Paper proposes a CRED taxonomy and benchmark to measure research-error detectors
soumitrashukla9 · x · 2026-07-22
The paper proposes three ingredients for measuring verifier quality in AI research: a versioned CRED taxonomy for research errors, a benchmark with deterministic scoring to quantify detection of real errors, and a longitudinal evaluation of verifiers, including both models and harnesses. The image adds concrete error classes such as prose-table inconsistency, paper-code inconsistency, execution/reproducibility failures, and data-integrity issues.
Related event: Project APE Evaluates LLMs as Autonomous Research Error Verifiers(5 posts)→
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