Beyond the benchmark: How an adaptive approach drives scientific discovery

For research and development (R&D) organizations, the promise of agentic AI is not a better one-time answer. It is a new way to explore complex scientific and engineering problems: pursuing multiple hypotheses, validating them against evidence, learning from what does not work, and adapting their approach as new information becomes available.

This unique nature of the agentic discovery process has been a core area of research for Microsoft, and a design principle for Microsoft Discovery, our platform for organizations embracing Frontier R&D.

Measuring adaptive AI for scientific discovery

A new benchmark result shows how that opportunity is becoming real. On Agent’s Last Exam, a demanding evaluation of long-running, tool-using professional tasks, Microsoft Discovery Engine with CLIO (Cognitive Loop via In-Situ Optimization) achieved higher scores than the other agentic harnesses evaluated across three scientific domains: 61.6% in health and medicine, 75.2% in physical sciences, and 64.6% in life sciences.

This result builds on Microsoft’s core research into what makes agentic discovery distinctive. CLIO enables independent reasoning paths to explore a problem, compare and share learning, and resolve the strongest trajectory into a single evidence-backed result. The system can determine when to keep exploring, change strategy, use a different model, or bring a domain expert into the loop.

The CLIO benchmark blog post describes this adaptive reasoning approach in depth. More broadly, this core innovation for scientific discovery, powered by agentic AI, is available to R&D organizations in every industry and the scientific community with Microsoft Discovery not only as a research breakthrough, but as a foundation for real R&D work.

Why scientific discovery requires adaptive reasoning