Tootfinder

QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance
Haoxue Wang, Keli Wen, Yuante Li, Qiancheng Qu, Xiangxu Mu, Xinjie Shen, Jiaqi Gao, Chenyang Chang, Chuhan Xie, San Yu Cheung, Zhuoyuan Hu, Xinyu Wang, Sirui Bi, Bi'an Du
https://arxiv.org/abs/2509.21507

QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance
Quantitative research increasingly relies on unstructured financial content such as filings, earnings calls, and research notes, yet existing LLM and RAG pipelines struggle with point-in-time correctness, evidence attribution, and integration into research workflows. To tackle this, We present QuantMind, an intelligent knowledge extraction and retrieval framework tailored to quantitative finance. QuantMind adopts a two-stage architecture: (i) a knowledge extraction stage that transforms heterog…

Observation Matrix Design for Densifying MIMO Channel Estimation via 2D Ice Filling
Zijian Zhang, Mingyao Cui
https://arxiv.org/abs/2510.08887 https://arxi…

Observation Matrix Design for Densifying MIMO Channel Estimation via 2D Ice Filling
In recent years, densifying multiple-input multiple-output (MIMO) has attracted much attention from the communication community. Thanks to the subwavelength antenna spacing, the strong correlations among densifying antennas provide sufficient prior knowledge about channel state information (CSI). This inspires the careful design of observation matrices (e.g., transmit precoders and receive combiners), that exploits the CSI prior knowledge, to boost channel estimation performance. Aligned with t…