Large language models (LLMs) are increasingly used to make predictions from numerical time-series histories and textual events. Yet accuracy alone cannot reveal whether correct answers reflect effective integration of the two inputs or instead arise from event polarity, unimodal priors, or superficial cues. Likewise, p...
Jie Gong, Mao-Wei Jiang, Zhiwei Liu et al.· 0 citations
This work recast the affective analysis task as a complex-reasoning problem, which yields one output interface across heterogeneous label spaces and a trajectory over which a verifiable reward can be optimised; to the authors' knowledge, this is the first such treatment covering both sentiment and emotion.
Tianlei Zhu, Zhiwei Liu, Yu-Yan Wang et al.· 0 citations
Automated prediction markets require sponsors to prefund liquidity before observing order flow, creating a financing challenge at launch. We study whether nonnegative charges conditioned on observable payoff direction can improve recovery of this prefunded capital while limiting their effect on informed participation....
Yan-Kai Chen, Bowei He, Zhuo-Han Xie et al.· 1 citation
Experiments on Chinese fund-market traces from 2021 to 2026 identify a stable leading group of LLM advisors that combines substantially stronger personalized content with competitive investor-side trajectory outcomes.
Jie Gong, Mao-Wei Jiang, Zhiwei Liu et al.· 3 citations· ⚡1
Simulations show that over-reliance on a weak AI is especially harmful, and that diversifying AI signals across users can better keep the crowd informative, and conclude with implications for understanding human-AI interaction in information spread and designing misinformation interventions.
Zhuo-Ran Lu, Wei-Long Wang, Yang-Yang Yu et al.· Proceedings of the 2026 ACM...· 0 citations
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