![[2510.19771] Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2510.19771] Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents
Summary
The paper presents PROBE, a new framework for measuring proactive problem-solving capabilities in LLM agents, highlighting their limitations and future research directions.
Why It Matters
As LLMs evolve towards more autonomous functionalities, understanding their proactive problem-solving abilities is crucial for advancing AI applications. This study addresses a significant gap in evaluating these capabilities, paving the way for improved AI systems that can better anticipate and resolve user needs.
Key Takeaways
- PROBE framework decomposes proactivity into three core capabilities.
- Current LLMs struggle with proactive problem-solving, achieving only 40% performance.
- The study identifies mutual failure modes among leading models, indicating areas for improvement.
Computer Science > Artificial Intelligence arXiv:2510.19771 (cs) [Submitted on 22 Oct 2025 (v1), last revised 19 Feb 2026 (this version, v3)] Title:Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents Authors:Gil Pasternak, Dheeraj Rajagopal, Julia White, Dhruv Atreja, Matthew Thomas, George Hurn-Maloney, Ash Lewis View a PDF of the paper titled Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents, by Gil Pasternak and 6 other authors View PDF HTML (experimental) 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) identifying specific bottlenecks, and (3) executing appropriate resolutions. We apply PROBE to evaluate leading LLMs and popular agentic frameworks, showing that even state-of-the-art models struggle to solve this benchmark. Computing our consistent measurements across frontier LLMs and agents, we find that the best end-to-end performance of 40% is achieved by both GPT-5 and Claude Opus-4.1. Additionally, we demonstrate the relative cap...