AWhat changed
Learned vocabulary-space query/document expansion with log-saturated weights and explicit sparsity regularization; v2 changed aggregation to max pooling, evaluated document-only expansion and added teacher-distilled hard-negative training.
Scientific scope
Learned vocabulary-space query/document expansion with log-saturated weights and explicit sparsity regularization; v2 changed aggregation to max pooling, evaluated document-only expansion and added teacher-distilled hard-negative training. FLOPS regularization and sparse expansion build on prior work, not invented wholesale here. Efficiency proxies, index storage and actual latency are distinct; offline preprocessing remains. Original preprints contain placeholder conference/DOI fields, not valid publication receipts. BEIR subset evaluations are not full benchmark-wide dominance.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. FLOPS regularization and sparse expansion build on prior work, not invented wholesale here. Efficiency proxies, index storage and actual latency are distinct; offline preprocessing remains. Original preprints contain placeholder conference/DOI fields, not valid publication receipts. BEIR subset evaluations are not full benchmark-wide dominance.
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.
- SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking
- SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval
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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