# FinSight: Towards Real-World Financial Deep Research

arXiv: https://arxiv.org/abs/2510.16844 · HTML: https://mlsanity.com/paper/2510.16844/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 19 Oct 2025.

## Builds on (most cited first, 22 of 22)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [Kimi K2: Open Agentic Intelligence](https://mlsanity.com/paper/2507.20534/) (2025)
- [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024)
- [Search-o1: Agentic Search-Enhanced Large Reasoning Models](https://mlsanity.com/paper/2501.05366/) (2025)
- [WebThinker: Empowering Large Reasoning Models with Deep Research Capability](https://mlsanity.com/paper/2504.21776/) (2025)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research](https://mlsanity.com/paper/2405.13576/) (2024)
- [OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation](https://mlsanity.com/paper/2505.23885/) (2025)
- [AutoSurvey: Large Language Models Can Automatically Write Surveys](https://mlsanity.com/paper/2406.10252/) (2024)
- [AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents](https://mlsanity.com/paper/2502.05957/) (2025)
- [Financial Report Chunking for Effective Retrieval Augmented Generation](https://mlsanity.com/paper/2402.05131/) (2024)
- [FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models](https://mlsanity.com/paper/2405.14767/) (2024)
- [CodeAgent: Autonomous Communicative Agents for Code Review](https://mlsanity.com/paper/2402.02172/) (2024)
- [BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence](https://mlsanity.com/paper/2402.12174/) (2024)
- [Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports From Scratch with Agentic Framework](https://mlsanity.com/paper/2506.02454/) (2025)
- [Template-Based Financial Report Generation in Agentic and Decomposed Information Retrieval](https://mlsanity.com/paper/2504.14233/) (2025)
- [FinTeam: A Multi-Agent Collaborative Intelligence System for Comprehensive Financial Scenarios](https://mlsanity.com/paper/2507.10448/) (2025)
- [FinWorld: An All-in-One Open-Source Platform for End-to-End Financial AI Research and Deployment](https://mlsanity.com/paper/2508.02292/) (2025)

## Built on by (most cited first, 9 of 9)

- [DeepAgent: A General Reasoning Agent with Scalable Toolsets](https://mlsanity.com/paper/2510.21618/) (2025)
- [LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval](https://mlsanity.com/paper/2603.01425/) (2026)
- [TVIR: Building Deep Research Agents Towards Text-Visual Interleaved Report Generation](https://mlsanity.com/paper/2606.02320/) (2026)
- [GEO-Flag: Detecting and Measuring GEO-Optimized Web Content](https://mlsanity.com/paper/2608.16824/) (2026)
- [Advancing ESG Intelligence: An Expert-level Agent and Comprehensive Benchmark for Sustainable Finance](https://mlsanity.com/paper/2601.08676/) (2026)
- [Towards Knowledgeable Deep Research: Framework and Benchmark](https://mlsanity.com/paper/2604.07720/) (2026)
- [VeriGraph: Towards Verifiable Data-Analytic Agents](https://mlsanity.com/paper/2606.16603/) (2026)
- [FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering](https://mlsanity.com/paper/2607.18102/) (2026)
- [FinReportBench: Measuring and Improving Institution-Grade Financial Report Generation](https://mlsanity.com/paper/2608.04374/) (2026)

## Abstract (arXiv)

Generating professional financial reports is a labor-intensive and intellectually demanding process that current AI systems struggle to fully automate. To address this challenge, we introduce FinSight (Financial InSight), a novel multi agent framework for producing high-quality, multimodal financial reports. The foundation of FinSight is the Code Agent with Variable Memory (CAVM) architecture, which unifies external data, designed tools, and agents into a programmable variable space, enabling flexible data collection, analysis and report generation through executable code. To ensure professional-grade visualization, we propose an Iterative Vision-Enhanced Mechanism that progressively refines raw visual outputs into polished financial charts. Furthermore, a two stage Writing Framework expands concise Chain-of-Analysis segments into coherent, citation-aware, and multimodal reports, ensuring both analytical depth and structural consistency. Experiments on various company and industry-level tasks demonstrate that FinSight significantly outperforms all baselines, including leading deep research systems in terms of factual accuracy, analytical depth, and presentation quality, demonstrating a clear path toward generating reports that approach human-expert quality.