---
license: cc-by-sa-4.0
task_categories:
- table-question-answering
language:
- en
tags:
- finance
- 10k
- edgar
- agent
- benchmark
size_categories:
- 1M<n<10M
configs:
- config_name: column_documentation
  data_files:
  - split: train
    path: data/column_documentation/column_documentation.parquet
- config_name: company_addresses
  data_files:
  - split: train
    path: data/company_addresses/company_addresses.parquet
- config_name: column_comments
  data_files:
  - split: train
    path: data/column_comments/column_comments.parquet
- config_name: sqlite_sequence
  data_files:
  - split: train
    path: data/sqlite_sequence/sqlite_sequence.parquet
- config_name: table_documentation
  data_files:
  - split: train
    path: data/table_documentation/table_documentation.parquet
- config_name: companies
  data_files:
  - split: train
    path: data/companies/companies.parquet
- config_name: filings
  data_files:
  - split: train
    path: data/filings/filings.parquet
- config_name: financial_facts
  data_files:
  - split: train
    path: data/financial_facts/financial_facts.parquet
- config_name: company_tickers
  data_files:
  - split: train
    path: data/company_tickers/company_tickers.parquet
- config_name: table_comments
  data_files:
  - split: train
    path: data/table_comments/table_comments.parquet
---

# DDRBench: Deep Data Research Benchmark

[**📊 Leaderboard & Demo**](https://huggingface.co/spaces/thinkwee/DDR_Bench) | [**📄 Paper (Arxiv)**](https://arxiv.org/abs/2602.02039)

## Overview
**DDRBench (Deep Data Research Benchmark)** is a comprehensive evaluation framework designed to assess the capabilities of Large Language Model (LLM) agents in performing complex, multi-turn data research and reasoning tasks. Unlike traditional Q&A benchmarks, DDRBench focuses on scenarios requiring deep interaction with structured databases, tool usage, and long-context reasoning.

This dataset repository specifically hosts the **10-K Financial Database**, a core component of the DDRBench suite. It contains structured financial data extracted from SEC 10-K filings, enabling agents to answer intricate financial questions that mimic real-world analyst workflows.

## Dataset Structure
The dataset is organized into multiple configurations (subsets), representing different tables from the underlying SQLite database:

*   **`financial_facts`**: The primary table containing over 5 million financial metrics (US-GAAP, IFRS) with values, units, and fiscal periods.
*   **`companies`**: Registry of companies with CIK, names, and SIC codes.
*   **`filings`**: Metadata for the SEC filings source documents.
*   **`company_addresses`** & **`company_tickers`**: Geographic and market identification data.
*   **`table_documentation`** & **`column_documentation`**: Meta-information describing the database schema to the agents.

## Usage

### Data Inspection

Load specific tables using the `datasets` library:

```python
from datasets import load_dataset

# Load the main financial facts table
financial_facts = load_dataset("thinkwee/DDRBench_10K", "financial_facts")

# Load company information
companies = load_dataset("thinkwee/DDRBench_10K", "companies")
```

For agent trajectories and evaluation logs, please refer to the [DDRBench Trajectory Dataset](https://huggingface.co/datasets/thinkwee/DDRBench_10K_trajectory).

### Run Deep Data Research

Please use the database file under ``/raw`` path and refer to https://github.com/thinkwee/DDR_Bench for running the agent.