Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents

1. 1Identify the limitations of existing benchmarks that only measure capability
2. 2Develop a reliability science framework with metrics that capture consistent success across repeated attempts
3. 3Apply the framework to long-horizon LLM agents to evaluate their reliability
4. 4Use the framework to inform model development and deployment decisions

AI researchers and engineers working on LLM agents can benefit from this framework to evaluate the reliability of their models, and product managers can use it to inform deployment decisions

💡 Reliability and capability are distinct properties that diverge as task duration grows, and existing benchmarks are insufficient for evaluating long-horizon LLM agents

🚀 Introducing a reliability science framework for long-horizon LLM agents to measure consistent success across repeated attempts 🤖