FinNextAssist: Towards Professional Financial Deep Research Assistant

2026-10-02

AI Summary

This study addresses the challenges of heterogeneous data integration, the lack of specialized tools, and insufficient subtask coordination in deep financial research by proposing an end-to-end multi-agent collaborative framework. The framework introduces a four-stage analytical pipeline and two novel lightweight sub-agents, TabAgent and HeteroAgent, designed for cross-market tabular parsing and cross-modal data processing, respectively, thereby significantly enhancing domain adaptability. Furthermore, it integrates autonomous planning, iterative retrieval, and structured report generation mechanisms to ensure the coordinated execution of complex tasks. Experimental results demonstrate that the proposed approach substantially outperforms mainstream proprietary and open-source models across multiple benchmarks, while ablation studies validate the effectiveness of each core component.

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.

Problem

• Deep Research Agent
• Financial Analysis
• Heterogeneous Data
• Domain-specific Sub-agents

Innovation

• Deep Research Agent
• Financial Analysis
• Lightweight Sub-agents
• Cross-modality Data Interpretation
• End-to-end Framework