AWhat changed
Specified document-score update signals using ranked positions and target retrieval measures, trained differentiable ranking models from these implicit-gradient rules and factored pairwise training contributions.
Scientific scope
Specified document-score update signals using ranked positions and target retrieval measures, trained differentiable ranking models from these implicit-gradient rules and factored pairwise training contributions. Integrability and convexity require stated regularity, symmetric Jacobian and appropriate Hessian sign conditions; arbitrary rank-dependent lambda rules are not automatically gradients of a smooth global objective. Empirical metric optimization is not a theorem of global NDCG optimality; Poincare's lemma is inherited, not a new theorem.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Integrability and convexity require stated regularity, symmetric Jacobian and appropriate Hessian sign conditions; arbitrary rank-dependent lambda rules are not automatically gradients of a smooth global objective. Empirical metric optimization is not a theorem of global NDCG optimality; Poincare's lemma is inherited, not a new theorem.
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.
Full scientific record · Browse contribution grades · Request a correction