When a benchmark saturates, the typical reaction is to retire it and replace it with a harder version. But this misses the chance to explore other dimensions of an agent's performance. A team of researchers used CORE-Bench Hard, a test on computational reproducibility of scientific code, to show that even after accuracy peaks, meaningful insights can be gained by measuring efficiency, reliability, the relative weight of model versus scaffold, and the lift from human-agent collaboration.

The construct validity tangle

The first step was to uncover threats to construct validity in CORE-Bench Hard—shortcuts that less capable agents didn't expose. To address this, the team introduced CORE-Bench v1.1 and an out-of-distribution (OOD) task suite. This is critical: when evaluating an LLM in an on-premise context, where hardware constraints and the need for control push toward quantized models or limited context windows, such validity issues are amplified. A single accuracy metric risks rewarding solutions that exploit dataset artifacts but fail in real-world scenarios where predictability is key.

Efficiency and reliability: the metrics that matter for self-hosting

The authors found that, despite accuracy saturation, CORE-Bench v1.1 remains useful for measuring efficiency and reliability. In a self-hosted deployment, these two dimensions become decisive: the computational cost of running an inference pipeline is measured not only in tokens per second but also in system stability and VRAM consumption over long sessions. A benchmark that assesses whether an agent can complete a task without crashes or unexpected drifts provides far more realistic guidance than a simple correctness score. And since many on-premise stacks run on consumer GPUs or shared-resource servers, reliability becomes a often underestimated TCO factor.

The boost of human-agent collaboration

The randomized experiment on real reproducibility tasks showed a statistically significant speedup—about a factor of two—when humans collaborated with the agent. For on-premise scenarios, this holds particular value: investing in a local AI assistant should be measured not only by its ability to replace the operator but also by how effectively it augments human work. In enterprise environments where data sovereignty demands isolation, human-machine synergy can reduce development time without exposing sensitive code or data to the outside world.

Beyond accuracy: a more mature paradigm

The lesson from CORE-Bench is clear: stop chasing accuracy saturation and embrace multidimensional evaluation. For those designing on-premise inference architectures, this means building test pipelines that include efficiency, robustness, and human-interaction metrics, along with OOD suites to avoid shortcuts. Ultimately, an agent running on proprietary hardware must not only answer correctly but do so predictably, efficiently, and in harmony with the team. A shift in perspective that turns benchmarks from finish lines into continuous diagnostic tools.