Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution
To address this gap, we introduce EC2-Bench, a benchmark for evaluating edge-cloud collaboration in long-horizon agent execution.
ProofPaper ↗
Key points
- Edge-cloud collaboration combines the lower inference overhead and greater data locality of edge-side execution with the stronger capabilities of cloud-hosted models.
- However, as LLM agents increasingly operate in long-horizon scenarios, cloud participation becomes interleaved throughout execution rather than a one-shot decision for each query.
- Using this benchmark, we empirically study diverse forms of edge-cloud collaboration.
- These findings provide a systematic understanding of edge-cloud collaboration in long-horizon agent execution and insights for designing future edge agents that effectively collaborate with cloud models.
Sources (1)
- [1]Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent ExecutionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 06:40 AM
To address this gap, we introduce EC2-Bench, a benchmark for evaluating edge-cloud collaboration in long-horizon agent execution.
Edge-cloud collaboration combines the lower inference overhead and greater data locality of edge-side execution with the stronger capabilities of cloud-hosted models.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 13, 2026Hybrid Reasoning Agents
- Oct 13, 2026Fish Audio Drama 3 Technical Report
- Oct 12, 2026Redesigning source control at Anthropic
- Oct 12, 2026Scheduled Agents, Dreaming and Q&A: Claude Managed Agents (Part 3 of 4) — Anthropic
- Oct 12, 2026AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers
- Oct 12, 2026AUGUstus: Autonomous Driving VLA Model with Unified Generation and Understanding