely on frontier models deployed in the cloud to achieve strong performance. However, using these models for every step of a long-horizon trajectory incurs substantial inference cost, even though not all steps may require the same level of model capability. In this paper, we find that allocating the LLM to only a small subset of key st
This research addresses the critical challenge of deploying powerful LLM agents efficiently and cost-effectively, especially for long-horizon tasks. It offers a practical approach to leverage smaller, local models while selectively utilizing expensive cloud-based frontier models, directly impacting deployment strategies and operational costs for ML engineers.