BWhat changed
Stacked ensembles of complementary feature-interaction modules with shared feature processing and residual information flow, paired with hybrid sharded training to support deeper click-prediction models.
Scientific scope
Stacked ensembles of complementary feature-interaction modules with shared feature processing and residual information flow, paired with hybrid sharded training to support deeper click-prediction models. Reuses established attention, convolution, DLRM, DCN, FSDP and checkpointing components. Its contribution is their heterogeneous interaction hierarchy and associated training design, not invention of those components. Normalized-entropy improvement is not a revenue or absolute click-through-rate gain; results are empirical.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Reuses established attention, convolution, DLRM, DCN, FSDP and checkpointing components. Its contribution is their heterogeneous interaction hierarchy and associated training design, not invention of those components. Normalized-entropy improvement is not a revenue or absolute click-through-rate gain; results are empirical.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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