Jiangwen Dong — accepted AI-conference papers

2026

HybridFlow: Resource-Adaptive Subtask Routing for Efficient Edge-Cloud LLM Inference

Jiangwen Dong, Jiayu Li, Tianhang Zheng, Wanyu LIN

ICML 2026poster

Edge-cloud collaborative inference is crucial for LLM-powered edge devices, as on-device models often lack the required reasoning capability, while cloud-only inference can be costly and slow under strict latency and token/API budgets. However, existing edge-cloud collaboration methods typically rou…

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2026

S-DAG: A Subject-Based Directed Acyclic Graph for Multi-Agent Heterogeneous Reasoning

Jiangwen Dong, Zehui Lin, Wanyu Lin, Mingjin Zhang

AAAI 2026technical

Large Language Models (LLMs) have achieved impressive performance in complex reasoning problems. Their effectiveness highly depends on the specific nature of the task, especially the required domain knowledge. Existing approaches, such as mixture-of-experts, typically operate at the task level; they

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