LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability
Deliberation plays a crucial role in collaboration; when humans work together, they naturally engage in communication to align information and reach an agreement. In this paper, we investigate deliberative large language model (LLM) agents under partially observable joint decision-making tasks. We formalize deliberative collaboration as a cooperative joint decision problem with partial and asymmetric observations, and introduce a scalable benchmark that instantiates this problem across multiple task settings and domains in which agents must exchange information through deliberation to reach a
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- PossiblePossibly related (embedding) · 47%agent-tools →
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- LinkedLinked via arxiv author · 85%Chenxu Wang →
“LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability”
- LinkedLinked via arxiv author · 85%Yongkun Yang →
“LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability”
- LinkedLinked via arxiv author · 85%Boyuan Du →
“LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability”
- LinkedLinked via arxiv author · 85%Shiwei Lin →
“LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability”
- LinkedLinked via arxiv author · 85%Huaping Liu →
“LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability”