arXiv:2605.25492v1 Announce Type: new Abstract: Pairwise model comparisons drawn from foundation-model benchmarks ("A is safer than B") are read as quantitative verdicts but hinge on harness choices benchmark papers under-specify. We close one theory-benchmark loop on this primitive: a finite-envelope proposition tying a measurable pairwise-disagreement rate to whether the strict ordering admits a configuration-pair reversal, paired with a commit-stamped evaluation protocol that operationalises it on widely cited alignment benchmarks. On every benchmark we test, configuration choice alone can
This research highlights a growing concern within the AI community regarding the reliability and reproducibility of foundational model evaluations, especially concerning safety benchmarks.
A strategic reader should care because the instability in safety benchmarks means that claims of model safety or superiority are often fragile and easily manipulated by configuration choices, impacting investment, regulation, and deployment.
The understanding of AI model safety 'rankings' shifts from quantitative verdicts to highly context-dependent statements, necessitating greater transparency and rigorous testing methodologies.
- · AI safety researchers
- · Developers of robust evaluation methodologies
- · Users prioritizing verifiable AI safety claims
- · Companies making unsubstantiated AI safety claims
- · Benchmarks with poor reproducibility
- · Rapid, unchecked deployment of 'safe' AI models
Increased scrutiny and demand for transparency in AI model evaluation and benchmarking.
Development of new, more robust, and configuration-independent alignment benchmarks and testing protocols.
Potential for regulatory bodies to mandate specific reproducibility standards for AI safety claims in deployed systems.
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Read at arXiv cs.LG