Unverified50% confidenceFactExact time
MIRA reuses only one feature extraction pass and then samples multiple perturbed classifiers around the source classifier to form a local prediction distribution.
1
Sources
50%
Confidence
Long-term
Relevance
11/3/2026
First Seen
Sources
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Related Claims
UnverifiedMIRA does not fit a posterior distribution; instead, it treats the perturbation scale as a local probing radius and uses mutual information and prediction consistency to select the scale for each batch.76% similarUnverifiedInference in classification models is non-autoregressive, requiring only a single forward pass to complete predictions for all positions, making computational efficiency significantly higher than that of generative models.68% similarUnverifiedScoring-based classification has the model compute a logit or similarity score for each candidate answer individually, which is normalized via softmax to obtain a probability distribution; inference typically requires only a single forward computation, without autoregressive token-by-token decoding.68% similarUnverifiedClassification predicts discrete class labels, while regression predicts continuous numerical values.67% similarVerifiedThe inherently stochastic sampling of diffusion models means that each generation result differs even given the same prompt.65% similar
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