ML Agent Bench

ML Agent Benchmarks are standardized evaluation tools designed to rigorously assess the capabilities of large multimodal language models (MLLMs) and their associated agents, focusing on areas like visual reasoning, cross-style robustness, and complex task execution in real-world scenarios such as code repositories. Current research emphasizes developing comprehensive benchmarks that go beyond simple accuracy metrics, incorporating evaluations of factors like language priors, multimodal task performance across diverse domains, and even privacy considerations. These benchmarks are crucial for driving progress in MLLM development, fostering fair comparisons between models, and ultimately advancing the field of artificial general intelligence.