Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents
📰 ArXiv cs.AI
Learn to measure proactive problem solving in LLM agents using the PROBE framework and improve their ability to anticipate user needs
Action Steps
- Develop a PROBE framework to evaluate proactive problem solving in LLM agents
- Implement PROBE to test reasoning across sources and longer time horizons
- Analyze the results to identify areas for improvement in LLM agent proactivity
- Apply the insights to refine LLM agent architecture and training data
- Configure the PROBE framework to accommodate diverse problem-solving scenarios
Who Needs to Know This
AI researchers and engineers working on LLM agents can benefit from this knowledge to develop more proactive and autonomous systems
Key Insight
💡 PROBE framework helps evaluate proactive problem solving in LLM agents beyond localized context
Share This
🤖 Improve LLM agent proactivity with PROBE framework! 📈
Key Takeaways
Learn to measure proactive problem solving in LLM agents using the PROBE framework and improve their ability to anticipate user needs
Full Article
Title: Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents
Abstract:
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 decompo
Abstract:
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 decompo
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