{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:T6TR3JUVOTFXW6SEWW7OKB3ULO","short_pith_number":"pith:T6TR3JUV","schema_version":"1.0","canonical_sha256":"9fa71da69574cb7b7a44b5bee507745b98025540e32f4ee4d05374031cae2b83","source":{"kind":"arxiv","id":"2608.08809","version":1},"attestation_state":"computed","paper":{"title":"Tevatron-Elastic: A Unified Abstraction for Training Elastic Retrievers and Rerankers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jimmy Lin, Shengyao Zhuang, Vivek Srikumar, Xueguang Ma, Yu Wang, Zhichao Xu, Zongyu Wu","submitted_at":"2026-08-09T16:45:28Z","abstract_excerpt":"A single model scale challenges the flexibility of a production retrieval system: some settings need it faster, others need a smaller index, and the right trade-off changes with the workload. In the context of information retrieval (IR), a transformer-based model can be made smaller in three ways---using fewer layers, passing fewer tokens through the upper layers, or producing a shorter embedding---and each way saves a different compute resource. These options have been studied one at a time, each as its own method with its own code and training setup, which makes them hard to combine or adapt"},"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":"2608.08809","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-08-09T16:45:28Z","cross_cats_sorted":[],"title_canon_sha256":"956f0f5bb7f16c57a8db2f85172837bcd0b96e48a3f301dcfcc0087aa889444b","abstract_canon_sha256":"45525d5fd9287c54d78b667983c105559cfea0661e11fd4140c04a2a275389ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-11T01:25:24.236547Z","signature_b64":"3EmtCTyjN8tN0vXlJY6MiZFIivXX43XEHpVrLuVOO/zhfT0qLH89v52qjZky/wpoPMx7Ad83EB/n3ZPI43ZOAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9fa71da69574cb7b7a44b5bee507745b98025540e32f4ee4d05374031cae2b83","last_reissued_at":"2026-08-11T01:25:24.234003Z","signature_status":"signed_v1","first_computed_at":"2026-08-11T01:25:24.234003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tevatron-Elastic: A Unified Abstraction for Training Elastic Retrievers and Rerankers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jimmy Lin, Shengyao Zhuang, Vivek Srikumar, Xueguang Ma, Yu Wang, Zhichao Xu, Zongyu Wu","submitted_at":"2026-08-09T16:45:28Z","abstract_excerpt":"A single model scale challenges the flexibility of a production retrieval system: some settings need it faster, others need a smaller index, and the right trade-off changes with the workload. In the context of information retrieval (IR), a transformer-based model can be made smaller in three ways---using fewer layers, passing fewer tokens through the upper layers, or producing a shorter embedding---and each way saves a different compute resource. These options have been studied one at a time, each as its own method with its own code and training setup, which makes them hard to combine or adapt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.08809","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/2608.08809/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":"2608.08809","created_at":"2026-08-11T01:25:24.234855+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.08809v1","created_at":"2026-08-11T01:25:24.234855+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.08809","created_at":"2026-08-11T01:25:24.234855+00:00"},{"alias_kind":"pith_short_12","alias_value":"T6TR3JUVOTFX","created_at":"2026-08-11T01:25:24.234855+00:00"},{"alias_kind":"pith_short_16","alias_value":"T6TR3JUVOTFXW6SE","created_at":"2026-08-11T01:25:24.234855+00:00"},{"alias_kind":"pith_short_8","alias_value":"T6TR3JUV","created_at":"2026-08-11T01:25:24.234855+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T6TR3JUVOTFXW6SEWW7OKB3ULO","json":"https://pith.science/pith/T6TR3JUVOTFXW6SEWW7OKB3ULO.json","graph_json":"https://pith.science/api/pith-number/T6TR3JUVOTFXW6SEWW7OKB3ULO/graph.json","events_json":"https://pith.science/api/pith-number/T6TR3JUVOTFXW6SEWW7OKB3ULO/events.json","paper":"https://pith.science/paper/T6TR3JUV"},"agent_actions":{"view_html":"https://pith.science/pith/T6TR3JUVOTFXW6SEWW7OKB3ULO","download_json":"https://pith.science/pith/T6TR3JUVOTFXW6SEWW7OKB3ULO.json","view_paper":"https://pith.science/paper/T6TR3JUV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.08809&json=true","fetch_graph":"https://pith.science/api/pith-number/T6TR3JUVOTFXW6SEWW7OKB3ULO/graph.json","fetch_events":"https://pith.science/api/pith-number/T6TR3JUVOTFXW6SEWW7OKB3ULO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T6TR3JUVOTFXW6SEWW7OKB3ULO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T6TR3JUVOTFXW6SEWW7OKB3ULO/action/storage_attestation","attest_author":"https://pith.science/pith/T6TR3JUVOTFXW6SEWW7OKB3ULO/action/author_attestation","sign_citation":"https://pith.science/pith/T6TR3JUVOTFXW6SEWW7OKB3ULO/action/citation_signature","submit_replication":"https://pith.science/pith/T6TR3JUVOTFXW6SEWW7OKB3ULO/action/replication_record"}},"created_at":"2026-08-11T01:25:24.234855+00:00","updated_at":"2026-08-11T01:25:24.234855+00:00"}