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HybridFlow: Adaptive Task Scheduling for Fast and Token-Efficient LLM Inference in Edge-Cloud Collaboration

Published in ICML, 2026

HybridFlow introduces an adaptive task scheduling framework for efficient LLM inference across edge-cloud environments, optimizing both latency and token usage.

Recommended citation: Dong, Jiangwen, Jiayu Li, and Wanyu Lin. "HybridFlow: Adaptive Task Scheduling for Fast and Token-Efficient LLM Inference in Edge-Cloud Collaboration." ICML 2026.
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S-DAG: A Subject-Based Directed Acyclic Graph for Multi-Agent Heterogeneous Reasoning

Published in AAAI Conference on Artificial Intelligence, 2026

We propose S-DAG, a subject-based directed acyclic graph framework for multi-agent heterogeneous reasoning that enables structured collaboration among diverse LLM agents.

Recommended citation: Dong, Jiangwen, Zehui Lin, Wanyu Lin, and Mingjin Zhang. "S-DAG: A Subject-Based Directed Acyclic Graph for Multi-Agent Heterogeneous Reasoning." AAAI 2026.
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Jiangwen Dong

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HybridFlow: Adaptive Task Scheduling for Fast and Token-Efficient LLM Inference in Edge-Cloud Collaboration

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

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