Unverified50% confidenceFactExact time
The error bound decomposes into two terms: the first term (m∧n)^(-1) reflects the precision of finite pretraining, and the second term N^(-1) reflects the localization ability with new context
1
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50%
Confidence
Long-term
Relevance
10/7/2026
First Seen
Sources
Related Claims
UnverifiedThe second term (1-M/N)(1-1/K) of the error rate lower bound corresponds to the traditional coverage problem, where the model can only guess randomly for facts that never appeared during training, with a correct-guess probability of 1/K.75% similarUnverifiedNaïve post-training quantization causes individual weight errors to accumulate and amplify across multiple layers—a phenomenon known as compounding error—and at 2-bit or 1-bit precision the model structure collapses74% similarUnverifiedUnder large-resolution initialization, applying constant-step gradient descent on m tasks (each containing n samples) yields a frozen estimator with error Õ((m∧n)^(-1) + N^(-1))70% similarUnverifiedA control accuracy that drops exactly to zero rather than showing a gradual gap typically indicates a systematic error in the data pipeline, target definition, or training signal, rather than subtle hyperparameter differences.68% similarUnverifiedTRACER achieves the lowest values on both group-level conversion rate error and semantic trajectory distance metrics, and generalizes to out-of-distribution scenarios.67% similar
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