Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
Authors: Jiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choi
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed framework is evaluated on both synthetic physics simulations and multiple real-world benchmark datasets in various areas. The experimental results illustrate that our approach achieves state-of-the-art performance in terms of prediction accuracy. |
| Researcher Affiliation | Collaboration | Jiachen Li1,2, Fan Yang2, Masayoshi Tomizuka2 Chiho Choi1 1Honda Research Institute, USA 2 University of California, Berkeley |
| Pseudocode | No | The paper describes its methodology using descriptive text and mathematical equations but does not include a formal pseudocode block or algorithm figure. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | In this paper, we validated the proposed framework Evolve Graph on one synthetic dataset and three benchmark datasets for real-world applications: Honda 3D Dataset (H3D) [31], NBA Sport VU Dataset (NBA), and Stanford Drone Dataset (SDD) [33]. |
| Dataset Splits | No | The paper describes historical observation periods and future prediction horizons for its data but does not explicitly provide training, validation, and test dataset splits with specific percentages, counts, or methodologies. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | No | The paper describes a double-stage training pipeline but does not provide specific details about experimental setup, such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |