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..
Neurosymbolic Transformers for Multi-Agent Communication
Authors: Jeevana Priya Inala, Yichen Yang, James Paulos, Yewen Pu, Osbert Bastani, Vijay Kumar, Martin Rinard, Armando Solar-Lezama
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate how our approach can synthesize policies that generate low-degree communication graphs while maintaining near-optimal performance. |
| Researcher Affiliation | Academia | 1 MIT CSAIL 2 University of Pennsylvania |
| Pseudocode | No | The paper describes the synthesis algorithm in prose but does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | The code and a video illustrating the different tasks are available at https://github.com/jinala/multi-agent-neurosym-transformers. |
| Open Datasets | No | The paper describes custom simulation environments ('Formation task', 'Unlabeled goals task') but does not provide access information or citations for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions training with '10k rollouts' and building a dataset for synthesis using '300 rollouts' but does not specify explicit training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | For all approaches, we train the model with 10k rollouts. For synthesizing the programmatic policy, we build a dataset using 300 rollouts and run MCMC for 10000 steps. We retrain the transformer with 1000 rollouts. We constrain the maximum in-degree to be a constant d0 across all approaches (except tf-full, where each agent communicates with every other agent); for dist and hard-attn, we do so by setting the communication neighbors to be k = d0, and for prog and prog-retrain, we choose the number of rules to be K = d0. |