arXiv:2606.14314v1 Announce Type: new Abstract: LLM agents have rapidly evolved into autonomous systems, yet a persistent information gap remains between users and agents: communication is costly, while users' identical preferences further limit information exchange. To investigate how agents should communicate across modalities, this paper formalizes Communication Policy, establishes textual and UI-based policies, and then evaluates communication policies across diverse environments, personas, and model combinations. Building information asymmetry for proactive agents, we set up two complemen
The rapid advancement and deployment of LLM agents necessitate formal frameworks for their communication, especially as they become more proactive and integrated into workflows.
Improved communication policies for LLM agents will enhance their autonomy, efficiency, and user interaction, directly impacting the adoption and effectiveness of agentic systems.
This formalization of communication policies moves agents from reactive tools to more sophisticated, proactively communicating entities, refining how they interact with users and other systems.
- · AI platform developers
- · Businesses adopting LLM agents
- · Automation software providers
- · Legacy workflow automation
- · Manual white-collar tasks
Further development of advanced agent architectures that can intelligently manage information exchange across modalities.
Increased user trust and reliance on proactive AI agents, leading to faster adoption in complex professional environments.
The emergence of new user interface paradigms tailored for proactive, multimodal agent communication.
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Read at arXiv cs.AI