Leming Shen

Edge AI development has relied on developers hand-building fixed agent workflows that struggle with heterogeneous data modalities and diverse task pipelines. SE2 distills development into four atomic operations, retrieve, reason, code and test, and lets a coordinator agent pick the next one from the current context, while mobile runtime feedback and reinforcement fine-tuning let the agents keep evolving. On the IoTBench benchmark and three multimodal edge AI applications, its synthesized programs reach up to 26% higher accuracy and 28% lower resource consumption than eight state-of-the-art baselines.

Edge AI development has relied on developers hand-building fixed agent workflows that struggle with heterogeneous data modalities and diverse task pipelines. SE2 distills development into four atomic operations, retrieve, reason, code and test, and lets a coordinator agent pick the next one from the current context, while mobile runtime feedback and reinforcement fine-tuning let the agents keep evolving. On the IoTBench benchmark and three multimodal edge AI applications, its synthesized programs reach up to 26% higher accuracy and 28% lower resource consumption than eight state-of-the-art baselines.