Paper: arXiv 2610.03174

Authors: Xiangyu Li, Fengbin Zhu, Xuan Yao, Siyu Liu, Xiaoluan Liu, Chao Wang, Huanbo Luan, Xiaofen Xing, Xiangmin Xu, Ke-Wei Huang, Richang Hong, Tat-Seng Chua

Abstract

Deep Research (DR) agents have demonstrated strong capabilities in complex, research-oriented tasks through autonomous planning, iterative retrieval, multi-step reasoning, and structured reporting. However, adapting DR agents to finance introduces unique challenges: financial analysis demands the joint completion of heterogeneous sub-tasks spanning diverse data types, tools, and analytical workflows. We identify three key requirements for a professional financial DR agent: integration of authoritative, heterogeneous financial data sources; specialized analytical tools and skills; and dedicated sub-agents for domain-specific sub-tasks. Building on these principles, we propose FinNextAssist, an end-to-end deep research framework designed for professional financial analysis. FinNextAssist decomposes the research process into four stages: Task Planner, Evidence Compiler, Reasoning Engine, and Report Assembler, and introduces two novel lightweight sub-agents: TabAgent, for cross-market financial table understanding, and HeteroAgent, for cross-modality heterogeneous financial data interpretation. Extensive experiments on FinDeepResearch, the Finance Agent Benchmark, and FinTMMBench-Web show that FinNextAssist substantially outperforms both strong proprietary and open-source DR agents, with ablation studies confirming the contribution of each component across diverse markets and languages.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel framework for financial deep research agents, addressing critical challenges in data heterogeneity and domain-specific reasoning. Its strong empirical validation across multiple benchmarks, coupled with a well-structured methodological approach, indicates high potential impact and practical applicability.

Research Flowchart

  flowchart TD
    A[Research Goal: Professional Financial Deep Research] --> B{FinNextAssist Framework};
    B --> C[Methodology: Task Planner, Evidence Compiler, Reasoning Engine, Report Assembler];
    C --> D[Novel Sub-Agents: TabAgent (cross-market tables), HeteroAgent (cross-modality data)];
    D --> E[Data/Inputs: Authoritative, Heterogeneous Financial Data Sources];
    E --> F[Computational Processes: Autonomous Planning, Iterative Retrieval, Multi-step Reasoning];
    F --> G[Outcomes: Substantially outperforms existing DR agents on FinDeepResearch, Finance Agent Benchmark, FinTMMBench-Web];