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.
Optimal Common Contract with Heterogeneous Agents
Authors: Shenke Xiao, Zihe Wang, Mengjing Chen, Pingzhong Tang, Xiwang Yang7309-7316
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,516 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,946 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 | We first show this problem is NP-complete. Then we provide a dynamic programming algorithm to compute the optimal contract in O(n2m) time, where n, m are the number of agents and actions, under the assumption that the agents cost functions obey increasing difference property. At last, we generalize the setting such that each agent can choose to directly produce a reward in [0, 1]. We provide an O(log n)-approximate algorithm for this generalization. |
| Researcher Affiliation | Collaboration | 1Tsinghua University, 2Shanghai University of Finance and Economics, 3Byte Dance EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms verbally and with mathematical formulas (e.g., dynamic programming recursion), but it does not include a formally structured pseudocode block or an algorithm box. |
| Open Source Code | No | The paper does not include any explicit statement about releasing open-source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and complexity analysis. It does not conduct empirical studies with datasets, thus no mention of publicly available training datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation on datasets. Therefore, no information on training/validation/test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |