A new benchmark called TasteVal measures how well AI systems design experiments on their own and interpret the results. The findings show an unusually steep improvement curve.

That capability has roughly doubled every three months since December 2025, according to Anthropic co-founder Jack Clark. The research lab P-Zero Research calls it "research taste," meaning the ability to pick the right experiments and draw the right conclusions from them. The best model tested outperformed a comparison group of 24 human experts at a fraction of the cost.

Clark co-founded Anthropic and writes the Import AI newsletter. Based on the numbers, he now believes recursive self-improvement (RSI), where AI systems develop their own successors, could become possible as early as 2027. He had previously expected 2028.

The benchmark doesn't prove that AI systems are already improving themselves, since it only captures a narrow slice of scientific work. But it does show where the debate is heading, away from raw model performance and toward the question of how much independent research work language models can actually take on.

P-Zero Research defines research taste as three abilities: identifying which research questions are worth pursuing, designing experiments to answer them, and correctly interpreting the results. TasteVal only tests the last two. Choosing the most promising research direction is often considered the core of scientific intuition, but in TasteVal that decision is already made for the model.

Each of the eight tasks specifies a fixed research goal from AI model development, along with a measurable score.

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