AI models compete on benchmark scores: SWE-bench Pro tests coding, while AIME and GPQA Diamond, tracked by Artificial Analysis, test math and science.
What if alignment benchmarks mattered just as much, or more? They seem to exist, but deserve more attention. Alongside solving problems, models should be judged on whether they are honest, accept corrections, respect people's choices, and stay under human control.
The whole AI community could build on this work and maintain a shared set of tests. Researchers, labs, and independent testers would share challenges, methods, and findings in a public collection that everyone could improve.
The aim would be to set the requirements before the next, more capable models arrive. Labs would develop ways to meet them, and independent testers would check the results. Under a shared, enforced release rule, a model that fails could not be released or used for high-stakes work inside a lab, such as building future AI systems.
The community would set the bar and check the evidence. Each lab would work out how to reach it.
Tests would examine behavior in unfamiliar situations, supported by research into how models work internally. Passing would be useful evidence, not a promise that nothing could go wrong.
If customers and reviewers valued these results at least as highly as coding, math, and other skills, labs would have a stronger reason to improve alignment. Helping people stay in control would become part of what makes a model good.
Originally published on Turtleand Growth.
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