• grounding — Tying a model's answers to checkable sources — retrieved documents, live data, tool results — instead of letting it answer from memory alone.
• agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.

Measures whether agents improve through sustained interaction rather than what they already know, and reports a log-sigmoid learning curve plus a doubling of learning speed every three months across model generations.

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