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.
Mechanism Design via the Interim Relaxation
Authors: Kshipra Bhawalkar, Marios Mertzanidis, Divyarthi Mohan, Alexandros Psomas
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 34,608 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,974 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 | The paper does not include experiments. All claims made in the abstract are backed by mathematically proven theorems. (from NeurIPS checklist justifications for sections 1 and 4. The paper presents a novel framework, defines theoretical concepts like two-level OCRS, and proves theorems such as Theorem 1, 2, 3, 4, 5, and 6, and their corollaries and propositions, without any empirical evaluations or data analysis.) |
| Researcher Affiliation | Collaboration | Kshipra Bhawalkar Google Research EMAIL Marios Mertzanidis Purdue University EMAIL Divyarthi Mohan Tel Aviv University EMAIL Alexandros Psomas Purdue University EMAIL |
| Pseudocode | Yes | ALGORITHM 1: Our framework for t OCRSs, ALGORITHM 2: Bernoulli Division [Mor21], ALGORITHM 3: Our framework for t CRSs, ALGORITHM 4: Our sequential procurement auction when given an OCRS |
| Open Source Code | No | Justification: The paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not include experiments, thus no datasets are used or made publicly available. (as per NeurIPS checklist justification for section 4) |
| Dataset Splits | No | The paper does not include experiments, therefore there is no discussion of dataset splits. |
| Hardware Specification | No | Justification: The paper does not include experiments. |
| Software Dependencies | No | Justification: The paper does not include experiments requiring code. |
| Experiment Setup | No | Justification: The paper does not include experiments. |