FINSIGHT: TOWARDS REAL-WORLD FINANCIAL DEEP RESEARCH

## Abstract

Abstract 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 ( Fin ancial In Sight ), 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.

## 1 Introduction

Investment decisions worth billions of dollars hinge on the quality and timeliness of financial research reports (Tian et al., 2025). These reports translate raw market data into strategic insights, serving as analytical support for asset managers, equity researchers, and institutional investors. However, producing such reports remains a challenging task due to the overwhelming volume of financial data and the demand for rapid, high-quality analysis (Ren et al., 2021; Jimeno-Yepes et al., 2024).
Recent advances in artificial intelligence, particularly in reasoning models (OpenAI, 2024; DeepSeek-AI et al., 2025; Bai et al., 2025), deep search and research applications (OpenAI, 2025a; Gemini, 2025; Grok, 2025; Camara, 2025), present great potential in solving these labor-intensive collecting and analyzing tasks. Despite these technical advances, significant challenges persist in automating the generation of full financial research reports that meet the high standards for data accuracy, analytical depth, and multimodal content integration (Yang et al., 2025).

Existing methods face several limitations that hinder their practical adoption:
(1) Lack of Financial Domain Knowledge:
Most current systems, whether closed-source  (OpenAI, 2025a; Grok, 2025; Gemini, 2025) or open-source (Hu et al., 2025; Li et al., 2025b), are designed for general search scenarios, ignoring the integration of real-time heterogeneous financial data (both unstructured articles, news, and structured data).
(2) Limited Multimodal Support and Visualization: Almost all current methods can only produce plain-text reports, lacking diverse visualizations (e.g., figures, charts and tables) that are critical in conveying information (Yang et al., 2025).
(3) Insufficient Analytical Depth: Current methods often rely on rigid, predefined workflows for single-pass data collection (Trivedi et al., 2023; Li et al., 2025a; Jin et al., 2025) and report generation (Chen et al., 2024), preventing them from dynamically adjusting research strategies based on intermediate findings, ultimately limiting the analytical depth and insight of the final report.

To address these challenges, we introduce FinSight, a novel multi-agent system that simulates the cognitive processes and analytical workflows of expert financial researchers. FinSight operates three necessary stages: (1) Data Collection, which gathers up-to-date heterogeneous data and organizes it into a structured multimodal memory. (2) Data Analysis, where an interactive environment enables multi-round interactions with data, tools, and agents to derive a concise Chain-of-Analysis sequence. (3) Report Generation, which follows a draft outline to transform the data and Chain-of-Analysis into a formatted financial report with chart and data references, finally rendered in a professional style.

To realize FinSight, we reconstruct the deep research workflow and propose a novel agent architecture, Code Agent with Variable Memory (CAVM), where all data, tools, and agents are unified into a programmable variable space accessible and manipulable through executable code. This architecture leverages the code capabilities of language models (Wang et al., 2024a; Jiang et al., 2024; Tang et al., 2024), and enables flexible, scalable task handling from bottom-up data operations to high-level workflow orchestration.

To address the critical challenges of multimodal generation and analytical depth, we introduce two specialized mechanisms. To overcome the shortcomings of automated visualization, we propose an Iterative Vision-Enhanced Mechanism, where a vision-language model provides critical feedback to iteratively refine code-generated charts until they meet professional standards. For the challenge of generating coherent, long-form reports, we employ a Two-Stage Writing Framework. This framework first distills insights into concise Chain-of-Analysis segments, which then serve as a structured foundation for the Report Generation Agent to compose a full, context-aware report with tightly integrated visualizations and citations. Our extensive evaluations demonstrate that this synergistic approach enables FinSight to significantly outperform existing methods, delivering reports with superior accuracy, depth, and multimodal coherence.

To comprehensively evaluate our method, we construct a high-quality benchmark featuring research tasks at both the company and industry levels, spanning multiple markets and diverse industries. Experiments demonstrate that our method significantly surpasses various deep research systems across three key dimensions: Factual Accuracy, Analytical Depth, and Presentation Quality, validating that FinSight can generate rich, insightful, and multimodal financial research reports that approach the quality of human experts.

Our core contributions are as follows:

- 1.
A Multi-Agent System for financial analysis based on the Code Agent with Variable Memory (CAVM) architecture, which integrates data, tools, and different agents into a unified programmable variable space, enabling flexible and scalable data collection, analysis, and report generation.
- 2.
An Iterative Vision-Enhanced Mechanism for professional chart generation that integrates the code-generation capabilities of large language models with the visual understanding of vision–language models to iteratively refine basic charts into professional-quality visualizations.
- 3.
A Two-stage Writing Framework with Generative Retrieval that progresses from short and concise Chain-of-Analysis segments to long and comprehensive financial reports, seamlessly integrating textual analysis with visual elements to meet the need for real-world financial multimodal deep research.

## 2 Method

Figure: Figure 1: Overview of the FinSight Framework.
Refer to caption: x1.png

In this section, we give a definition of the multimodal financial report generation task (Section [2.1](https://arxiv.org/html/2510.16844v1#S2.SS1)) and present the foundational Code Agent with Variable Memory (CAVM) architecture (Section [2.3](https://arxiv.org/html/2510.16844v1#S2.SS3)), the Iterative Vision-Enhanced Mechanism (Section [2.4](https://arxiv.org/html/2510.16844v1#S2.SS4)) and Two-Stage Writing Framework with Generative Retrieval (Section [2.5](https://arxiv.org/html/2510.16844v1#S2.SS5)).

### 2.1 Problem Formulation

We formalize the generation of open-domain multimodal financial research reports as follows. Given a research question $q$ (e.g., Research the development of the robotics industry), the system is required to generate a structure report $R$:

$$ $R=\{r_{1},r_{2},\cdots,r_{L}\},r_{i}\in\{T,V,C\},$ $$

where:

- •
Texts: $T=\{t_{1},t_{2},...,t_{n}\}$ represents the textual analysis, such as executive summary, insights, conclusions and others.
- •
Visualizations: $V=\{v_{1},v_{2},...,v_{m}\}$ denotes the visualizations, such as charts, graphs, and tables that can support textual analysis or add more information.
- •
Citations: $C=\{c_{1},c_{2},...,c_{k}\}$ contains citations and references to data sources.

### 2.2 The Framework of FinSight

FinSight is a multi-agent system designed to simulate the workflow of a professional financial analyst. The system realizes three core processes: multi-source data collection, multi-turn data analysis and progressive report writing, implemented through the CAVM architecture described in Section [2.3](https://arxiv.org/html/2510.16844v1#S2.SS3). The key design of this framework will be detailed in the following sections.

#### Data Collection

To address the limitations of general web search systems in financial domains, we design two specialized agents for comprehensive data gathering: (1) Deep Search Agent: Conducts iterative, multi-round investigations using search engines and virtual browsers to gather comprehensive information with source verification. (2) Multi-Source Data Collection Agent: Collects heterogeneous data from financial databases, APIs, and web sources, leveraging different tools to access diverse information types. It can invoke the deep search agent for specific information requirements.
Instead of treating data collection as an isolated preliminary step, FinSight allows the analysis and writing stages to dynamically invoke further data collection, ensuring broader and more relevant knowledge coverage.

#### Data Analysis

Built on CAVM, the Data Analysis Agent executes analytical tasks via multi-turn code actions, dynamically deciding when to process data, invoke data collection workflows, or terminate with a concise Chain-of-Analysis (CoA) output (Section [2.5](https://arxiv.org/html/2510.16844v1#S2.SS5)). It integrates the Iterative Vision-Enhanced Mechanism (Section [2.4](https://arxiv.org/html/2510.16844v1#S2.SS4)) for professional chart generation.

#### Report Generation

The Report Generation Agent handles drafting, optimization, and post-processing using the Two-Stage Writing Framework (Section [2.5](https://arxiv.org/html/2510.16844v1#S2.SS5)). The process includes: (1) Drafting: retrieving relevant CoA segments and structured data according to predefined outlines; (2) Self-reflective Optimization: iteratively refining text for factual accuracy and consistency; and (3) Post-processing: parsing identifiers, loading visualizations, formatting citations, and rendering into a publication-ready format.

### 2.3 Code Agent with Variable Memory (CAVM)

Figure: Figure 2: The design philosophy of CAVM architecture.
Refer to caption: x2.png

#### Motivation

In previous research, agents are often equipped with a role-specific toolkit, which limits flexible interaction between agents and the environment. Meanwhile, how to organize the intermediate data and memory of multi-agent system wisely is also underexplored. These factors significantly affect the overall performance of multi-agent systems when collaborating and handling complex tasks. Therefore, the core design philosophy of Code Agent with Variable Memory (CAVM) architecture is to empower each agent with autonomy, enabling flexible action space and seamless sharing of contextual memory across agents.

#### Unified Variable Space

The collaboration of multi-agent system involves diverse elements, which we abstract into three variable types, shown in Figure [2](https://arxiv.org/html/2510.16844v1#S2.F2): data ($\mathcal{V}_{data}$), including both structured and unstructured data variables; tools ($\mathcal{V}_{tool}$) with different functions; and agents ($\mathcal{V}_{agent}$) we designed with distinct role specification. In CAVM architecture, we represent all these variables in a unified variable space $\mathcal{V}$,

$$ $\mathcal{V}=\mathcal{V}_{data}\cup\mathcal{V}_{tool}\cup\mathcal{V}_{agent}.$ $$

During multi-agent collaboration, the variable space is dynamically maintained and updated to support unified and efficient context management.

#### Foundation Agent with Code Action

The agent operates in an iterative cycle. At each step, it first generates a reasoning trace and then produces executable code. The feedback from the code’s execution then informs the planning for the subsequent step. This code-centric design leverages the language model’s inherent coding abilities to unify diverse operations into a single, flexible, and scalable programming paradigm. Formally, at a given step $t$, the generation process can be decomposed as:

$$ $P_{\theta}(\mathcal{R}_{t},\mathcal{C}_{t}\mid q,\mathcal{V}_{t-1},\mathcal{H}_{t-1})=\underbrace{P_{\theta}(\mathcal{R}_{t}\mid\Phi(\mathcal{V}_{t-1}),\cdot)}_{\text{Reasoning Process}}\cdot\underbrace{P_{\theta}(C_{t}\mid\mathcal{R}_{t},\Phi(\mathcal{V}_{t-1},C_{t-1}),\cdot)}_{\text{Code Action Generation}},$ $$

where $\mathcal{R}_{t}$, $\mathcal{C}_{t}$ and $\mathcal{H}_{t}$ respectively represent the generated reasoning chain, code snippet and interaction history of step $t$. $\Phi$ represents the format function that converts variable environment information into readable strings, with the following definition:

$$ $\Phi(\mathcal{V}_{t})=\begin{cases}\text{Info}(\mathcal{V}_{0})&\text{if }t=0,\\ \Phi(\mathcal{V}_{t-1})\oplus\text{Info}(\mathcal{V}_{t}\setminus\mathcal{V}_{t-1})&\text{if }t>0.\end{cases}$ (1) $$

Then, $\mathcal{C}_{t}$will be sent to the code interpreter for execution, and $V_{t-1}$ will be modified and the result will be added to $H_{t-1}$:

$$ $\displaystyle\mathcal{V}_{t},\text{output}_{t}$ $\displaystyle=\text{Execute}(\mathcal{C}_{t},\mathcal{V}_{t-1}),$ (2) $\displaystyle\mathcal{H}_{t}$ $\displaystyle=\mathcal{H}_{t-1}\oplus\text{output}_{t}.$ (3) $$

### 2.4 Iterative Vision-Enhanced Mechanism for Visualization

Motivation
Generating high-quality visualizations is a persistent challenge in automated report generation, particularly in data-intensive domains like finance that require nuanced analysis and presentation. Existing methods often rely on single-pass code execution or employ Vision-Language Models (VLMs) without incorporating visual feedback, which frequently leads to suboptimal outcomes. Drawing inspiration from Chain-of-Thought (Wei et al., 2022) and Actor-Critic (Schulman et al., 2017), we propose a framework where an agent learns to progressively improve visualizations. This is achieved by iteratively plotting a chart and refining it based on critical feedback, ensuring both stable generation and continuous quality enhancement.

Iterative Vision-Enhanced Mechanism Specifically, the final output of the Data Analysis Agent includes the target chart specifications along with the corresponding descriptions and data. For each chart, the agent generates an initial visualization through executable plotting code, which is then evaluated by a VLM to give potential issues of visual cues (e.g. missing labels, inappropriate color schemes). These feedbacks are sent to the system, directing the iterative code generation until the output reaches professional quality.

$$ $P(\mathcal{C}_{vis}\mid\mathcal{V})=\prod_{t=1}^{M}P_{\theta}(\mathcal{C}_{t}^{vis}\mid\mathcal{C}_{t-1}^{vis},\mathcal{F}_{t-1},\mathcal{V}),\quad\mathcal{F}_{t-1}=\text{VLM}(\text{Execute}(\mathcal{C}_{t-1}^{vis})),$ $$

where $M$ is the maximum number of iterations. The iteration continues until convergence or a predefined quality threshold is satisfied.

### 2.5 Two-Stage Writing with Generative Retrieval

Figure: Figure 3: Chain-of-Analysis Illustration.
Refer to caption: x3.png

Motivation A complete report encompasses analyses from multiple perspectives, which can be regarded as an integration of several Chains-of-Analysis. To generate long-form financial research reports with both textual depth and multimodal coherence, we design a two-stage writing framework augmented with generative retrieval. It decomposes the report writing process into (1) Chain-of-Analysis Generation and (2) Structured Writing with Generative Retrieval.

Stage 1: Chain-of-Analysis Generation
Given the research question $q$, the Data Analysis Agent first generates a set of analytical perspectives $\mathcal{P}=\{p_{1},p_{2},...,p_{K}\}$. The agent then performs parallel data analysis for each $p_{i}$, producing corresponding Chain-of-Analysis (CoA) that capture insights from distinct viewpoints.

Each CoA is generated based on the interaction history $\mathcal{H}_{i}$, accumulated during the data analysis process. To ensure coherence between textual content and referenced elements (e.g. figure, reference), this process is augmented with a generative retrieval mechanism that jointly produces textual contents along with element identifiers. These identifiers specify chart and reference attributes using natural language descriptions, enabling unified autoregressive generation.
The process can be formalized as:

$$ $P(\mathcal{A}\mid q,\mathcal{V})=P(\mathcal{P}\mid q,\mathcal{V})\cdot\prod_{i=1}^{\mid\mathcal{P}\mid}P(a_{i}\mid p_{i},\mathcal{V}).$ $$

Stage 2: Structured Writing Building on CoAs, a Report Generation Agent first constructs a report outline $\mathcal{O}=\{o_{1},o_{2},...,o_{n}\}$, and then writes each section sequentially. For each section $s_{i}$, the agent dynamically retrieves the most relevant data and CoA segments from the unified variable memory $\mathcal{V}$, formalized as:

$$ $P(R\mid\mathcal{A},\mathcal{V},q)=P(\mathcal{O}\mid\mathcal{A},q)\cdot\prod_{i=1}^{n}P(A_{\text{selected}}^{(i)},\mathcal{V_{\text{selected}}^{(i)}}\mid\mathcal{A},\mathcal{V},\cdot)\cdot P(s_{i}|s_{<i},A_{\text{selected}}^{(i)},\mathcal{V_{\text{selected}}^{(i)}},\cdot).$ $$

To prevent hallucination of non-existent references and figures, agent is instructed to follow the identifiers established in $\mathcal{A}$.
To ensure reference accuracy, the agent strictly follows the identifiers established during the stage 1.

## 3 Experiments

### 3.1 Dataset and Evaluation Metrics

Financial research report generation remains an under-explored problem lacking appropriate evaluation benchmarks and metrics. To address this gap, we construct a high-quality benchmark specifically designed for financial research report generation, comprising a dataset of research targets with corresponding professional institutional reports and a comprehensive set of automated evaluation metrics. Details can be found in Appendix [E](https://arxiv.org/html/2510.16844v1#A5).

#### Dataset.

Our dataset encompasses research targets at both company and industry levels. For company-level analysis, we curated a diverse list of companies from authoritative financial platforms, covering different markets, industry sectors, and market capitalizations. For industry-level analysis, we selected high-attention industries from these platforms as research targets. For all targets, we collected in-depth analysis reports authored by professional brokerage institutions as golden reference reports to facilitate evaluation of data accuracy and analytical quality.

To ensure the quality of golden reference reports, we applied stringent filtering criteria, selecting only reports exceeding 20 pages in length and containing more than 20 charts and visualizations. Following established practices in report generation research (Wang et al., 2024b; 2025; Li et al., 2025b), and considering the substantial time and computational costs associated with report generation and evaluation, we collected 20 samples: 10 company-level and 10 industry-level targets.

#### Evaluation Metrics.

We design 9 automated evaluation metrics across three critical dimensions, each ranging from 0 to 10 points. Detailed description of each metric can be found in Appendix [E](https://arxiv.org/html/2510.16844v1#A5).

(1) Factual Accuracy: Measures the reliability and correctness of generated content through Core Conclusion Consistency (alignment with reference conclusions), Textual Faithfulness (proper citation support), and Text-Image Coherence (consistency between textual and visual elements).

(2) Information Effectiveness: Evaluates the analytical value delivered to investors via Information Richness (distinct information points), Coverage (proportion of key reference information captured), and Analytical Insight (critical analysis and forward-looking recommendations).

(3) Presentation Quality: Assesses professional standards through Structural Logic (organizational coherence), Language Professionalism (adherence to financial terminology), and Chart Expressiveness (effective visualization utilization and aesthetic quality).

### 3.2 Baselines

We compare FinSight against multiple categories of baselines:

LLMs with Search Tools: We evaluate leading large language models directly combined with search tools for report generation, including OpenAI GPT-5 (OpenAI, 2025b), DeepSeek-R1 (DeepSeek-AI et al., 2025), and Claude-4.1-Sonnet (Google, 2023).

Deep Research Agents: We compare against state-of-the-art commercial deep research products, including Gemini-2.5-Pro Deep Research (Gemini, 2025), Grok Deep Search (Grok, 2025), OpenAI Deep Research (OpenAI, 2025a), and Perplexity Deep Research(^1^11[https://www.perplexity.ai/?model_id=deep_research](https://www.perplexity.ai/?model_id=deep_research)). Details of baseline implementations can be found in Appendix [B](https://arxiv.org/html/2510.16844v1#A2).

### 3.3 Implementation Details

Our backbone model uses DeepSeek-V3, and during the writing phase, we employ DeepSeek-R1 with reasoning capabilities. The maximum input length is set to 81,920, and the maximum output length is set to 16,384. For search, we use the Google Search API, setting the region to China and retrieving the top 10 search results. For evaluation, we use the multimodal model Gemini-2.5-Pro as our evaluation model. Details can be found in Appendix [C](https://arxiv.org/html/2510.16844v1#A3).

### 3.4 Main Results

**Table 1: Overall evaluation results on financial report generation benchmark. Bold denotes the highest score in each column, Underlined denotes the second highest.**
| Model | Factual | Analytical | Presentation | Avg. |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Cons. | Faith. | T-I. | Rich. | Cover. | Ins. | Logic | Lang. | Vis. |  |  |
| *LLM with Search Tools* |  |  |  |  |  |  |  |  |  |  |
| GPT-5 w/ Search | 5.60 | 6.45 | 3.45 | 5.95 | 4.30 | 5.60 | 6.80 | 5.30 | 2.95 | 5.16 |
| Claude-4.1-Sonnet w/ Search | 4.75 | 5.15 | 2.70 | 5.45 | 4.60 | 4.70 | 6.15 | 5.50 | 2.20 | 4.58 |
| DeepSeek-R1 w/ Search | 6.05 | 4.95 | 2.75 | 7.25 | 6.45 | 7.05 | 7.20 | 6.85 | 2.10 | 5.63 |
| *Deep Research Agent* |  |  |  |  |  |  |  |  |  |  |
| Grok Deep Search | 4.05 | 5.70 | 3.80 | 5.10 | 3.65 | 4.55 | 5.75 | 4.80 | 4.10 | 4.61 |
| Perplexity Deep Research | 4.10 | 5.60 | 4.25 | 4.00 | 2.70 | 3.60 | 5.30 | 3.90 | 3.95 | 4.16 |
| OpenAI Deep Research | 5.60 | 7.45 | 4.90 | 6.35 | 6.40 | 5.90 | 6.90 | 6.85 | 4.65 | 6.11 |
| Gemini-2.5-Pro Deep Research | 7.10 | 6.80 | 4.65 | 7.45 | 7.75 | 7.85 | 7.65 | 7.85 | 4.25 | 6.82 |
| FinSight (ours) | 6.85 | 7.50 | 7.85 | 8.70 | 8.30 | 8.45 | 8.05 | 8.10 | 9.00 | 8.09 |

Table [1](https://arxiv.org/html/2510.16844v1#S3.T1) presents the performance of FinSight against two categories of baselines on the financial research report generation task.
Overall, FinSight achieves the highest overall score (8.09), significantly outperforming all baselines, including closed-source commercial agents like Gemini Deep Research (6.82) and OpenAI Deep Research (6.11).
This result validates the effectiveness of our proposed multi-agent framework for crafting in-depth financial research reports.
In terms of factuality, FinSight obtains the best scores in both the faithfulness of text citations and text-image consistency, demonstrating the efficacy of the identifier mechanism designed within our Chain-of-Analysis process.

A noteworthy observation is that the consistency score of our model (6.85) is slightly lower than that of Gemini Deep Research (7.10).
Case studies reveal that our method prioritizes comprehensive data acquisition to deliver deeper insights.
This approach leads it to uncover more data-driven findings, rather than generating simplified conclusions from conventional search-based methods.

**Table 2: Ablation studies of our key design.**
| Method | Fact. | Ana. | Pres. |
| --- | --- | --- | --- |
| FinSight | 7.0 | 7.9 | 8.0 |
| w/o Iter.. | 6.9 | 7.2 | 7.5 |
| w/o 2-Stage. | 6.4 | 5.9 | 6.3 |
| w/o Dyn. | 5.9 | 5.7 | 6.4 |

The superiority of our method is further reflected in the analytical quality of the reports.
FinSight scores the highest in information richness, coverage of key information from professional reports, and insightfulness.
Regarding presentation quality, our system demonstrates a comprehensive lead in logic, language, and visualization.
It particularly excels in visualization (9.00), far surpassing other methods and showcasing the advanced multimodal presentation capabilities of our system.

## 4 Ablation Studies

We conduct ablation studies to evaluate the contribution of our key components, with results summarized in Table [2](https://arxiv.org/html/2510.16844v1#S3.T2). Key findings are as follows: (1) Removing iterative VLM feedback for chart generation causes a significant decline in both Presentation Quality (from 8.0 to 7.5) and Analytical Quality (from 7.9 to 7.2). This is primarily because the writing process relies on analyzing the generated images, lower-quality visuals impede the ability to perform insightful analysis based on the charts. (2) Merging analysis and writing into a single process leads to a significant drop in analytical quality (from 7.9 to 5.9) and factual accuracy (from 7.0 to 6.4), demonstrating the effectiveness of our proposed two-stage, analyze-then-write strategy. (3) Eliminating dynamic search during the analysis and writing phases results in a significant performance drop across all dimensions, including Factual Accuracy (from 7.0 to 5.9) and Analytical Depth (from 7.9 to 5.7). This highlights the necessity of acquiring additional knowledge during these stages to ensure comprehensive and factually correct reports.

### 4.1 Analysis

#### Statistical Analysis of Generation Process.

**Table 3: Statistics of our generation process. We analyze metrics at both the CoA level and the final report level.**
| Metric | Avg. Value |
| --- | --- |
| Chain of Analysis (CoA) |  |
| # Tokens | 2,761 |
| # Images | 5.3 |
| Final Report |  |
| # Fin. API Calls | 18.3 |
| # Search Queries | 983.2 |
| # Browse Pages | 469.8 |
| # CoA Segments | 17.6 |
| # Tokens | 62,586 |
| # Images | 51.2 |

Table [3](https://arxiv.org/html/2510.16844v1#S4.T3) summarizes report statistics. Key findings: (1) Each CoA is a self-contained multimodal block, averaging 2,761 tokens and 5.3 images. (2) A report synthesizes about 17.6 CoAs, yielding 62,586 tokens and 51.2 images. (3) Incorporating deep search introduces richer knowledge, with 983.2 searches and 469.8 browsed pages per report.

#### Analysis of Image Generation.

As illustrated in Figure [5](https://arxiv.org/html/2510.16844v1#S4.F5), our Iterative Vision-Enhanced Mechanism progressively refines a stock chart over three iterations. In contrast to the initial, simplistic plot with low information density, the final visualization resolves this issue by integrating price and volume on a dual-axis, enriched with analytical overlays and contextual event markers, thereby presenting multifaceted data within a single view. This process is driven by critical VLM feedback across iterations, which targets improvements in aesthetics, information density, and other aspects.This suggests our mechanism is crucial for bridging the gap between automated chart generation and expert-quality financial visualizations.

#### Analysis of Report Length and Quality

Figure: Figure 4: Correlation between report length and quality score across different methods.
Refer to caption: x4.png

To further investigate the characteristics of the generated reports, we analyze the relationship between report length and overall quality score, as illustrated in Figure [4](https://arxiv.org/html/2510.16844v1#S4.F4). The plot shows that the outputs from our method are concentrated in the top-right quadrant, which indicates that our generated reports are not only comprehensive and of substantial length (typically over 20,000 words) but also of superior quality. We attribute this strong and consistent performance to our proposed two-stage writing framework. By first generating a concise Chain-of-Analysis, the model can then compose the final report based on richer, well-structured information, ensuring both analytical depth and coherence.

In contrast, baseline methods exhibit significant limitations. Simpler approaches like LLM with search tool, which often rely on single-pass generation, are typically constrained to shorter reports. Meanwhile, other deep research agents such as OpenAI DR and Perplexity DR display a wide scatter of data points across the plot, which signifies a critical lack of consistency. For these methods, a greater length does not reliably translate into higher quality, highlighting the effectiveness of our structured, two-stage approach.

Figure: Figure 5: An example of our Iterative Vision-Enhanced Mechanism of Visualization. The chart is generated by matplotlib and seaborn package in Python.
Refer to caption: x5.png

## 5 Related Work

### 5.1 Deep Research Systems

Deep research systems represent a paradigm shift from traditional information retrieval to comprehensive knowledge synthesis, characterized by their ability to conduct multi-round information searching and integration. Current open-source deep research frameworks have emerged along several technical trajectories.
ReAct-based agents (Yao et al., 2022), such as Open Deep Research (OpenAI, 2025a) and WebThinker (Li et al., 2025b), employ observation-thought-action loops with reasoning capabilities for iterative problem planning and execution. Multi-agent systems, including OWL (Hu et al., 2025) and Auto Deep Research (Tang et al., 2025), focus on collaborative problem-solving through agent specialization and coordination. Additionally, commercial systems represented by OpenAI Deep Research (OpenAI, 2025a) and Grok Deep Research (Grok, 2025) have demonstrated promising performance.
However, existing frameworks exhibit significant limitations in multimodal processing (Yang et al., 2025) and domain-specific applications (Jimeno-Yepes et al., 2024; Tian et al., 2025). Due to the text-centric design of report generation workflows and the base models’ lack of native image generation capabilities (Ren et al., 2021; Chen et al., 2024), current systems produce reports deficient in visual elements such as charts and diagrams.
Furthermore, these systems demonstrate inadequate adaptation to financial domains, particularly in their inability to support for professional-grade chart generation, limited real-time market data integration, creating substantial gaps between system outputs and professional requirements.

### 5.2 LLM Agents in Financial Domain

Recent advances in Large Language Models have led to the development of various financial AI systems, each targeting specific aspects of financial analysis. Many of these works focus on stock price prediction and modeling (Zhang et al., 2025; Xiao et al., 2025) using multi-agent architectures. From a report generation perspective, FinTeam (Wu et al., 2025) can provide analysis from multiple viewpoints including company and industry levels. However, due to its single-round generation process, the resulting analysis lacks depth and comprehensiveness. Similarly, FinRobot (Yang et al., 2024) directly inputs collected information to models for single-round investment recommendation generation.
Additionally, several open-source works (Zhang et al., 2025; Tian et al., 2025) provide comprehensive tools and data interfaces, yet they lack well-designed frameworks for report generation.
Overall, existing systems exhibit critical limitations for comprehensive financial research report generation, particularly regarding report depth, data breadth, and multimodal integration.

## 6 Conclusion

In this paper, we introduce FinSight, a multi-agent system designed to generate high-quality, multimodal financial research reports. At its core, FinSight leverages the Code Agent with Variable Memory architecture, unifying data, tools, and agents into a single programmable space for dynamic, code-driven analysis. We also propose an Iterative Vision-Enhanced Mechanism to refine visualizations to professional standards and a Two-stage Writing Framework that expands concise analysis chains into comprehensive, coherent reports. Our experiments demonstrate that FinSight significantly outperforms existing baselines across a range of financial research tasks. This work validates the effectiveness of our multi-agent, code-centric approach and highlights its potential to revolutionize the field of automated financial analysis.

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## Appendix

## Appendix A Statement on the Use of Large Language Models (LLMs)

During the preparation of this manuscript, we use Large Language Models (LLMs) as a general-purpose assistance tool. The primary role of the LLM is to aid in improving the clarity and readability of the text, as well as to accelerate the implementation of our research ideas. Specific applications include: (1) Language and Grammar Correction: Polishing sentence structure, correcting grammatical erros, and refining word choices to enhance the overall quality of the writing. (2) Paraphrasing and Style Refinement: Rephrasing sentences and paragraphs to ensure consistency in tone and style throughout the paper. (3) Code Implementation Assistance: Generating code snippets and providing debugging support to help implement the proposed algorithms and experimental setups.

It should be noted that all core research concepts, experimental design, data analysis, and conclusions are developed exclusively by the human authors. Any content or suggestions generated by the LLM, including code, are critically checked, and substantially edited by the authors to ensure accuracy. The authors take full responsibility for the final content of this paper.

## Appendix B Baselines Details

We mainly compare our method with the following two types of baselines:

#### (1) LLMs with Search Tools.

- •
OpenAI GPT-5 w/Search: The latest OpenAI’s GPT model with web search API for research question.
- •
Claude-4.1-Sonnet w/Search The latest Anthropic’s reasoning LLM with web search API for research question.
- •
DeepSeek-R1 w/ Search: The DeepSeek’s LLM integrated with web search API for research question.

#### (2) Deep Research Agents.

- •
Grok Deep Search: The xAI’s Deep Search applications, powered by the latest Grok model.
- •
Perplexity Deep Research: A commercial AI research assistant integrating multi-step search and analysis, optimized for rapid information aggregation.
- •
OpenAI Deep Research: A multi-step web research agent built on ChatGPT that searches, analyzes, and synthesizes information from multiple sources to produce research-grade reports with citations.
- •
Gemini-2.5-Pro Deep Research: Google’s advanced research agent featuring multi-turn planning, deep web navigation, and multi-source evidence integration.

We evaluate these baselines directly on their official web applications. For consistency across different systems, we use the following unified prompt template to get the report:

## Appendix C Implementation Details of FinSight

#### Backbone

For Multi-source Data Collection Agent, Deep Search Agent and Data Analysis Agent, we use the DeepSeek-V3 as the backbone model. For Report Generation Agent, we use DeepSeek-R1 as the backbone model. The maximum input length is 81,920 tokens, and the maximum output length is 16,384 tokens.

#### Data Collection

We implement the financial api tool based on akshare (^2^22https://github.com/akfamily/akshare) package in Python. For web search, we use the Google Search API, with the region set to China and the number of retrieved results fixed at the top 10. For web content acquisition, we employ Playwright (^3^33https://playwright.dev/) to simulate a browser for webpage content extraction.

#### Retrieval

We use Qwen3-Embedding-0.6B to generate embeddings for data and CoA segments. Then we use the cosine similarity to select the relevant data and CoA segments for each section.

#### Iterative Vision-Enhanced Mechanism

We use the Qwen2.5-VL-72B as the critic vision-language model in the chart generation stage. To balance effectiveness and cost, we perform three iterations of the critic process.

#### Ablation Study

We conduct ablation study on $5$ company questions, which includes: Cambricon Technologies, Li Auto-W, Pop Mart, 3SBio, China Mobile. Some variants are as follows:

- •
w/o Iteration Vision-Enhanced Mechanism We remove the iterative refinement process and plot charts in a single pass.
- •
w/o Two-Stage Writing Framework We only concatenate the CoA segments to output the final report.
- •
w/o Dynamic Search-Enhanced Strategy We remove the Dynamic Search-Enhanced Strategy from the Data Collection and Report Generation process.

## Appendix D Construction of the Financial Report Generation Benchmark

#### Questions

We select the most popular five A-share companies, five Hong Kong-stock companies, and ten representative industries from [https://www.djyanbao.com](https://www.djyanbao.com) as the benchmark research questions. These companies and industries cover a diverse set of market sectors and provide a comprehensive foundation for evaluating the effectiveness of deep research systems.

#### Golden Referenced Report

To establish human expert-level benchmark, we collect the latest equity and industry research reports from well-known Chinese securities firms, as shown in Table [4](https://arxiv.org/html/2510.16844v1#A4.T4). These golden references cover both company-level and industry-level analyses across A-shares, Hong Kong stocks, and major industries.

**Table 4: Golden Referenced Reports from Chinese Securities Firms**
| Market | Company / Industry | Securities Firm |
| --- | --- | --- |
| A-shares | SMIC (688981) | Soochow Securities |
|  | Cambricon Technologies (688256) | Donghai Securities |
|  | China Mobile (600941) | Zhongtai Securities |
|  | Skshu Paint (603737) | Huatai Securities |
|  | Yiwu China Commodities City (600415) | Guolian Minsheng Securities |
| Hong Kong Stocks | Pop Mart (09992) | Zhongtai Securities |
|  | SenseTime (00020) | Zhongtai Securities |
|  | Li Auto-W (02015) | Huayuan Securities |
|  | 3SBio (01530) | Huatai Securities |
|  | UBTECH Robotics (09880) | Guohai Securities |
| Industries | Semiconductor Industry | Kaiyuan Securities |
|  | Food & Beverage Industry | Huachuang Securities |
|  | Basic Chemical Industry | Zhongtai Securities |
|  | Steel Industry | Orient Securities |
|  | Construction & Decoration Industry | Guosheng Securities |
|  | Environmental Protection & Public Utilities (Controlled Nuclear Fusion) | Huachuang Securities |
|  | Light Manufacturing (Durable Consumer Goods) | Guotai Haitong Securities |
|  | K12 Education Industry | Guosheng Securities |
|  | Media Industry (Short Drama Overseas Expansion) | Soochow Securities |
|  | Transportation (Cross-border E-commerce Logistics) | Maigao Securities |

## Appendix E Evaluation and Metrics

We further illustrate the metrics we used for evaluation:

#### (1) Factual Metrics

Measure the textual quality and factual accuracy of the final report.

- •
Core Conclusion Consistency: Whether the core conclusions in the generated report are consistent with those in the reference report.
- •
Textual Faithfulness: Whether the arguments in the report are properly supported by citations from the reference.
- •
Text-Image Coherence: Whether the report integrates images into the discussion, and whether the textual and visual descriptions align.

#### (2) Analysis Effectiveness

Measure whether the financial report provides sufficient information and insights for investors.

- •
Information Richness: The number of distinct information points included in the report.
- •
Coverage: The extent to which key information from the golden reference report is covered.
- •
Analytical Insight: Whether the report provides critical analysis, original insights, and forward-looking recommendations.

#### (3) Presentation Quality

Measure the presentation quality of the final report.

- •
Structural Logic: The logical organization of each section and the overall structural soundness of the report.
- •
Language Professionalism: Whether the language conforms to financial terminology, using the golden report as a reference.
- •
Chart Expressiveness: The effectiveness of charts in supporting the narrative, including their informativeness and aesthetic quality.

#### Evaluation Process

We adopt Gemini-2.5-Pro as the backbone evaluation model. To ensure fair comparison across reports, we employ a list-wise evaluation strategy, where the model is provided with all candidate reports along with the golden reference report and assigns scores accordingly.
The nine metrics mentioned above can be divided into two parts, one is unrelated to the golden report and the other is related to the golden report. For these two types, we have designed two types of prompts, which are listed below.

## Appendix F A Case of Company Research Question

To demonstrate the practical application of our system, this section shows the case of SenseTime Technology (0020.HK), a leading artificial intelligence company in China.

We present the collecting tasks of the Data Collection process in Table LABEL:tab:data_collection, and an analytical tasks of the Data Analysis process in Table LABEL:tab:data_analysis.

**Table 5: The predefined and brainstormed data collection tasks.**
| Data Collection |
| --- |

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 4
presentation: 2
questions: 1. Please clarify how “human-expert quality” was defined and whether any human evaluation was performed.
2. Do you plan to release code or data to support reproducibility?
3. How is the “financial research report” defined—does it target professional investment research or general analytical summaries?
soundness: 3
strengths: The topic is timely and practically relevant, addressing the automation of multimodal financial analysis—a domain rarely explored in LLM research. The proposed FinSight system integrates multiple functional agents (retrieval, visualization, and report writing) into a coherent pipeline. The idea of separating analytical reasoning from structured writing via the Two-Stage Writing Framework is conceptually sound and could be useful for other applied domains. The paper provides qualitative examples demonstrating real-world potential and shows moderate engineering effort to bridge LLM reasoning with data-driven financial reporting.
summary: The paper presents FinSight, a multi-agent system for automatically generating multimodal financial research reports that integrate text, visualizations, and citations. The system addresses three challenges—domain knowledge, multimodal generation, and analytical depth—by introducing a Code Agent with Variable Memory (CAVM) for tool-based data processing, and a Two-Stage Writing Framework that transforms chains of analysis into structured reports. An Iterative Vision-Enhanced Mechanism refines visual outputs into professional-looking charts. Experiments on company- and industry-level tasks show FinSight outperforming commercial deep research systems in factual accuracy, analytical depth, and presentation quality based on automatic and LLM-based evaluations. However, no human evaluation or code release is provided. While the work tackles a novel and practically interesting problem, the paper’s structure and methodological clarity are limited, and the evaluation lacks rigor.
weaknesses: 1. The problem formulation lacks rigor. The definition of the financial report R as a set ignores its hierarchical and sequential structure, and the mapping from query q to report R is not well formalized.
2. The paper’s overall organization is unclear, with descriptions of system components, data collection, and evaluation interleaved, making it difficult to follow the main contributions.
3. No code, data, or benchmark release, which limits reproducibility. Given the complexity of the multi-agent design, partial release or detailed pseudo-code would be essential for validation.
4. Evaluation relies entirely on automatic or LLM-based metrics, with no human expert assessment. The claim that FinSight “approaches human-expert quality” is not substantiated.

---

confidence: 5
contribution: 2
initial_rating: 2
presentation: 2
questions: 1. What is the agreement between gemini and human labels? What is the sensitivity to prompt variants and stochastic generations? 20 samples is a small set, what are the confidence intervals and variability in measurements under repeated analysis?
2. Beyond LLM scoring, did you evaluation precision/recall on citations or fact checking? How did you evaluate that claims are grounded in sources? How are you auditing the factual and quantitative claims in the reports? Consider adding a human review study of the reports for factuality and hallucination detection. 
3. Baselines: How did you ensure each baseline had the same regional access to data?
4. Can you comment on plans for out of domain generalization experiments? Have you considered public filings from other jurisdictions (US annual reports, 10Ks, EU annual reports). These are mandated to be public, which helps reproducibility.
soundness: 1
strengths: 1. System design is coherent and includes formulations for the CAVM updates. There is a clear decomposition and separation of modalities with a programmable memory abstraction. 
2. Integration of modalities is well defined and motivated, and examples in appendix look good. 
3. Iteration on stock charts highlights the core contrition of the VLLM critic. This is well-motivated, and the iterative model, factor analysis is well specified. 
4. Generally readable minus 1 typo. 
5. Ablations cover each component and experiments cover wide variety of commercial research platforms. 
6. Transparency on toolchain, judge prompts is very helpful for reproducibility.
summary: This paper presents FinSight multi-agent system for generating long-form, multimodal financial research reports. Two main contributions are a Code Agent with Variable memory framework that unifies data, tools, and agents together with code execution, and a two stage writing framework with Chains of Analysis to produce structured reports. A vision-enhanced loop is also included to critique and refine code-generated charts (stock price charts, etc). The paper provides several examples in the appendix. 

The paper outlines the main problem as producing a unified text, visual, and citation-grounded report with access to real-time data (via tools) and grounded content generation (VLM critic, graphs, visual generation). The system is evaluated on a small 20 target benchmark and evaluated on 9 metrics by an LLM-as-Judge (Gemini-2.5-pro). The framework is compared against several commercial dee research platforms (OpenIAI, Grok, Deepseek).
weaknesses: 1. LLM-as-judge without calibration or human study. Scores are produced by Gemini-2.5-Pro using list-wise prompts, but the paper does not report inter-rater reliability, judge sensitivity to prompt phrasing, or correlation with human experts. No robustness analysis (e.g., seed variation, alternative judges, bootstrap CIs) is provided. The bias in gemini is evident from the experiments (gemini is often 2nd best). 
2. Small, in-distribution benchmark. There are only 20 examples, and the formats/layouts/color schemes are similar. There is no OOD test on alternative firm reports or other market/regulators (EU reports, EDGAR) - generalizability is unclear. 
3. The decision to restrict google search api to a single region -> is this applied to all models? How are the authors mitigating regional bias from one provider vs another. This should be made clearer.
4. Metric definitions are all subjective, given the small evaluation set the anticipated variability on repeated judgements is not clear. what is the CI on these numbers? Furthermore, evaluation is qualitative - this paper would benefit from some quantitative metrics (difference in pixels from real data vs generated chart, etc.)

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confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: 1. How is code execution integrated into the FinSight workflow? From the overall framework, it seems that the writing stage mainly involves text generation rather than executable code.
2. Could the authors clarify whether the proposed *variable memory* primarily serves as a conceptual abstraction for flexible variable access, or if it introduces a genuinely new representation or learning mechanism in memory design?
soundness: 2
strengths: 1. The framework diagram of Finsight is very clear, and the overall workflow is well-structured and easy to follow.
2. The proposed two-stage writing framework substantially enhances the overall quality of report generation. Also, the iterative vision-enhanced mechanism effectively integrates textual reasoning with visual elements to meet the needs of multimodal financial analysis.
summary: FinSight introduces a multi-agent system for automated financial report generation. Its CAVM architecture and iterative vision mechanism enable accurate data synthesis and professional-grade charts, and the two-stage writing framework enhances coherence and depth. FinSight uses LLM as judge to evaluate the qualities of generated reports, comparing with deep research models. Experimental results demonstrate that FinSight achieves the highest scores across all evaluation dimensions, outperforming both deep search agents and LLMs equipped with search tools.
weaknesses: 1. The approach largely extends the deep research agent framework to the financial domain, with improvements mainly in the writing and generation stages. While effective, the overall innovation appears somewhat limited. Also, CAVM is overclaimed as a novel agent architecture. While FinSight includes limited code-execution components for data visualization and analysis, its overall workflow is primarily coordinated through multi-agent collaboration enhanced by code execution rather than code-driven reasoning.

2. In the report generation evaluation process, Finsight uses LLM as judge to give scores on 3 different aspects. However, for the factual accuracy dimension, aspects such as citation accuracy and faithfulness are typically measurable through formula- or rule-based metrics in RAG. Relying on an LLM to subjectively score these aspects may introduce bias or inconsistency.

3. In the experiments, FinSight was compared with LLMs with search tools and deep research agents. As shown in Figure 4, the reports generated by FinSight are significantly longer and get better scores. However, this correlation between report length and quality score may not be entirely desirable. It could suggest a potential bias in the LLM-based evaluation, which might favor longer outputs while paying less attention to their actual quality. A relatively fair comparison could be achieved by controlling the report length across methods.
4. The paper does not include the implementation code, which may limit the reproducibility of the proposed method.