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Yingqiang Ge on Muck Rack

Yingqiang Ge

As seen in: arXiv, MDPI
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Articles

Stop Comparing LLM Agents Without Disclosing the Harness

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By Yunbei Zhang, Janet Wang, Yingqiang Ge
| preprints.org
You are already at the latest version This position paper argues that, for long-horizon tasks evaluated across models with comparable frontier capability, the agent execution harness, namely the infrastructure layer that governs context construction, tool interaction, orchestration, and verification around a language model, is often a stronger determinant of agent performance than the model it wraps.
Open in Who Shared Issue with article?

IDGenRec: LLM-RecSys Alignment with Textual ID Learning

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By Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Zelong Li
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (447) Issue with article?

Efficient Non-Sampling Graph Neural Networks

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By Zelong Li, Shuyuan Xu, Jianchao Ji, Yingqiang Ge
| MDPI
Abstract : A graph is a widely used and effective data structure in many applications; it describes the relationships among nodes or entities. Currently, most semi-supervised or unsupervised graph neural network models are trained based on a very basic operation called negative sampling. Usually, the purpose of the learning objective is to maximize the similarity between neighboring nodes while minimizing the similarity between nodes that are not close to each other.
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