Large Language Models have demonstrated remarkable progress in general-purpose capabilities and can achieve strong performance in specific domains through fine-tuning on domain-specific data. However, acquiring high-quality data for target domains remains a significant challenge. Existing data synthesis approaches foll...
Tong Ye, Hang Yu, Tengfei Ma et al.· Proceedings of the 32nd ACM...· 1 citation
Modelica is an industry-standard language for modeling and simulating complex cyber-physical systems, playing a critical role in digital twin development. Driven by the escalating demand for complex system modeling, there is a growing interest in leveraging the advanced code generation capabilities of Large Language Mo...
Jia-Hui Xiang, Tong Ye, Pei-Yu Liu et al.· Proceedings of the ACM/IEEE...· 0 citations
This research proposes a specialized workflow that integrates supervised fine-tuning, graph retrieval-augmented generation, and feedback optimization to improve the accuracy and reliability of Modelica code generation.
Jia-Hui Xiang, Tong Ye, Pei-Yu Liu et al.· arXiv.org· 8 citations
BlueLM-GUI, a 35B-A3B mobile GUI agent built as a real-device-centric flywheel that closes gaps through three principles, demonstrates that grounding model training and iterative improvement in both real devices and the three Every principles yields strong, robust, and transferable mobile GUI capability.
Tong Ye, Kunyang Han, Guo-Zhi Wang et al.· 0 citations
A multi-agent harness that represents each request as a persistent, artifact-grounded analysis run, and demonstrates that reliable bioinformatics automation depends not only on model capability, but also on the harness that governs planning, execution, memory, and evidence flow.
Qi Shao, Xin Zhang, Zhou-Yang Yuan et al.· 0 citations
This work proposes a novel framework, DOMINO, that learns a minimal sufficient domain representation from reference samples and leverages it to guide the generation of domain-aligned synthetic data, enabling practical and scalable domain adaptation without manual prompt design or natural language domain specifications.
Tong Ye, Hang Yu, Tengfei Ma et al.· Proceedings of the 32nd ACM...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
