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Anatomy of a benchmark pipeline

From The Good, the Bad, and the Ugly: Why Coding Benchmarks Are Broken · ≈2:30

“And so, the equation is simple.”

“If prompts and instructions are great and verifiers and rubrics are doing their job while the harness is preventing um or creating an environment that is good for a benchmark, we should have amazing results.”

What’s in it

Breaks down the core anatomy of an AI benchmark pipeline
Explains how models get graded via verifiers, rubrics, and harnesses
Gives a simple 'equation' for what makes benchmark results trustworthy

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Clip transcript

nailed like simplified it to the most basics. Um and so, the way I see it is that it starts as a prompt or an instruction. That prompt is fed to models and agents. Agents provide solutions. Those solutions are verified uh and graded through verifiers and rubrics. All of that is wrapped in a harness that's that's preventing it from um from the external factors. And if it all goes good, uh we have um trajectories, scores, and um metadata that we can use um to to to verif- to basically uh rank um models. And so, the equation is simple. If prompts and instructions are great and verifiers and rubrics are doing their job while the harness is preventing um or creating an environment that is good for a benchmark, we should have amazing results.