5 citations · 12 across the 21 of their papers we have counts for
22 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…
ZenGen: Social Mind for LLMs
ZenGen Team, Ao Xiang, Bi Jingping +57
As large language models move from isolated task solving toward long-term service in human environments, they require social intelligence: the ability to infer mental states, track…
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…
CrossAlpha: An Annual-Report Benchmark for Cross-Market Factor Research (with LLM Agents)
Qian Wang, Zhongyi Tong, Nuo Chen +2
Cross-market factor research studies whether firm-level signals from one or more markets can predict returns in a target market, but existing public benchmarks do not support cross…
VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing
Xiaoyan Su, Peijie Dong, Zhenheng Tang +8
Despite the rapid advancements in Vision-Language Models (VLMs), a critical gap remains in their ability to handle structured, controllable diagrammatic tasks essential for profess…
Is Your LLM-as-a-Recommender Agent Trustable? LLMs' Recommendation is Easily Hacked by Biases (Preferences)
Zichen Tang, Zirui Zhang, Qian Wang +3
Current Large Language Models (LLMs) are gradually exploited in practically valuable agentic workflows such as Deep Research, E-commerce recommendation, and job recruitment. In the…
Are Dilemmas and Conflicts in LLM Alignment Solvable? A View from Priority Graph
Zhenheng Tang, Xiang Liu, Qian Wang +3
As Large Language Models (LLMs) become more powerful and autonomous, they increasingly face conflicts and dilemmas in many scenarios. We first summarize and taxonomize these divers…
Making Bias Non-Predictive: Training Robust LLM Reasoning via Reinforcement Learning
Qian Wang, Xuandong Zhao, Zirui Zhang +4
Large language models (LLMs) increasingly serve as reasoners and automated evaluators, yet they remain susceptible to cognitive biases -- often altering their reasoning when faced…
LLM DNA: Tracing Model Evolution via Functional Representations
Zhaomin Wu, Haodong Zhao, Ziyang Wang +3
The explosive growth of large language models (LLMs) has created a vast but opaque landscape: millions of models exist, yet their evolutionary relationships through fine-tuning, di…
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…
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…
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…
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…