5 citations · 11 across the 14 of their papers we have counts for
15 papers
Articulate Intuition or Genuine Analysis? Benchmarking Epistemic Reliability in LLM-as-a-Judge Peer Reviews
Nuo Chen, Qian Wang, Qingyun Zou +1
When an LLM judge calls a peer review analytical and a human committee calls another review high quality, are they tracking the same thing? We argue they are not, and that the diff…
Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges
Qian Wang, Zhanzhi Lou, Zhenheng Tang +2
LLMs increasingly serve as automated judges, but their judgments remain vulnerable to cognitive biases. Existing mitigations mostly rely on prompt-driven debiasing, which is brittl…
MHRC-Bench: A Multilingual Hardware Repository-Level Code Completion benchmark
Qingyun Zou, Jiahao Cui, Nuo Chen +2
Large language models (LLMs) have achieved strong performance on code completion tasks in general-purpose programming languages. However, existing repository-level code completion…
PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing
Junyi Hou, Andre Lin Huikai, Nuo Chen +2
Large language models are increasingly embedded into academic writing workflows, yet existing assistants remain external to the editor, preventing deep interaction with document st…
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…
Exploring LLM Cryptocurrency Trading Through Fact-Subjectivity Aware Reasoning
Qian Wang, Yuchen Gao, Zhenheng Tang +3
While many studies show that more advanced LLMs excel in tasks such as mathematics and coding, we observe that in cryptocurrency trading, stronger LLMs sometimes underperform compa…
MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs
Qian Wang, Tianyu Wang, Zhenheng Tang +4
LLM-based multi-agent systems (MAS) have shown promise in tackling complex tasks. However, existing solutions often suffer from limited agent coordination and heavy reliance on pre…
A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading
Yuan Li, Bingqiao Luo, Qian Wang +3
The utilization of Large Language Models (LLMs) in financial trading has primarily been concentrated within the stock market, aiding in economic and financial decisions. Yet, the u…