Unverified50% confidenceConceptExact time
Binning/bucketing is the practice of slicing continuous reported values into intervals and then computing the actual hit rate bucket by bucket, and its key discipline is to set the buckets before looking at the data, otherwise it creates an illusion of calibration.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe core operation of the paper is to lift the finite-sample bound to a function bound, by refining the sample set until it is dense on the domain, and measuring the per-sample error so that the constant remains consistent.66% similarUnverifiedIf a saturated dataset is still included in the aggregate total score, its effect is equivalent to mixing a constant into the average score, which not only contributes no information but also dilutes the signal weight of other datasets that still have discriminating power.64% similarUnverifiedScoring outputs are more sensitive to numerical perturbations, and low-precision quantization easily causes shifts in score ranking, while ranking accuracy is the core metric for filtering and candidate ranking tasks.63% similarUnverifiedThe confidence drift of each position is defined as the difference between the confidence score at the time of the original commitment and the confidence score obtained upon re-evaluation.63% similarVerifiedBenchmark data contamination refers to the phenomenon where, after a test set is made public, its questions may appear in the pre-training corpora of subsequent models, causing scores to reflect memorization rather than genuine reasoning ability.63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1037019API
curl https://kongchang.com/api/v1/knowledge/claims/1037019MCP
get_claim(id=1037019)