5 citations · 11 across the 14 of their papers we have counts for
8 papers · 1 filter
Beyond Brainstorming: What Drives High-Quality Scientific Ideas? Lessons from Multi-Agent Collaboration
Nuo Chen, Yicheng Tong, Jiaying Wu +5
While AI agents show potential in scientific ideation, most existing frameworks rely on single-agent refinement, limiting creativity due to bounded knowledge and perspective. Inspi…
Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference
Nuo Chen, Moming Duan, Andre Huikai Lin +3
Artificial Intelligence (AI) conferences are essential for advancing research, sharing knowledge, and fostering academic community. However, their rapid expansion has rendered the…
Towards Evaluting Fake Reasoning Bias in Language Models
Qian Wang, Zhenheng Tang, Zhanzhi Lou +3
Large Reasoning Models (LRMs), evolved from standard Large Language Models (LLMs), are increasingly utilized as automated judges because of their explicit reasoning processes. Yet…
HLStrans: Dataset for C-to-HLS Hardware Code Synthesis
Qingyun Zou, Nuo Chen, Yao Chen +2
High-Level Synthesis (HLS) enables hardware design from C/C++ kernels but requires extensive transformations, such as restructuring code, inserting pragmas, adapting data types, an…
XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration
Nuo Chen, Andre Lin HuiKai, Jiaying Wu +5
Despite the growing adoption of large language models (LLMs) in academic workflows, their capabilities remain limited in supporting high-quality scientific writing. Most existing s…
Assessing Judging Bias in Large Reasoning Models: An Empirical Study
Qian Wang, Zhanzhi Lou, Zhenheng Tang +5
Large Reasoning Models (LRMs) like DeepSeek-R1 and OpenAI-o1 have demonstrated remarkable reasoning capabilities, raising important questions about their biases in LLM-as-a-judge s…
JudgeLRM: Large Reasoning Models as a Judge
Nuo Chen, Zhiyuan Hu, Qingyun Zou +4
Large Language Models (LLMs) are increasingly adopted as evaluators, offering a scalable alternative to human annotation. However, existing supervised fine-tuning (SFT) approaches…
LLM-based Human Simulations Have Not Yet Been Reliable
Qian Wang, Jiaying Wu, Zichen Jiang +6
Large Language Models (LLMs) are increasingly employed for simulating human behaviors across diverse domains. However, our position is that current LLM-based human simulations rema…