Evaluation has always been the foundation of AI progress. Without reliable ways to measure performance, improvement cannot be tracked or compared.<\/p>\n\n\n
Today, this foundation is becoming unstable.<\/p>\n\n\n
Benchmarks are created, quickly saturated, and replaced. Metrics lose meaning faster than before. Results depend more on the specific test used than on any stable notion of capability.<\/p>\n\n\n
This creates a new situation: evaluation is no longer a fixed background. It becomes part of the system that is constantly changing.<\/p>\n\n\n
When evaluation changes too quickly, it cannot serve as a stable reference. Scores become difficult to interpret. Comparisons lose clarity. Confidence in results decreases.<\/p>\n\n\n
The problem is not only that models are improving. It is that the tools used to measure them cannot keep up.<\/p>\n\n\n
AI progress is therefore no longer just a question of capability. It is increasingly a question of whether evaluation itself can remain reliable under rapid change.<\/p>\n\n\n
Read the Structural Note (SN-01)<\/a> for the complete framework.<\/p>\n\n\n
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