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...
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