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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
UltraModel: A Modeling Paradigm for Industrial Objects
Authors: Haoran Yang, Yinan Zhang, Qunshan He, Yuqi Ye, Jing Zhao, Wenhai Wang
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two different industrial objects demonstrate our Ultra Model outperforms existing methods, offering a novel perspective for addressing industrial modeling challenges. We conduct extensive experiments on two distinct industrial objects, demonstrating that Ultra Model outperforms baseline methods and validates its effectiveness for MIO tasks. We conducted a comprehensive ablation study to assess the individual contributions of each component in our model. |
| Researcher Affiliation | Academia | Haoran Yang , Yinan Zhang , Qunshan He , Yuqi Ye , Jing Zhao and Wenhai Wang College of Control Science and Engineering, Zhejiang University, Hangzhou, China EMAIL, EMAIL, |
| Pseudocode | No | The paper describes the proposed method in sections 3.1, 3.2, 3.3, and 3.4 using textual descriptions and architectural diagrams (Figures 2, 3, 4), but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions "Appendix is available at https://github.com/SpriteAndMango/UltraModel/blob/main/Appendix.pdf" which points to a PDF document, not a direct source code repository for the methodology described. |
| Open Datasets | No | We refer to the distillation column dataset as DIS-COL and the acentric factor dataset as ACE-FAC. A detailed introduction to these two industrial objects and their datasets can be found in Appendix B.1. The paper does not provide specific access information such as a link, DOI, or formal citation for these datasets to indicate their public availability. |
| Dataset Splits | No | The experimental setup and implementation details can be found in Appendix B. The main text does not explicitly provide specific dataset split percentages, sample counts, or a detailed splitting methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python, PyTorch, TensorFlow, or other libraries/solvers and their versions) needed to replicate the experiment. |
| Experiment Setup | No | The experimental setup and implementation details can be found in Appendix B. The main text does not contain specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or training configurations. |