# VDMA: Video Question Answering with Dynamically Generated Multi-Agents

arXiv: https://arxiv.org/abs/2407.03610 · HTML: https://mlsanity.com/paper/2407.03610/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 6 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding](https://mlsanity.com/paper/2308.09126/) (2023)
- [VideoAgent: Long-form Video Understanding with Large Language Model as Agent](https://mlsanity.com/paper/2403.10517/) (2024)
- [Learning Video Representations from Large Language Models](https://mlsanity.com/paper/2212.04501/) (2022)
- [A Simple LLM Framework for Long-Range Video Question-Answering](https://mlsanity.com/paper/2312.17235/) (2023)

## Built on by (most cited first, 2 of 2)

- [VideoMultiAgents: A Multi-Agent Framework for Video Question Answering](https://mlsanity.com/paper/2504.20091/) (2025)
- [DIVE: Deep-search Iterative Video Exploration A Technical Report for the CVRR Challenge at CVPR 2025](https://mlsanity.com/paper/2506.21891/) (2025)

## Abstract (arXiv)

This technical report provides a detailed description of our approach to the EgoSchema Challenge 2024. The EgoSchema Challenge aims to identify the most appropriate responses to questions regarding a given video clip. In this paper, we propose Video Question Answering with Dynamically Generated Multi-Agents (VDMA). This method is a complementary approach to existing response generation systems by employing a multi-agent system with dynamically generated expert agents. This method aims to provide the most accurate and contextually appropriate responses. This report details the stages of our approach, the tools employed, and the results of our experiments.