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
Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps. Sandbox training produces a distribution mismatch with production environments; expensive real-device failures remain underutilized; and fixed benchmarks saturate, losi...
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
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