{"paper":{"title":"Bekko Embedding: Parameter-Efficient Multilingual Retrieval with Ultra-Compact Encoders","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Yuichi Tateno","submitted_at":"2026-07-28T01:13:37Z","abstract_excerpt":"How small can a competitive multilingual retrieval model be? We present Bekko Embedding: its smallest model, bekko-embedding-v1-a8m, has just under 8M Active Parameters (AP) -- the non-embedding parameters that dominate inference compute -- yet on official MMTEB Multilingual v2 Retrieval (nDCG@10) it scores 56.2, above the multilingual-e5 family and BGE-M3 (40x the AP) in our comparison. The higher-quality bekko-embedding-v1-a25m (just under 25M AP) reaches 57.5, on par with gte-multilingual-base, and Multilingual NanoBEIR (14 languages) confirms the trend. Both models handle inputs of up to 8"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.25180","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.25180/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}