Stock price forecasting is a long-standing challenge in computational finance, driven by the inherent randomness of markets and complex temporal patterns. While recent deep-learning models have raised forecasting accuracy by jointly modeling inter-stock and temporal price dynamics, they conflate inter-stock relationshi...
Heng-Yi Yang, Si-Da Lin, Yi-Yan Qi et al.· 0 citations
Measurement-Gated Provenance Attenuation (MGPA), built on one principle: measurement evidence determines where correction may act, and preserved information determines what it should aim for, brings source to near chance with task performance unchanged.
Anuar Aimoldin, Yan-Kai Chen, Ayana Mussabayeva et al.· 0 citations
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
SpanPM is introduced, a prediction-market mechanism that sets the local-curvature multiplier from each trade's realized payoff spread and preserves 96--97% of the trader surplus achieved with exact Bregman fees while cutting excess fees by 94\% relative to the global quadratic mechanism.
Yan-Kai Chen, Rassul Magauin, Bowei He et al.· 1 citation
ANIMASK, a simulation framework that freezes books and scripts into story worlds whose characters act on their own motivations and replays each story from its freeze point, is introduced.
Xiu-Cheng Zhang, Zhuo-Ning Xu, Han-Jun Luo et al.· 0 citations
This work instantiates AutoCRAT, a decoder-side controller for frozen backbones that operates over a discrete action space and updates control decisions only at semantic boundaries, improving stability while remaining responsive to the evolving reasoning process.
Han-Jun Luo, Qiu-Shi Liu, Jing-Yang Zhang et al.· 0 citations
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