{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YIEPN4VMK65UCLM4ZP3EUACKBC","short_pith_number":"pith:YIEPN4VM","schema_version":"1.0","canonical_sha256":"c208f6f2ac57bb412d9ccbf64a004a08b7e649330318f080bbb9d325518ddd87","source":{"kind":"arxiv","id":"2411.04403","version":2},"attestation_state":"computed","paper":{"title":"Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Dongyu Ru, Yang Yang, Yiwen Wang, Zhichao Geng","submitted_at":"2024-11-07T03:46:43Z","abstract_excerpt":"Learned sparse retrieval, which can efficiently perform retrieval through mature inverted-index engines, has garnered growing attention in recent years. Particularly, the inference-free sparse retrievers are attractive as they eliminate online model inference in the retrieval phase thereby avoids huge computational cost, offering reasonable throughput and latency. However, even the state-of-the-art (SOTA) inference-free sparse models lag far behind in terms of search relevance when compared to both sparse and dense siamese models. Towards competitive search relevance for inference-free sparse "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.04403","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2024-11-07T03:46:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"81b277f71bfcc19198617d299a3ff763d6c111b187686e8bcc97690503f98310","abstract_canon_sha256":"ff5a0d6d3e667838eeb8f275a3f89d3d175acd5fdd5a6a4a0e68fef254d960da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:51.972442Z","signature_b64":"qX5klM/PyVo4vEgukCY1pUWInqdnioiahvb85cqkuXfO3n3WxRL0FyS33i8TuLRhOQnXjlmrG2ZPqXZ+4hqbAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c208f6f2ac57bb412d9ccbf64a004a08b7e649330318f080bbb9d325518ddd87","last_reissued_at":"2026-07-05T11:29:51.971972Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:51.971972Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Dongyu Ru, Yang Yang, Yiwen Wang, Zhichao Geng","submitted_at":"2024-11-07T03:46:43Z","abstract_excerpt":"Learned sparse retrieval, which can efficiently perform retrieval through mature inverted-index engines, has garnered growing attention in recent years. Particularly, the inference-free sparse retrievers are attractive as they eliminate online model inference in the retrieval phase thereby avoids huge computational cost, offering reasonable throughput and latency. However, even the state-of-the-art (SOTA) inference-free sparse models lag far behind in terms of search relevance when compared to both sparse and dense siamese models. Towards competitive search relevance for inference-free sparse "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.04403","kind":"arxiv","version":2},"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/2411.04403/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.04403","created_at":"2026-07-05T11:29:51.972039+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.04403v2","created_at":"2026-07-05T11:29:51.972039+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.04403","created_at":"2026-07-05T11:29:51.972039+00:00"},{"alias_kind":"pith_short_12","alias_value":"YIEPN4VMK65U","created_at":"2026-07-05T11:29:51.972039+00:00"},{"alias_kind":"pith_short_16","alias_value":"YIEPN4VMK65UCLM4","created_at":"2026-07-05T11:29:51.972039+00:00"},{"alias_kind":"pith_short_8","alias_value":"YIEPN4VM","created_at":"2026-07-05T11:29:51.972039+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.00004","citing_title":"Why Advanced Encoders Lag on Sparse Retrieval? The Answer and an Approach to Bridging Vocabulary Gaps","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"2605.17762","citing_title":"Surface-Form Neural Sparse Retrieval: Robust Fuzzy Matching for Industrial Music Search","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YIEPN4VMK65UCLM4ZP3EUACKBC","json":"https://pith.science/pith/YIEPN4VMK65UCLM4ZP3EUACKBC.json","graph_json":"https://pith.science/api/pith-number/YIEPN4VMK65UCLM4ZP3EUACKBC/graph.json","events_json":"https://pith.science/api/pith-number/YIEPN4VMK65UCLM4ZP3EUACKBC/events.json","paper":"https://pith.science/paper/YIEPN4VM"},"agent_actions":{"view_html":"https://pith.science/pith/YIEPN4VMK65UCLM4ZP3EUACKBC","download_json":"https://pith.science/pith/YIEPN4VMK65UCLM4ZP3EUACKBC.json","view_paper":"https://pith.science/paper/YIEPN4VM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.04403&json=true","fetch_graph":"https://pith.science/api/pith-number/YIEPN4VMK65UCLM4ZP3EUACKBC/graph.json","fetch_events":"https://pith.science/api/pith-number/YIEPN4VMK65UCLM4ZP3EUACKBC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YIEPN4VMK65UCLM4ZP3EUACKBC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YIEPN4VMK65UCLM4ZP3EUACKBC/action/storage_attestation","attest_author":"https://pith.science/pith/YIEPN4VMK65UCLM4ZP3EUACKBC/action/author_attestation","sign_citation":"https://pith.science/pith/YIEPN4VMK65UCLM4ZP3EUACKBC/action/citation_signature","submit_replication":"https://pith.science/pith/YIEPN4VMK65UCLM4ZP3EUACKBC/action/replication_record"}},"created_at":"2026-07-05T11:29:51.972039+00:00","updated_at":"2026-07-05T11:29:51.972039+00:00"}