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Jinlin Wang

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3 papers

2 author rows

AIIM Journal 2026 Journal Article

Double Graph Attention Network for predicting non-alcoholic fatty liver disease in patients with type 2 diabetes

Tianbin Chen
Yongbin Zeng
Jinlin Wang
Xiao Sun
Sihao Liu
Ya Fu
Qiang Yi
Qishui Ou

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease, while non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease, which can progress to more severe liver diseases such as liver fibrosis, cirrhosis and hepatocellular carcinoma. Approximately 50%–70% of T2DM patients also have NAFLD. Traditional diagnostic methods like liver biopsy have limitations, making large-scale screening difficult. In the past decade, machine learning have emerged as crucial tools for assisting in NAFLD diagnosis. In this paper, we propose a novel Dual Graph Attention Network (DGAN) for diagnosing NAFLD in T2DM patients. We model the NAFLD diagnosis problem as a node classification task on graph by using features similarity constructed graph. The model includes a Feature Attention Module to capture feature importance through a feature graph and a Patient Attention Module to evaluate patient importance using graph attention mechanisms. These components enhance the model’s classification accuracy by leveraging both feature and topological information. The model was trained and tested on clinical data from 2402 T2DM patients, demonstrating superior accuracy in identifying NAFLD compared to other models.

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ICLR Conference 2025 Conference Paper

AFlow: Automating Agentic Workflow Generation

Jiayi Zhang 0017
Jinyu Xiang
Zhaoyang Yu 0004
Fengwei Teng
Xionghui Chen
Jiaqi Chen
Mingchen Zhuge
Xin Cheng

Large language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains, typically by employing agentic workflows that follow detailed instructions and operational sequences. However, constructing these workflows requires significant human effort, limiting scalability and generalizability. Recent research has sought to automate the generation and optimization of these workflows, but existing methods still rely on initial manual setup and fall short of achieving fully automated and effective workflow generation. To address this challenge, we reformulate workflow optimization as a search problem over code-represented workflows, where LLM-invoking nodes are connected by edges. We introduce AFLOW, an automated framework that efficiently explores this space using Monte Carlo Tree Search, iteratively refining workflows through code modification, tree-structured experience, and execution feedback. Empirical evaluations across six benchmark datasets demonstrate AFLOW's efficacy, yielding a 5.7% average improvement over state-of-the-art baselines. Furthermore, AFLOW enables smaller models to outperform GPT-4o on specific tasks at 4.55% of its inference cost in dollars. The code is available at https://github.com/FoundationAgents/AFlow.

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ICLR Conference 2024 Conference Paper

MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Sirui Hong
Mingchen Zhuge
Jonathan Chen
Xiawu Zheng
Yuheng Cheng
Jinlin Wang
Ceyao Zhang
Zili Wang

Recently, remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Previous LLM-based multi-agent systems can already solve simple dialogue tasks. More complex tasks, however, face challenges through logic inconsistencies due to cascading hallucinations caused by naively chaining LLMs. Here we introduce MetaGPT, an innovative meta-programming framework incorporating efficient human workflows into LLM-based multi-agent collaborations. MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for more streamlined workflows, thus allowing agents with human-like domain expertise to verify intermediate results and reduce errors. MetaGPT utilizes an assembly line paradigm to assign diverse roles to various agents, efficiently breaking down complex tasks into subtasks involving many agents working together. On collaborative software engineering benchmarks, MetaGPT generates more coherent solutions than previous chat-based multi-agent systems.

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