LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability
arXiv:2607.06157v1 Announce Type: cross Abstract: 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 exc
The rapid advancement in large language models has made the development of sophisticated AI agents, capable of complex interaction and deliberation, an immediate research frontier.
The ability of LLM agents to engage in deliberative collaboration with partial information is critical for designing autonomous systems that can handle real-world complexity and uncertainty.
This research provides a formal framework and scalable benchmark for evaluating how LLM agents can achieve joint decision-making, moving beyond simple task execution towards more human-like collaborative intelligence.
- · AI product developers
- · Automation software companies
- · Businesses with complex workflows
- · Tasks requiring manual coordination
- · Legacy enterprise software
Further development of robust, autonomous AI systems capable of advanced reasoning and collaboration will accelerate.
Enterprise workflows for white-collar tasks will be increasingly automated and streamlined by interconnected agentic systems.
The definition of many white-collar jobs will fundamentally shift as AI agents handle deliberative and collaborative aspects of work, requiring humans to focus on oversight and higher-level strategy.
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Read at arXiv cs.AI