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arXiv AI By Wei Guan, Jian Cao, Jinyu Cai, Qiqi Cai, Jianqi Gao, See-Kiong Ng

GLOW: Graph-Language Co-Encoding for Agentic Workflow Performance Prediction

Read the original on arXiv AI →

GLOW is a framework that predicts the performance of Agentic Workflows by combining Graph Neural Networks with a graph-oriented Large Language Model. It extracts topology-aware semantic representations from workflow descriptions and fuses them with structural representations via a Transformer-based module, using contrastive learning to enhance discriminative power. Experiments on the FLORA-Bench benchmark show GLOW surpasses existing baselines in accuracy and ranking, and when used in the AFLOW generation system, it cuts optimization time by 98.7% with minimal loss in score.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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