Unverified50% confidenceFactExact time
MLR is specifically designed to catch hidden core-claim errors in AI-generated or research papers.
1
Sources
50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedCore-claim errors refer specifically to critical errors that directly affect whether the main conclusions of a study hold, as distinct from surface-level issues such as grammar, citation formatting, or vague wording.64% similarUnverifiedWhen OCR output is fed directly to an LLM, recognition errors are amplified and propagated, causing the model to generate plausible-sounding but incorrect conclusions based on erroneous text60% similarUnverifiedDeterministic scorers can reliably catch field format errors, but averaging them across the entire suite cannot tell you where the errors are concentrated60% similarUnverified作者归纳出四类高频确定性工具调用错误：可选字段传null值、数组格式错误、空对象包裹、参数值缺失60% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/996941API
curl https://kongchang.com/api/v1/knowledge/claims/996941MCP
get_claim(id=996941)