18 citations · 19 across the 7 of their papers we have counts for
7 papers
Test-Time Deep Thinking to Explore Implicit Rules
Wentong Chen, Xin Cong, Zhong Zhang +8
With the continuous advancement of Large Language Models (LLMs), intelligent agents are becoming increasingly vital. However, these agents often fail in environments governed by im…
AgentCPM-Report: Interleaving Drafting and Deepening for Open-Ended Deep Research
Yishan Li, Wentong Chen, Yukun Yan +12
Generating deep research reports requires large-scale information acquisition and the synthesis of insight-driven analysis, posing a significant challenge for current language mode…
MiniCPM4: Ultra-Efficient LLMs on End Devices
MiniCPM Team, Chaojun Xiao, Yuxuan Li +80
This paper introduces MiniCPM4, a highly efficient large language model (LLM) designed explicitly for end-side devices. We achieve this efficiency through systematic innovation in…
YuLan: An Open-source Large Language Model
Yutao Zhu, Kun Zhou, Kelong Mao +35
Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many o…
ICLEval: Evaluating In-Context Learning Ability of Large Language Models
Wentong Chen, Yankai Lin, ZhenHao Zhou +4
In-Context Learning (ICL) is a critical capability of Large Language Models (LLMs) as it empowers them to comprehend and reason across interconnected inputs. Evaluating the ICL abi…
GUICourse: From General Vision Language Models to Versatile GUI Agents
Wentong Chen, Junbo Cui, Jinyi Hu +11
Utilizing Graphic User Interface (GUI) for human-computer interaction is essential for accessing a wide range of digital tools. Recent advancements in Vision Language Models (VLMs)…
Don't Make Your LLM an Evaluation Benchmark Cheater
Kun Zhou, Yutao Zhu, Zhipeng Chen +6
Large language models~(LLMs) have greatly advanced the frontiers of artificial intelligence, attaining remarkable improvement in model capacity. To assess the model performance, a…