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..
Hypertron: Explicit Social-Temporal Hypergraph Framework for Multi-Agent Forecasting
Authors: Yu Tian, Xingliang Huang, Ruigang Niu, Hongfeng Yu, Peijin Wang, Xian Sun
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on several challenging real-world trajectory forecasting datasets show that Hypertron outperforms a wide array of stateof-the-art methods while saving over 60% parameters and reducing 30% inference time. |
| Researcher Affiliation | Academia | 1Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, Chinese Academy of Sciences 2 School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences |
| Pseudocode | No | The paper does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | To evaluate our methods, we conduct experiments on three publicly examined datasets: The ETH/UCY datasets and the Stanford Drone Dataset. |
| Dataset Splits | No | The paper does not specify exact train/validation/test split percentages or sample counts for the datasets. |
| Hardware Specification | Yes | To measure the inference time, we use a V100 GPU. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' but does not specify software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We train the Hypertron with Adam optimizer, and the initial learning rate is 0.001. The number of hyperedges in the social hypergraph is set to 32, and each hyperedge ei s indicates the social correlation of the i-th agent with others. Similarly, the counterpart of the temporal hypergraph is set to 20, and each ej t indicates the temporal correlation of the agent in the j-th timestep with others. |