Vision-DeepResearch explores the limits of multimodal large language models (MLLMs) by focusing on long-horizon reasoning and search. This research addresses the challenge of enabling MLLMs to perform complex tasks requiring extended interactions and information retrieval. We designed the Vision-DeepResearch Benchmark (VDR-Bench) to evaluate and compare the performance of various MLLMs across several tasks utilizing multimodal inputs. By pushing the boundaries of MLLM capabilities, this work aims to stimulate further innovation in the field.
This project introduces a comprehensive benchmark designed specifically for evaluating multimodal MLLMs, a notable advancement in the field. The benchmark's design emphasizes long-horizon reasoning and extensive search interactions, providing a more rigorous assessment than existing benchmarks. The dataset and code, including both SFT and RL versions, are publicly available, fostering community contribution and reproducibility. The inclusion of a detailed performance evaluation across various MLLMs provides valuable insights into the current state of the art.
- Core Functionality: Provides a benchmark dataset and evaluation framework for multimodal MLLMs.
- Supported Platforms: Designed to be adaptable to various MLLM architectures and platforms.
- Configuration/Extensibility: The benchmark is modular and designed for easy expansion with new tasks and models.
- Performance/Scalability: Includes a comprehensive performance evaluation, enabling comparisons across different MLLMs and prompting future scalability studies.
- Developer Experience: Provides readily available datasets and code, making it easy for researchers to reproduce and build upon the work.
The project is actively being developed and released, with initial datasets and code now available. Recent releases indicate ongoing development and a commitment to providing tools for the community. Early performance data has been published, although more comprehensive evaluations are expected in the near future. The project's open release signifies a commitment to collaborative research.
This project benefits researchers and developers working with multimodal large language models by providing a standardized benchmark for evaluating performance on complex reasoning and search tasks. It allows for objective comparisons between different MLLMs and helps drive progress in the field of multimodal AI. The publicly available datasets and code empower the community to contribute to and build upon this research.