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SPARSEUP

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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2026-09-19 / reddit-researcherLinkup releases SPARSEUP, a 149M sparse retriever hitting 56.4 nDCG@10 on BEIR-13 under Apache 2.0Linkup Research published SPARSEUP on Hugging Face on September 19, a learned sparse encoder on a 149M ModernBERT backbone, Apache 2.0, which it calls the strongest public vocabulary-based sparse encoder under 150M on BEIR-13. It uses a logit shift, per-token top-12 vocabulary expansion, and case folding of byte-level BPE variants, and reaches over 97% recall in roughly 380 microseconds per query with a Seismic index. The useful part for RAG builders is the controlled comparison with backbone and data held fixed: LateOn 58.9, DenseOn 57.9, SPARSEUP 56.4, with SPARSEUP winning ArguAna and Touché and losing worst on FiQA.

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BENCHMARKED AGAINST
BEIR-13
SPARSEUP achieves 56.4 nDCG@10 on BEIR-13 benchmark.
Source finding
USES
ModernBERT
SPARSEUP uses a ModernBERT 149M backbone with logit shift and vocabulary expansion.
Source finding
RELEASED
Linkup
Linkup released SPARSEUP, a 149M learned sparse retriever on ModernBERT backbone.
Source finding
RELEASED
Linkup -> SPARSEUP
Linkup released SPARSEUP, a 149M learned sparse retriever on ModernBERT backbone.
Source finding

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