Unverified50% confidenceFactExact time
Incremental recalc precisely tracks dependencies by building a cell dependency graph, recalculating only the minimum affected subset.
1
Sources
50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLinear probing baselines show that the model encodes prompt category information at every layer, but truly restructures the computational process in only a small fraction of layers.70% similarUnverifiedSimpleSpread supports incremental recalc, recalculating only affected cells rather than recalculating the entire sheet.68% similarUnverifiedThe perceptron convergence theorem proves that if the data is linearly separable, the algorithm is guaranteed to find the correct classification hyperplane within a finite number of steps65% similarUnverifiedIdentifiability of a causal effect means that the interventional distribution P(Y|do(X)) can be uniquely determined from purely observational data, holds for all parameter assignments compatible with the graph structure, is independent of specific numerical values, and depends only on the graph structure.65% similarUnverifiedCurrent generative models are essentially limited to the association level—identifying statistical co-occurrence patterns in data—and lack causal reasoning capability.64% similar
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