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+---
+layout: post
+title: "When AI Benchmarks Stop Measuring Progress"
+date: 2026-06-30
+published: true
+category: # Infrastructure | Research | Organization | Tooling | Documentation | Datasets
+image: /assets/img/blogs/saturation_scores_scatter.png
+authors:
+  - name: Mubashara Akhtar, Jeba Sania, Jocelyn D’Arcy, Leshem Choshen
+tags:
+  - "Benchmarks"
+  - "Evaluation"
+description: "Many popular AI benchmarks are losing their ability to separate leading models. Our ICML paper studies this growing problem of benchmark saturation."
+---
+
+## When AI Benchmarks Stop Measuring Progress
+
+AI progress is often measured with benchmarks like MMLU, HumanEval, and ARC-AGI. But what happens when the best models score almost the same?
+
+In our ICML paper "[When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation](https://arxiv.org/pdf/2602.16763)", we study this growing problem: many popular benchmarks are losing their ability to separate between leading models.
+
+### Saturation, When Benchmarks Stop Measuring Progress
+
+Benchmarks are useful when they can tell strong models apart from weaker ones. However, over time, top models start clustering together on benchmarks' leaderboards. A model might score 91%, another 92%, and another 92.3%. **The differences are so small that they may just reflect evaluation noise rather than real capability gains.** This is what we call benchmark saturation.
+
+Saturation is not the same as getting a perfect score. If the benchmark can no longer reliably measure differences between leading models, it stops being a useful measurement - even if plenty of room for improvement remains.
+
+### Measuring Saturation
+
+To understand how widespread this problem is, we analyzed 60 benchmarks covering reasoning, coding, knowledge, multilingual tasks, factuality, and agentic systems.
+
+We also developed an index to measure saturation. Instead of looking only at leaderboard scores, we asked: *Can this benchmark still reliably tell top models apart?* If score differences are smaller than the benchmark's uncertainty, then those differences may not be meaningful.
+
+The result: Nearly **half of the benchmarks we studied already show high levels of saturation**, meaning they have limited ability to distinguish between today's leading models.
+
+### Common Assumptions Don't Hold
+
+We also tested several popular assumptions about what makes benchmarks last longer:
+
+- "Keep the test set private."
+- "Use open-ended generation instead of multiple-choice questions."
+- "Evaluate models in many languages."
+
+Surprisingly, none of these factors showed a strong relationship with benchmark saturation once benchmark age was taken into account.
+
+*What seemed to matter more?* If benchmarks are measurement tools, they need maintenance. Based on our findings, benchmark creators should consider:
+
+- Larger and more diverse evaluation sets
+- Dynamic benchmark updates
+- Adversarial data collection
+- Uncertainty-aware reporting
+- Explicit criteria for benchmark revision or retirement
+
+### Saturation Isn't Always Bad
+
+If models genuinely master a capability, a saturated benchmark may be evidence of real progress - BUT the challenge is figuring out whether a benchmark is saturated because:
+
+- Models have solved the task, or
+- The benchmark has stopped measuring meaningful differences.
+
+**As models continue to improve, telling these two apart will become increasingly important.**
+
+### Explore our Work
+
+- Paper: [https://arxiv.org/pdf/2602.16763](https://arxiv.org/pdf/2602.16763)
+- Code & dataset: [https://github.com/evaleval/benchmark-saturation](https://github.com/evaleval/benchmark-saturation)
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