Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia
  Games
LLMs are used predominantly in synchronous communication, where a human user
and a model communicate in alternating turns. In contrast, many real-world
settings are inherently asynchronous. For example, in group chats, online team
meetings, or social games, there is no inherent notion of turns; therefore, the
decision of when to speak forms a crucial part of the participant's decision
making. In this work, we develop an adaptive asynchronous LLM-agent which, in
addition to determining what to say, also decides when to say it. To evaluate
our agent, we collect a unique dataset of online Mafia games, including both
human participants, as well as our asynchronous agent. Overall, our agent
performs on par with human players, both in game performance, as well as in its
ability to blend in with the other human players. Our analysis shows that the
agent's behavior in deciding when to speak closely mirrors human patterns,
although differences emerge in message content. We release all our data and
code to support and encourage further research for more realistic asynchronous
communication between LLM agents. This work paves the way for integration of
LLMs into realistic human group settings, from assistance in team discussions
to educational and professional environments where complex social dynamics must
be navigated.
68483359308cb7e626e80e88
Niv Eckhaus
claimed_verified
2025-06-10T15:20:12.219Z
67fa8526e1bb5094301f17c9
/avatars/b0ef0f77dc14a498919957f1a6d8688b.svg
Niv Eckhaus
user
niveck
68483359308cb7e626e80e89
Uri Berger
68483359308cb7e626e80e8a
Gabriel Stanovsky
Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia
  Games
LLMs are used predominantly in synchronous communication, where a human user
and a model communicate in alternating turns. In contrast, many real-world
settings are inherently asynchronous. For example, in group chats, online team
meetings, or social games, there is no inherent notion of turns; therefore, the
decision of when to speak forms a crucial part of the participant's decision
making. In this work, we develop an adaptive asynchronous LLM-agent which, in
addition to determining what to say, also decides when to say it. To evaluate
our agent, we collect a unique dataset of online Mafia games, including both
human participants, as well as our asynchronous agent. Overall, our agent
performs on par with human players, both in game performance, as well as in its
ability to blend in with the other human players. Our analysis shows that the
agent's behavior in deciding when to speak closely mirrors human patterns,
although differences emerge in message content. We release all our data and
code to support and encourage further research for more realistic asynchronous
communication between LLM agents. This work paves the way for integration of
LLMs into realistic human group settings, from assistance in team discussions
to educational and professional environments where complex social dynamics must
be navigated.
An adaptive asynchronous LLM-agent performs similarly to human players in online Mafia games, demonstrating the potential for integrating LLMs into realistic group settings with complex social dynamics.
asynchronous communication LLM-agent online Mafia games social dynamics