10 citations · 19 across the 16 of their papers we have counts for
18 papers
Trading Strategy Optimization via Textual Gradient
Chaoqun Yang, Qian Wang, Fengbin Zhu +4
Quantitative trading strategy design aims to discover trading programs from historical data that remain effective in future markets, which can be viewed as a black-box program opti…
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…
Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation
Nuo Chen, Yicheng Tong, Yuzhe Yang +5
Multi-agent systems (MAS) are increasingly used for open-ended idea generation, driven by the expectation that collective interaction will broaden the exploration diversity. Howeve…
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…
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…
From ChatGPT to DeepSeek: Can LLMs Simulate Humanity?
Qian Wang, Zhenheng Tang, Bingsheng He
Simulation powered by Large Language Models (LLMs) has become a promising method for exploring complex human social behaviors. However, the application of LLMs in simulations prese…
The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?
Zhenheng Tang, Xiang Liu, Qian Wang +4
Motivated by reducing the computational and storage costs of LLMs, model compression and KV cache compression have attracted much attention from researchers. However, current metho…
FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression
Zhenheng Tang, Xueze Kang, Yiming Yin +11
To alleviate hardware scarcity in training large deep neural networks (DNNs), particularly large language models (LLMs), we present FusionLLM, a decentralized training system desig…
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…
Fault-Tolerant Hybrid-Parallel Training at Scale with Reliable and Efficient In-memory Checkpointing
Yuxin Wang, Xueze Kang, Shaohuai Shi +8
To efficiently scale large model (LM) training, researchers transition from data parallelism (DP) to hybrid parallelism (HP) on GPU clusters, which frequently experience hardware a…
FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs
Zhenheng Tang, Yuxin Wang, Xin He +8
The rapid growth of memory and computation requirements of large language models (LLMs) has outpaced the development of hardware, hindering people who lack large-scale high-end GPU…