arXiv:2510.19771v4 Announce Type: replace Abstract: LLM-based agents are increasingly moving towards proactivity: rather than awaiting instruction, they exercise agency to anticipate user needs and solve them autonomously. However, evaluating proactivity is challenging; current benchmarks are constrained to localized context, limiting their ability to test reasoning across sources and longer time horizons. To address this gap, we present PROBE (Proactive Resolution Of BottlEnecks). PROBE decomposes proactivity as a pipeline of three core capabilities: (1) searching for unspecified issues, (2)
The rapid advancement of LLMs has reached a point where the next frontier is autonomous proactivity, making current evaluation methods insufficient for this emerging capability.
This development allows for a more accurate assessment and acceleration of AI agents' ability to anticipate needs and solve problems independently, impacting white-collar workflows.
The focus in LLM agent development shifts from mere reactivity to proactive problem-solving, requiring new benchmarking tools to measure and drive progress.
- · AI Agent developers
- · Generative AI platforms
- · Businesses adopting autonomous AI
- · Consumers of AI-driven services
- · SaaS companies reliant on manual human-agent interaction
- · Outdated AI testing methodologies
LLM agents will become more effective at anticipating and solving complex, multi-source problems without explicit user prompts.
This will lead to increased adoption of autonomous AI in various industries, fundamentally altering business process automation and administrative roles.
The enhanced proactivity of AI could potentially create entirely new categories of services and industries, while displacing existing human-centric workflows at an accelerated pace.
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Read at arXiv cs.AI