arXiv:2604.03244v2 Announce Type: replace Abstract: This position paper argues that standardized item-level benchmark data should become the default infrastructure for AI evaluation. Current evaluations suffer from underspecified item selection, construct misalignment, and poor generalization. The root cause of these failures is a misplaced focus on aggregate model scores. Without item-level evidence, validity claims cannot be assessed, resulting in inflated capability claims, misdirected research, and unwarranted trust in deployed systems. Our position is that designing valid evaluations requ
The proliferation of AI models and their increasing deployment necessitates more robust and reliable evaluation methods to ensure their safety and efficacy.
Standardized item-level data releases are crucial for fostering legitimate AI progress, preventing misdirection, and building warranted trust in AI systems.
The focus for AI evaluation shifts from aggregate model scores to transparent, verifiable, item-level data, which can improve the validity and generalizability of assessment.
- · AI safety researchers
- · Responsible AI developers
- · AI regulatory bodies
- · End-users of AI systems
- · Companies making unsubstantiated AI claims
- · AI evaluation methods relying solely on aggregate scores
- · Research without rigorous evaluation infrastructure
AI development becomes more accountable with clearer metrics for performance and reliability.
Increased transparency in AI evaluation could accelerate the development of more robust and trustworthy AI models, leading to greater adoption in critical sectors.
A global standard for AI evaluation might emerge, influencing international collaboration and competition in AI development based on verified capabilities rather than marketing.
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Read at arXiv cs.AI