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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios
Authors: Xiachong Feng, Longxu Dou, Minzhi Li, Qinghao Wang, Yu Guo, Haochuan Wang, Chang Ma, Lingpeng Kong
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios. While numerous studies have explored these agents in such settings, there is a lack of a comprehensive survey summarizing the current progress. To address this gap, we systematically review existing research on LLM-based social agents within game-theoretic scenarios. |
| Researcher Affiliation | Academia | µThe University of Hong Kong s Independent Researcher αNational University of Singapore γInstitute for Infocomm Research (I2R), A*STAR δPeking University βHarbin Institute of Technology EMAIL,EMAIL |
| Pseudocode | No | The paper does not contain any sections explicitly labeled "Pseudocode" or "Algorithm", nor does it present any structured code-like blocks describing a method or procedure. |
| Open Source Code | No | The paper is a survey and does not describe new methodology that would typically be accompanied by source code. It discusses open-source code in the context of other research it reviews, but does not provide its own. |
| Open Datasets | No | The paper is a survey and does not introduce new datasets. It mentions datasets and benchmarks from other research papers it reviews, but does not provide access information for a dataset created or utilized by the authors of this survey for their own methodology. |
| Dataset Splits | No | The paper is a survey and does not conduct its own experiments; therefore, it does not provide dataset splits for reproduction. |
| Hardware Specification | No | The paper is a survey and does not describe any specific hardware used for running its own experiments or analyses. |
| Software Dependencies | No | The paper is a survey and does not describe any specific software dependencies or versions used for running its own methodology or analyses. |
| Experiment Setup | No | The paper is a survey and does not present any original experimental work; therefore, it does not include details about an experimental setup, hyperparameters, or training settings. |