Life After Benchmark Saturation: A Case Study of CORE-Bench
When a benchmark's accuracy saturates, it is often retired. This paper shows that this approach misses six other key dimensions: construct validity issues, out-of-distribution generalizability, efficiency, reliability, model versus scaffold importance, and human-agent collaboration uplift. Using CORE-Bench Hard, they surface construct validity threats, introduce an improved benchmark v1.1 and OOD suite, and find that the benchmark remains useful for measuring efficiency, reliability, and performance. A small-scale experiment shows human-agent collaboration yields about a 2x speedup.
[2606.26158] Life After Benchmark Saturation: A Case Study of CORE-Bench
[Submitted on 23 Jun 2026]
Title:Life After Benchmark Saturation: A Case Study of CORE-Bench
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Abstract:When a benchmark's accuracy saturates, it is often retired and replaced with a more challenging version. We show that this approach privileges accuracy and misses the opportunity to study six other key dimensions of agent performance: construct validity issues such as shortcuts, out-of-distribution generalizability, efficiency, reliability, the relative importance of the model versus the scaffold, and uplift from human-agent collaboration. We use CORE-Bench Hard, a benchmark for computational reproducibility of scientific code, as a case study to demonstrate that measuring agents along these dimensions yields meaningful insights into agent performance even after accuracy saturates. First, we surface threats to construct validity in CORE-Bench Hard that are difficult to anticipate with less capable agents. We introduce an improved benchmark, CORE-Bench v1.1, and an out-of-distribution task suite, CORE-Bench OOD. Second, we find that despite accuracy saturation, CORE-Bench v1.1 remains useful for measuring efficiency, reliability, model performance, and scaffold performance. Finally, we conduct a small-scale randomized experiment to measure uplift from human-agent collaboration on real-world computational reproducibility tasks. We find a statistically significant speedup by about a factor of two -- likely underestimated due to one-fifth of human-only reproductions reaching the time limit before completing -- and describe various other findings. Together, our contributions present a more rigorous alternative to the dominant accuracy-centric evaluation paradigm.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.26158 [cs.AI]
(or arXiv:2606.26158v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.26158
arXiv-issued DOI via DataCite
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From: Nitya Nadgir [view email] [v1] Tue, 23 Jun 2026 22:30:44 UTC (2,036 KB)
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