Ground truth is a process, not a dataset

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Machine learning

Ground truth is a process, not a dataset

Automatically fact-checking long, AI-generated research reports poses new challenges — including benchmarking.

By Venkatesh Saligrama

June 3, 2026

4 min read

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Overview by Amazon Nova

A new audit-then-score protocol improves accuracy on benchmarks from 60.8% to 90.9% by using AI models to challenge and refine human-generated benchmarks. The audit-then-score protocol transforms benchmarks into an evolving process, emphasizing ongoing collaboration between humans, models, and evidence. This approach highlights the need for dynamic and adaptive evaluation systems as AI capabilities advance, ensuring benchmarks remain relevant and accurate.

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Today, the key challenge in AI isn’t only how to build better models; it’s how to build evaluation systems that can keep up. Search-augmented AI systems can now produce deep research reports — long, polished syntheses of many sources that increasingly resemble expert analysis. But those reports are useful only if their claims are supported by the underlying literature. Most existing fact-checking tools work best when a claim can be matched to a short quote or a single document. But in AI-generated research reports, a single sentence may combine evidence from several sources. It can depend on the surrounding report for context, and it might compare assertions in a way that no single source does on its own. When Amazon’s Artificial General Intelligence (AGI) group started working on the problem of evaluating AI-generated research reports, we thought that the main technical challenge would be building a stronger AI fact checker. But before you can evaluate an AI fact checker, you need a benchmark, a standardized test set used to measure performance. And in this setting, building the benchmark turned out to be at least as hard as building the model. Traditionally, we view the ground truth for a problem as a fixed dataset. But we discovered that to evaluate complex AI properly, ground truth has to become a process. We call that process audit-then-score , and we present it, together with two accompanying datasets, in a paper we recently published to arXiv . When static datasets break down

In the standard method for measuring AI performance, human experts label examples, those labels become the “ground truth” (the undisputed correct answers), and models are scored against them. To test this approach with AI-generated research reports, we recruited PhD-level specialists from fields such as computer science, control theory, education, public health, and environmental engineering. We asked them to verify claims from reports in their own specialties, mixing in a hidden set of claims whose answers we already knew. The result was sobering. In a controlled study, unassisted experts achieved only 60.8% accuracy on the hidden set of known answers. The issue was not a lack of…