5 citations · 12 across the 21 of their papers we have counts for
9 papers · 1 filter
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…