2 citations · 2 across the 4 of their papers we have counts for
5 papers
FinNextAssist: Towards Professional Financial Deep Research Assistant
Xiangyu Li, Fengbin Zhu, Xuan Yao +9
Deep Research (DR) agents have demonstrated strong capabilities in complex, research-oriented tasks through autonomous planning, iterative retrieval, multi-step reasoning, and stru…
FinDeepForecast: A Live Multi-Agent System for Benchmarking Deep Research Agents in Financial Forecasting
Xiangyu Li, Xuan Yao, Guohao Qi +16
Deep Research (DR) Agents powered by advanced Large Language Models (LLMs) have fundamentally shifted the paradigm for completing complex research tasks. Yet, a comprehensive and l…
FinDeepResearch: Evaluating Deep Research Agents in Rigorous Financial Analysis
Fengbin Zhu, Xiang Yao Ng, Ziyang Liu +19
Deep Research (DR) agents, powered by advanced Large Language Models (LLMs), have recently garnered increasing attention for their capability in conducting complex research tasks.…
Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance
Fengbin Zhu, Junfeng Li, Liangming Pan +5
Finance decision-making often relies on in-depth data analysis across various data sources, including financial tables, news articles, stock prices, etc. In this work, we introduce…
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding
Fengbin Zhu, Ziyang Liu, Xiang Yao Ng +6
Large Vision-Language Models (LVLMs) have achieved remarkable performance in many vision-language tasks, yet their capabilities in fine-grained visual understanding remain insuffic…