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..
Generating Multi-Agent Trajectories using Programmatic Weak Supervision
Authors: Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts. |
| Researcher Affiliation | Collaboration | Eric Zhan Caltech EMAIL Stephan Zheng Salesforce EMAIL Yisong Yue Caltech EMAIL Long Sha & Patrick Lucey STATS EMAIL |
| Pseudocode | Yes | Algorithm 1 describes LF-window25, which computes macro-intents based on last positions in 25-timestep windows (LF-window50 is similar). Algorithm 2 describes LF-stationary, which computes macro-intents based on stationary positions. |
| Open Source Code | Yes | Code is available at https://github.com/ezhan94/multiagent-programmatic-supervision. |
| Open Datasets | Yes | Dataset was provided by STATS: https://www.stats.com/data-science/. |
| Dataset Splits | No | The paper states 'There are 107,146 training and 13,845 test examples.' but does not explicitly mention a validation dataset split. |
| Hardware Specification | No | Information not found. The paper does not specify the hardware used for running its experiments. |
| Software Dependencies | No | Information not found. The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with versions). |
| Experiment Setup | Yes | We model each latent variable zk t as a multivariate Gaussian with diagonal covariance of dimension 16. All output models are implemented with memory-less 2-layer fully-connected neural networks with a hidden layer of size 200. Our agent-models sample from a multivariate Gaussian with diagonal covariance while our macro-intent models sample from a multinomial distribution over the macro-intents. All hidden states (hg,t, h1 t, . . . h K t ) are modeled with 200 2-layer GRU memory cells each. We maximize the log-likelihood/ELBO with stochastic gradient descent using the Adam optimizer (Kingma & Ba, 2015) and a learning rate of 0.0001. |