This GitHub repository provides RBench, a benchmark for evaluating robotics video generation, and RoVid-X, a dataset for training models with RGB, depth, and optical flow videos. The authors highlight limitations in existing video models and aim to enhance embodied AI research.

AIRS-Bench evaluates the research capabilities of large language model agents across 20 tasks in machine learning. Each task includes a problem, dataset, metric, and state-of-the-art value, allowing for performance comparison among various agent configurations. The framework supports contributions from the AI research community for further development.

LLM-SRBench is a new benchmark aimed at enhancing scientific equation discovery using large language models, featuring comprehensive evaluation methods and open-source implementation. It includes a structured setup guide for running and contributing new search methods, as well as the necessary configurations for various datasets. The benchmark has been recognized for its significance, being selected for oral presentation at ICML 2025.

SpatialScore introduces a comprehensive benchmark for evaluating multimodal large language models (MLLMs) in spatial understanding, consisting of the VGBench dataset and an extensive collection of 28K samples. It features the SpatialAgent, a multi-agent system designed for enhanced spatial reasoning, and reveals persistent challenges and improvements in spatial tasks through quantitative and qualitative evaluations.