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.
Markov Persuasion Processes: Learning to Persuade From Scratch
Authors: Francesco Bacchiocchi, Francesco Emanuele Stradi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 35,515 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,439 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 design a learning algorithm for the sender, working with partial feedback. We prove that its regret with respect to an optimal information-disclosure policy grows sublinearly in the number of episodes... Moreover, we provide lower bounds for our setting matching the guarantees of our algorithm. The paper does not include experiments. |
| Researcher Affiliation | Academia | Francesco Bacchiocchi Politecnico di Milano EMAIL Francesco Emanuele Stradi Politecnico di Milano EMAIL Matteo Castiglioni Politecnico di Milano EMAIL Alberto Marchesi Politecnico di Milano EMAIL Nicola Gatti Politecnico di Milano EMAIL |
| Pseudocode | Yes | Algorithm 1 Sender-Receivers Interaction at t [T] Algorithm 2 Optimistic Persuasive Policy Search (full) Algorithm 3 Optimistic Persuasive Policy Search (partial) |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: The paper does not include experiments. |
| Open Datasets | No | Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? Answer: [NA] Justification: The paper does not include experiments. |
| Dataset Splits | No | Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? Answer: [NA] Justification: The paper does not include experiments. |
| Hardware Specification | No | Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [NA] Justification: The paper does not include experiments. |
| Software Dependencies | No | Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] Justification: The paper does not include experiments. |
| Experiment Setup | No | Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] Justification: The paper does not include experiments. |