Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
The Linear Distance Traveling Tournament Problem Allows an EPTAS
Authors: Jingyang Zhao, Mingyu Xiao
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 21,037 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,126 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we mainly focus on theoretical analysis of the approximation ratio. |
| Researcher Affiliation | Academia | Jingyang Zhao, Mingyu Xiao* University of Electronic Science and Technology of China EMAIL, EMAIL |
| Pseudocode | Yes | The Construction of the Schedule section uses detailed textual descriptions and tables (Table 1, 2, 3, 4, 5) to illustrate the step-by-step process of extending super-games into normal games. For example, Table 1 is titled 'Extending the middle super-game from Ui = {u1, . . . , u6} to Uj = {v1, . . . , v6} into path-games in d = 6 sessions, where home path-games are marked in bold', which functions as structured algorithmic steps. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve the use of specific datasets for training or evaluation. No datasets are mentioned or referenced. |
| Dataset Splits | No | The paper does not involve empirical experiments with datasets, and therefore, no training, validation, or test splits are described. |
| Hardware Specification | No | The paper is a theoretical work focusing on algorithm design and analysis. It does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software components or their version numbers used for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical, presenting an algorithm construction and its analysis. It does not include details on experimental setup, hyperparameters, or system-level training settings, as no experiments were conducted. |