3 citations · 4 across the 7 of their papers we have counts for
7 papers
X-Alpha: An AI Quant Researcher with Memory-Driven Self-Evolution
Fengyuan Liu, Yuchen Fu, Yuqi Wang +2
Human quantitative researchers mine alpha through an iterative process: they derive hypotheses from financial knowledge, implement and test them as factors, and use the resulting e…
Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Models
Yibin Zhao, Fangxin Shang, Dingrui Yang +1
Table question answering requires models to recover semantic relations encoded implicitly by two-dimensional layout, merged cells, and hierarchical headers. Current pipelines typic…
Cognitive Alpha Mining via LLM-Driven Code-Based Evolution
Fengyuan Liu, Yi Huang, Sichun Luo +6
Discovering effective predictive signals, or "alphas," from financial data with high dimensionality and extremely low signal-to-noise ratio remains a difficult open problem. Despit…
Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models
Piotr Padlewski, Max Bain, Matthew Henderson +19
We introduce Vibe-Eval: a new open benchmark and framework for evaluating multimodal chat models. Vibe-Eval consists of 269 visual understanding prompts, including 100 of hard diff…
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models
Reka Team, Aitor Ormazabal, Che Zheng +23
We introduce Reka Core, Flash, and Edge, a series of powerful multimodal language models trained from scratch by Reka. Reka models are able to process and reason with text, images,…
Unlock the Potential of Large Language Models for Predictive Tabular Tasks in Data Science with Table-Specific Pretraining
Yazheng Yang, Yuqi Wang, Yaxuan Li +4
In the domain of data science, the predictive tasks of classification, regression, and imputation of missing values are commonly encountered challenges associated with tabular data…
Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models
Lei Li, Yuqi Wang, Runxin Xu +4
Large vision-language models (LVLMs) excel across diverse tasks involving concrete images from natural scenes. However, their ability to interpret abstract figures, such as geometr…