VMARC-QA: A Knowledge-based Multi-agent Approach for Vietnamese VQA with Rationale Explanations

📖 Introduction

This is the official implementation of the paper "A knowledge-based multi-agent approach for Vietnamese VQA with rationale explanations".

Visual Question Answering with Natural Language Explanations (VQA-NLE) is a major challenge for AI, especially for the Vietnamese language due to a lack of specialized datasets and methods.

VMARC-QA (Vietnamese Multi-Agent Rationale-driven Consensus for Question Answering) is a framework designed to solve this problem. Our system uses a team of AI agents working in parallel to gather evidence, form a logical explanation, and ultimately derive a final answer.

The core features of VMARC-QA include:

• Multi-Agent Collaboration: Employs an ensemble of three distinct agents (Junior, Senior, Manager) that work in parallel to gather evidence using different tools and perspectives.
• Verifiable Reasoning: Implements an "evidence-to-rationale" process, ensuring that every explanation is grounded in the evidence collected by the agents, rather than being freely hallucinated.
• Reliable Consensus: Aggregates agent outputs through a dual-stream mechanism: weighted voting determines the best answer, while a semantic consistency check on the rationales ensures the final output is coherent and trustworthy.

On the ViVQA-X benchmark, VMARC-QA sets a new standard for answer accuracy while maintaining top-tier explanation quality:

• 🏆 : Achieves 64.8% answer accuracy, outperforming strong prior models like NLX-GPT by over 11 percentage points.
• ✍️ : Produces explanations with high semantic fidelity, confirmed by a highly competitive BERTScore of 76.0, nearly matching the specialized fine-tuned model NLX-GPT (76.3).

The overall architecture of VMARC-QA is shown in the figure below:

Figure 1: Overview of the VMARC-QA Framework. Three agents independently generate answer-rationale pairs, which are then aggregated by a dual-stream consensus mechanism to produce the final output.

Table of Contents

• Introduction
• Repository Structure
• Installation
• Data Preparation
• Usage
• Results
• Citation
• License

Repository Structure

VMARC-QA/
├── src/                 # Main framework source code
│   ├── agents/          # Logic for Junior, Senior, and Manager agents
│   ├── core/            # LangGraph multi-agent graph implementation
│   ├── models/          # Pydantic models for state management
│   ├── tools/           # VQA and knowledge retrieval tools
│   └── utils/           # Utility functions
│
├── api/                 # FastAPI server providing the VQA tool
│
├── ViVQA-X/             # Git submodule for the base ViVQA model and data
│
├── data/                # (To be created) Stores COCO images and ViVQA-X annotations
│
├── assets/              # Contains images and assets for the README
│
├── notebooks/           # Jupyter notebooks for data exploration or analysis
│
├── results/             # Directory to save experiment outputs
│
├── experiments/         # Contains experiment configurations and logs
│
├── scripts/             # Scripts for setup and running experiments
│
├── .env.example         # Example environment file
├── main.py              # Main entry point for the application
├── requirements.txt     # Python dependencies for the main environment
└── README.md            # This file

Installation

This project requires two separate Conda environments due to dependency conflicts between the main LangGraph framework and the legacy ViVQA-X model used for the VQA tool.

1. Prerequisites

• Conda: For managing isolated environments.
• Python 3.10+
• API Keys: Copy the .env.example file to .env and add your API keys (e.g., OPENAI_API_KEY).

2. Environment Setup

You can use the automated setup script:

bash scripts/setup.sh

This will create both environments and install all dependencies.

If you prefer manual setup, follow these steps:

a. Main Environment (vmarc-qa) This environment runs the core multi-agent framework.

conda create -n vmarc-qa python=3.10 -y
conda activate vmarc-qa
pip install -r requirements.txt

b. Tool Environment (vmarc-qa-tool) This environment runs a FastAPI server that provides the Aligned Candidate Generator tool, based on the original ViVQA-X LSTM-Generative model.

# From the project root
cd ViVQA-X
conda create -n vmarc-qa-tool python=3.10 -y
conda activate vmarc-qa-tool
pip install -r requirements.txt
pip install fastapi uvicorn[standard] python-multipart
cd ..

Data Preparation

VMARC-QA is evaluated on the ViVQA-X dataset, which uses images from MS COCO 2014.

1. Download COCO 2014 Images

Create a data directory and download the val2014 image set:

# Create the directory structure
mkdir -p data/COCO_Images data/ViVQA-X

# Download and unzip the Validation 2014 images (~6GB)
wget http://images.cocodataset.org/zips/val2014.zip -P data/
unzip data/val2014.zip -d data/COCO_Images/
rm data/val2014.zip

2. Set Up ViVQA-X Annotations

Copy the required annotation files from the submodule into your data directory:

# Copy the annotation file from the submodule
cp ViVQA-X/data/final/ViVQA-X_test.json data/ViVQA-X/

3. Final Directory Structure

Your data directory structure should look like this when you're done:

VMARC-QA/
├── data/
│   ├── COCO_Images/
│   │   └── val2014/
│   │       ├── COCO_val2014_000000000042.jpg
│   │       └── ...
│   └── ViVQA-X/
│       └── ViVQA-X_test.json

Usage

The VMARC-QA system consists of multiple components. Follow these steps to run a full experiment.

Step 1: Run the VQA Tool Server

Open a terminal, activate the vmarc-qa-tool environment, and start the API server from the ViVQA-X submodule directory.

conda activate vmarc-qa-tool
cd ViVQA-X/api
python main.py

This server provides the Aligned Candidate Generator tool to the agents.

Step 2: Run the LLM Server (Optional, for local models)

If you are using a local LLM with VLLM, open a new terminal, activate the vmarc-qa environment, and start the server. The following is an example for the Qwen model:

conda activate vmarc-qa

# Command to serve a local LLM with VLLM
vllm serve Qwen/Qwen2-1.5B \
    --port 1234 \
    --dtype auto \
    --gpu-memory-utilization 0.5 \
    --max-model-len 4096 \
    --trust-remote-code

Step 3: Run the Main Experiment

Once the servers are ready, open another new terminal, activate the vmarc-qa environment, and run the main experiment script.

Configuration: Open the scripts/full_system.sh file to customize your run:

• SAMPLES: Set the number of samples to run. Set to 0 to run on the entire test set.
• TEST_JSON_PATH and TEST_IMAGE_DIR: By default, the script looks for data in the data/ directory. You can uncomment and modify these paths if your data is stored elsewhere.

Execution:

conda activate vmarc-qa
bash scripts/full_system.sh

Main Results

Performance comparison on the ViVQA-X test set. Our method establishes a new state-of-the-art in answer accuracy while maintaining highly competitive explanation quality.

Method	B1	B2	B3	B4	M	R-L	C	S	BS	Acc
Heuristic Baseline	8.46	3.0	1.3	0.6	8.5	7.9	0.5	0.6	70.8	10.1
LSTM-Generative	22.6	11.7	6.2	3.2	16.4	23.7	34.1	4.3	72.2	53.8
NLX-GPT	42.4	27.8	18.5	12.4	20.4	32.8	51.4	5.0	76.3	53.7
OFA-X	30.1	22.5	10.9	9.2	17.6	25.4	25.7	3.9	68.9	50.5
ReRe	34.0	21.2	13.8	9.0	20.8	29.4	35.5	4.2	74.9	47.5
VMARC-QA (ours)	27.5	14.8	8.1	4.4	17.6	22.4	23.6	4.0	76.0	64.8

Citation

If you use the code or methods from this work in your research, please cite our paper:

License

This project is licensed under the MIT License. See the LICENSE file for details.