{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SR3B3JYACOGO4OH3ZZJXZPLLHB","short_pith_number":"pith:SR3B3JYA","schema_version":"1.0","canonical_sha256":"94761da700138cee38fbce537cbd6b38678f67f81295e6d603c9bcb36b36ae4c","source":{"kind":"arxiv","id":"2505.14432","version":1},"attestation_state":"computed","paper":{"title":"Rank-K: Test-Time Reasoning for Listwise Reranking","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Andrew Yates, Benjamin Van Durme, Dawn Lawrie, Eugene Yang, Kathryn Ricci, Orion Weller, Vivek Chari","submitted_at":"2025-05-20T14:39:34Z","abstract_excerpt":"Retrieve-and-rerank is a popular retrieval pipeline because of its ability to make slow but effective rerankers efficient enough at query time by reducing the number of comparisons. Recent works in neural rerankers take advantage of large language models for their capability in reasoning between queries and passages and have achieved state-of-the-art retrieval effectiveness. However, such rerankers are resource-intensive, even after heavy optimization. In this work, we introduce Rank-K, a listwise passage reranking model that leverages the reasoning capability of the reasoning language model a"},"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":"2505.14432","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2025-05-20T14:39:34Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"cda19ec98304377dc3d23306075dd9cf65ec522b35b8ac210ca8b3c02e15fb47","abstract_canon_sha256":"2c535a6200b45b1d84a99ba03b96c982380a14647906571e97aeef7ebfda8099"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:06:01.247017Z","signature_b64":"CosJA7ktwJyusM3Vod6Y06H7PNNeK1dGnrzavvgJOcsx/2BhlcMS9zWmcvIA5sblgxy58iFdzE9vXHR/OC3SAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94761da700138cee38fbce537cbd6b38678f67f81295e6d603c9bcb36b36ae4c","last_reissued_at":"2026-07-05T11:06:01.246494Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:06:01.246494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rank-K: Test-Time Reasoning for Listwise Reranking","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Andrew Yates, Benjamin Van Durme, Dawn Lawrie, Eugene Yang, Kathryn Ricci, Orion Weller, Vivek Chari","submitted_at":"2025-05-20T14:39:34Z","abstract_excerpt":"Retrieve-and-rerank is a popular retrieval pipeline because of its ability to make slow but effective rerankers efficient enough at query time by reducing the number of comparisons. Recent works in neural rerankers take advantage of large language models for their capability in reasoning between queries and passages and have achieved state-of-the-art retrieval effectiveness. However, such rerankers are resource-intensive, even after heavy optimization. In this work, we introduce Rank-K, a listwise passage reranking model that leverages the reasoning capability of the reasoning language model a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.14432","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/2505.14432/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":"2505.14432","created_at":"2026-07-05T11:06:01.246553+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.14432v1","created_at":"2026-07-05T11:06:01.246553+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.14432","created_at":"2026-07-05T11:06:01.246553+00:00"},{"alias_kind":"pith_short_12","alias_value":"SR3B3JYACOGO","created_at":"2026-07-05T11:06:01.246553+00:00"},{"alias_kind":"pith_short_16","alias_value":"SR3B3JYACOGO4OH3","created_at":"2026-07-05T11:06:01.246553+00:00"},{"alias_kind":"pith_short_8","alias_value":"SR3B3JYA","created_at":"2026-07-05T11:06:01.246553+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.02387","citing_title":"Bringing Agentic Search to Earth Observation Data Discovery","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20683","citing_title":"Layer-wise Token Compression for Efficient Document Reranking","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20683","citing_title":"Layer-wise Token Compression for Efficient Document Reranking","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18767","citing_title":"DualView: Adaptive Local-Global Fusion for Multi-Hop Document Reranking","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2511.11653","citing_title":"GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00063","citing_title":"A Survey of Reasoning-Intensive Retrieval: Progress and Challenges","ref_index":81,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SR3B3JYACOGO4OH3ZZJXZPLLHB","json":"https://pith.science/pith/SR3B3JYACOGO4OH3ZZJXZPLLHB.json","graph_json":"https://pith.science/api/pith-number/SR3B3JYACOGO4OH3ZZJXZPLLHB/graph.json","events_json":"https://pith.science/api/pith-number/SR3B3JYACOGO4OH3ZZJXZPLLHB/events.json","paper":"https://pith.science/paper/SR3B3JYA"},"agent_actions":{"view_html":"https://pith.science/pith/SR3B3JYACOGO4OH3ZZJXZPLLHB","download_json":"https://pith.science/pith/SR3B3JYACOGO4OH3ZZJXZPLLHB.json","view_paper":"https://pith.science/paper/SR3B3JYA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.14432&json=true","fetch_graph":"https://pith.science/api/pith-number/SR3B3JYACOGO4OH3ZZJXZPLLHB/graph.json","fetch_events":"https://pith.science/api/pith-number/SR3B3JYACOGO4OH3ZZJXZPLLHB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SR3B3JYACOGO4OH3ZZJXZPLLHB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SR3B3JYACOGO4OH3ZZJXZPLLHB/action/storage_attestation","attest_author":"https://pith.science/pith/SR3B3JYACOGO4OH3ZZJXZPLLHB/action/author_attestation","sign_citation":"https://pith.science/pith/SR3B3JYACOGO4OH3ZZJXZPLLHB/action/citation_signature","submit_replication":"https://pith.science/pith/SR3B3JYACOGO4OH3ZZJXZPLLHB/action/replication_record"}},"created_at":"2026-07-05T11:06:01.246553+00:00","updated_at":"2026-07-05T11:06:01.246553+00:00"}