5 citations · 5 across the 5 of their papers we have counts for
5 papers
Is Serving-Time Language-Model Inference Necessary for Recommendation? Evidence from Generative and Agentic Settings
Kong Wang, Zhongke He, Xiang Chen +4
Language models (LMs) are increasingly used in recommender systems to generate recommendations or coordinate recommendation tools. However, does their strong performance imply that…
FinToolBench: Evaluating LLM Agents for Real-World Financial Tool Use
Jiaxuan Lu, Kong Wang, Yemin Wang +9
The integration of Large Language Models (LLMs) into the financial domain is driving a paradigm shift from passive information retrieval to dynamic, agentic interaction. While gene…
ASA: Backbone-Training-Free Representation Engineering for Tool-Calling Agents
Youjin Wang, Run Zhou, Yingjie Ma +6
Adapting LLM agents to domain-specific tool calling remains notably brittle under evolving interfaces. Prompt and schema engineering is easy to deploy but often fragile under distr…
Knowledge forest: a novel model to organize knowledge fragments
Qinghua Zheng, Jun Liu, Hongwei Zeng +3
With the rapid growth of knowledge, it shows a steady trend of knowledge fragmentization. Knowledge fragmentization manifests as that the knowledge related to a specific topic in a…
Extended Answer and Uncertainty Aware Neural Question Generation
Hongwei Zeng, Zhuo Zhi, Jun Liu +1
In this paper, we study automatic question generation, the task of creating questions from corresponding text passages where some certain spans of the text can serve as the answers…