AWhat changed
Trained a scalar neural ranking function from document pairs using logistic score differences and cross-entropy loss, extending backpropagation to pairwise preferences while retaining individual-item scoring at inference.
Scientific scope
Trained a scalar neural ranking function from document pairs using logistic score differences and cross-entropy loss, extending backpropagation to pairwise preferences while retaining individual-item scoring at inference. Pairwise loss does not directly optimize every listwise retrieval metric, nor guarantee global neural optimization. Does not invent gradient descent, pairwise preference learning or logistic models.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Pairwise loss does not directly optimize every listwise retrieval metric, nor guarantee global neural optimization. Does not invent gradient descent, pairwise preference learning or logistic models.
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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