1 citations · 1 across the 4 of their papers we have counts for
4 papers
From History to State: Constant-Context Skill Learning for LLM Agents
Haoyang Xie, Xinyuan Wang, Yancheng Wang +2
Large language model (LLM) agents are increasingly used to operate browsers, files, code and tools, making personal assistants a natural deployment target. Yet personal agents face…
Empowering Reliable Visual-Centric Instruction Following in MLLMs
Weilei He, Feng Ju, Zhiyuan Fan +3
Evaluating the instruction-following (IF) capabilities of Multimodal Large Language Models (MLLMs) is essential for rigorously assessing how faithfully model outputs adhere to user…
Reasoning Path Divergence: A New Metric and Curation Strategy to Unlock LLM Diverse Thinking
Feng Ju, Zeyu Qin, Rui Min +3
While Test-Time Scaling (TTS) has proven effective in improving the reasoning ability of large language models (LLMs), low diversity in model outputs often becomes a bottleneck; th…
Text-to-CadQuery: A New Paradigm for CAD Generation with Scalable Large Model Capabilities
Haoyang Xie, Feng Ju
Computer-aided design (CAD) is fundamental to modern engineering and manufacturing, but creating CAD models still requires expert knowledge and specialized software. Recent advance…