arXiv:2605.27141v1 Announce Type: new Abstract: Large language models (LLMs) have evolved into interactive agents that collaborate with users in real-world tasks. Effective collaboration in such settings increasingly depends on understanding the user beyond what is explicitly stated, as user intent is often reflected in fragmented daily interactions and requires both personalized modeling and proactive interaction. However, existing agent benchmarks primarily evaluate reasoning and tool use, largely overlooking the challenges of inferring and leveraging user preferences in realistic scenarios.
The rapid advancement of large language models is transitioning them into interactive agents, necessitating more sophisticated evaluation methods that reflect real-world user interactions.
This development highlights the critical need for benchmarks that assess personalized and proactive AI agents, moving beyond basic reasoning and tool use to more human-like collaboration.
The focus of agent evaluation shifts from isolated tasks to continuous, personalized, and proactive interactions, pushing development towards more effective and adaptable AI agents.
- · AI agent developers
- · Companies building personalized AI services
- · SaaS providers integrating advanced AI agents
- · Developers relying on simplistic AI benchmarks
- · AI models lacking personalization capabilities
New benchmarks will drive the development of AI agents capable of deeper user understanding and proactive engagement.
The improved capabilities of AI agents will accelerate the automation of white-collar workflows and specialized tasks.
As AI agents become more autonomous and personalized, ethical considerations around data privacy and control will intensify.
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Read at arXiv cs.AI