# AMONGAGENTS: Evaluating Large Language Models in the Interactive Text-Based Social Deduction Game

arXiv: https://arxiv.org/abs/2407.16521 · HTML: https://mlsanity.com/paper/2407.16521/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Character-LLM: A Trainable Agent for Role-Playing](https://mlsanity.com/paper/2310.10158/) (2023)
- [Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf](https://mlsanity.com/paper/2309.04658/) (2023)
- [Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models](https://mlsanity.com/paper/2305.14763/) (2023)
- [Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game](https://mlsanity.com/paper/2310.18940/) (2023)
- [War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars](https://mlsanity.com/paper/2311.17227/) (2023)
- [MetaAgents: Large Language Model Based Agents for Decision-Making on Teaming](https://mlsanity.com/paper/2310.06500/) (2023)
- [Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning](https://mlsanity.com/paper/2210.05492/) (2022)
- [Welfare Diplomacy: Benchmarking Language Model Cooperation](https://mlsanity.com/paper/2310.08901/) (2023)
- [Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games](https://mlsanity.com/paper/2312.00746/) (2023)
- [Cooperation on the Fly: Exploring Language Agents for Ad Hoc Teamwork in the Avalon Game](https://mlsanity.com/paper/2312.17515/) (2023)

## Built on by (most cited first, 28 of 28)

- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [Among Us: A Sandbox for Measuring and Detecting Agentic Deception](https://mlsanity.com/paper/2504.04072/) (2025)
- [A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios](https://mlsanity.com/paper/2412.03920/) (2024)
- [Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents](https://mlsanity.com/paper/2502.13012/) (2025)
- [Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models](https://mlsanity.com/paper/2412.17128/) (2024)
- [WOLF: Werewolf-based Observations for LLM Deception and Falsehoods](https://mlsanity.com/paper/2512.09187/) (2025)
- [Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game](https://mlsanity.com/paper/2501.14225/) (2025)
- [Beyond Survival: Evaluating LLMs in Social Deduction Games with Human-Aligned Strategies](https://mlsanity.com/paper/2510.11389/) (2025)
- [Among Them: A game-based framework for assessing persuasion capabilities of LLMs](https://mlsanity.com/paper/2502.20426/) (2025)
- [DVM: Towards Controllable LLM Agents in Social Deduction Games](https://mlsanity.com/paper/2501.06695/) (2025)
- [Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts](https://mlsanity.com/paper/2508.06361/) (2025)
- [Vox Deorum: A Hybrid LLM Architecture for 4X / Grand Strategy Game AI -- Lessons from Civilization V](https://mlsanity.com/paper/2512.18564/) (2025)
- [QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents](https://mlsanity.com/paper/2605.27068/) (2026)
- [PRISON: Unmasking the Criminal Potential of Large Language Models](https://mlsanity.com/paper/2506.16150/) (2025)
- [InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles](https://mlsanity.com/paper/2508.16072/) (2025)
- [Shall We Play a Game? Language Models for Open-ended Wargames](https://mlsanity.com/paper/2509.17192/) (2025)
- [CSP4SDG: Constraint and Information-Theory Based Role Identification in Social Deduction Games with LLM-Enhanced Inference](https://mlsanity.com/paper/2511.06175/) (2025)
- [How Clued up are LLMs? Evaluating Multi-Step Deductive Reasoning in a Text-Based Game Environment](https://mlsanity.com/paper/2603.17169/) (2026)
- [Evaluating Large Language Models in a Complex Hidden Role Game](https://mlsanity.com/paper/2605.22826/) (2026)
- [Multicultural Spyfall: Assessing LLMs through Dynamic Multilingual Social Deduction Game](https://mlsanity.com/paper/2601.09017/) (2026)
- [From Feelings to Metrics: Understanding and Formalizing How Users Vibe-Test LLMs](https://mlsanity.com/paper/2604.14137/) (2026)
- [SocialGrid: A Benchmark for Planning and Social Reasoning in Embodied Multi-Agent Systems](https://mlsanity.com/paper/2604.16022/) (2026)
- [Triadic Werewolf: A Jester Role for Multi-Hop Theory of Mind in LLMs](https://mlsanity.com/paper/2606.27909/) (2026)
- [Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems](https://mlsanity.com/paper/2607.26120/) (2026)
- [Can Agents Deceive? Evaluating Reasoning and Deception in ParliamentBench using a Social Deduction Game](https://mlsanity.com/paper/2607.28146/) (2026)
- [Lies We Can See: Joint Verbal and Non-Verbal Deception by VLM Agents in Embodied Social Interactions](https://mlsanity.com/paper/2608.30428/) (2026)
- [Clueing up LLMs with Tool-Augmented Deductive Reasoning](https://mlsanity.com/paper/2609.18736/) (2026)
- [Playing social deduction games with reinforcement fine-tuned large language models](https://mlsanity.com/paper/2610.04261/) (2026)

## Abstract (arXiv)

Strategic social deduction games serve as valuable testbeds for evaluating the understanding and inference skills of language models, offering crucial insights into social science, artificial intelligence, and strategic gaming. This paper focuses on creating proxies of human behavior in simulated environments, with Among Us utilized as a tool for studying simulated human behavior. The study introduces a text-based game environment, named AmongAgents, that mirrors the dynamics of Among Us. Players act as crew members aboard a spaceship, tasked with identifying impostors who are sabotaging the ship and eliminating the crew. Within this environment, the behavior of simulated language agents is analyzed. The experiments involve diverse game sequences featuring different configurations of Crewmates and Impostor personality archetypes. Our work demonstrates that state-of-the-art large language models (LLMs) can effectively grasp the game rules and make decisions based on the current context. This work aims to promote further exploration of LLMs in goal-oriented games with incomplete information and complex action spaces, as these settings offer valuable opportunities to assess language model performance in socially driven scenarios.