Urban rail transit systems require reliable decision-support methods to assist operators in handling complex fault events. Although large language models provide strong reasoning and knowledge integration capabilities, their open-ended generation may produce responses that are difficult to align with operational proced...
Wei Zhang, Hui Fang, Jiajun Bu et al.· Applied Informatics· 0 citations
Results indicate that the proposed multi-stage post-training framework effectively improves LLM performance on real-world text-based fault diagnosis, and provides a practical and extensible solution for intelligent maintenance decision support in complex electromechanical systems.
Recently, Large Language Models (LLMs) have become the dominant paradigm for molecular editing due to their strong generalization capabilities across diverse tasks. However, treating molecules as 1D text strings (SMILES) introduces significant challenges in controllability and structural validity. In this paper, we que...
Jiajun Yu, Zhihao Wu, Yizhen Zheng et al.· Proceedings of the 32nd ACM...· 0 citations
This work demonstrates that structure-aware discrete tokenization is a feasible way toward general-purpose graph–language models capable of structural reasoning, and preserves full structural fidelity while enabling LLMs to natively reason over compositional graphlets through graph-level next-token prediction.
Zhonghao Wang, Yugang Ji, Zhuonan Zheng et al.· Proceedings of the 32nd ACM...· 0 citations
This paper proposes UniEdit, a Unified Graph-based Mixture-of-Experts (MoE) Molecular Editing model that offers a robust alternative to LLMs and incorporates a Mixture-of-Experts architecture that dynamically routes tasks to specialized components.
Jiajun Yu, Zhihao Wu, Yizhen Zheng et al.· Proceedings of the 32nd ACM...· 0 citations
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