QZhou-Flowchart-QA-Benchmark: Real-World Flowchart Understanding Benchmark

Overview

While the open-source community has various chart and document benchmarks, there is no specialized evaluation set for flowchart understanding. QZhou-Flowchart-QA-Benchmark fills this gap by providing a dedicated benchmark to effectively assess multimodal models' flowchart comprehension abilities.

Dataset Composition

Part 1: Web-Collected Real-World Flowcharts (Public)

Manually curated flowcharts from image search engines, covering actual deployment scenarios including:

• Government services and administrative processes
• Banking and financial operations
• Campus management systems
• Daily office workflows
• Financial processing procedures

Quality Diversity: We deliberately control the distribution of image resolution and clarity, introducing varying degrees of blur and size differences to better reflect real-world application environments.

Annotation: All questions and answers are carefully labeled and verified by human annotators.

Part 2: Enterprise Office Flowcharts (Coming Soon)

Real flowcharts from production office environments, including:

• HR management processes
• Financial reimbursement workflows
• Internal approval procedures

Note: This portion is currently undergoing anonymization and will be released in a future update.

Question Diversity

FlowchartBench ensures comprehensive query coverage, considering various questioning angles and possibilities:

• Upstream and downstream node queries
• Conditional branch reasoning
• Path analysis and node relationships
• Structural understanding
• Spatial reasoning with X/Y axes

Performance Leaderboard

State-of-the-art results on QZhou-Flowchart-QA-Benchmark:

Model	QZhou-Flowchart-QA-Benchmark (%)
QZhou-Flowchart-VL-32B (Ours)	87.83
Qwen3-VL-Plus-Thinking (235B)	86.09
Gemini-2.5-Pro	84.42
doubao-seed-1-6	83.83
GPT-5	79.29
GLM-4.5V	75.97
Qwen2.5-VL-32B	73.90

Comparison with Base Model

Model	MMMU	CMMU	MathVista	DocVQA	QZhou-Flowchart-QA-Benchmark
Qwen2.5-VL-32B	66.67	76.38	74.20	93.96	73.90
QZhou-Flowchart-VL-32B	67.78	76.46	76.50	93.87	87.83

Usage

from datasets import load_dataset

# Load benchmark
benchmark = load_dataset("Kingsoft-LLM/QZhou-Flowchart-QA-Benchmark", split="test")

# Evaluate your model
for sample in benchmark:
    prediction = model.predict(sample['image'], sample['question'])
    accuracy = evaluate(prediction, sample['answer'])

Evaluation Protocol

• Answer Matching: Two evaluation methods based on question type:
  • Exact Match: For multiple-choice questions, direct comparison with ground truth
  • Normalized Edit Distance: For open-ended questions, score calculated as 1 - (edit_distance / max_length)
• Metrics: Overall accuracy, breakdown by question type, domain, and complexity level
• Submission: Open a GitHub issue with your model predictions to be added to the leaderboard

Key Features

✅ Real-world scenarios - Flowcharts from actual deployments
✅ Manual annotation - Human-verified questions and answers
✅ Quality diversity - Various resolutions, clarity levels, and sizes
✅ Comprehensive coverage - 20+ question types across multiple domains
✅ Rigorous evaluation - Standardized protocol for fair comparison