3 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
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,…