{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3KVME2A27BHYHMKZFB32MI4DRU","short_pith_number":"pith:3KVME2A2","schema_version":"1.0","canonical_sha256":"daaac2681af84f83b1592877a623838d13bf859bbf0daadf4eb506eecc34e8e1","source":{"kind":"arxiv","id":"2602.07774","version":5},"attestation_state":"computed","paper":{"title":"Generative Reasoning Re-ranker","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Chonglin Sun, Fei Tian, Frank Shyu, Hamed Firooz, Jacob Tao, Jay Xu, Jiang Liu, Kaushik Rangadurai, Kavosh Asadi, Luke Simon, Mengying Sun, Mingfu Liang, Sandeep Pandey, Santanu Kolay, Shike Mei, Shuaiwen Wang, Shuo Gu, Song Yang, Wenlin Chen, Xiaohan Wei, Xi Liu, Yufei Li, Zhijing Li","submitted_at":"2026-02-08T02:12:24Z","abstract_excerpt":"Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge. However, existing work has three key limitations: (1) most efforts focus on retrieval and ranking, while the reranking phase, critical for refining final recommendations, is largely overlooked; (2) LLMs are typically used in zero-shot or supervised fine-tuning settings, leaving their reasoning abilities, especially those enhanced through reinforcement learning (RL) and high-quality reasoning data, underexploited; (3) items are commonly repr"},"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":"2602.07774","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2026-02-08T02:12:24Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8a6e2d2a1638db6083fe9af4fd54f19ef94cf34ded7899deff59983844bf2a2f","abstract_canon_sha256":"11060f9cec2d756fcecf79fb95ef6322891b93df63bf30ae9f296a4e99e02380"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:07:20.204572Z","signature_b64":"6EcSJkeDtVuTQDuGU+j8KeI/Uomr/lZu/NH00UB3pGK787a+H+kvc/Hwy+gSy1C3c14R9VAyO6mXF3wvEW4ZDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"daaac2681af84f83b1592877a623838d13bf859bbf0daadf4eb506eecc34e8e1","last_reissued_at":"2026-06-09T02:07:20.203495Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:07:20.203495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative Reasoning Re-ranker","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Chonglin Sun, Fei Tian, Frank Shyu, Hamed Firooz, Jacob Tao, Jay Xu, Jiang Liu, Kaushik Rangadurai, Kavosh Asadi, Luke Simon, Mengying Sun, Mingfu Liang, Sandeep Pandey, Santanu Kolay, Shike Mei, Shuaiwen Wang, Shuo Gu, Song Yang, Wenlin Chen, Xiaohan Wei, Xi Liu, Yufei Li, Zhijing Li","submitted_at":"2026-02-08T02:12:24Z","abstract_excerpt":"Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge. However, existing work has three key limitations: (1) most efforts focus on retrieval and ranking, while the reranking phase, critical for refining final recommendations, is largely overlooked; (2) LLMs are typically used in zero-shot or supervised fine-tuning settings, leaving their reasoning abilities, especially those enhanced through reinforcement learning (RL) and high-quality reasoning data, underexploited; (3) items are commonly repr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.07774","kind":"arxiv","version":5},"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/2602.07774/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":"2602.07774","created_at":"2026-06-09T02:07:20.203631+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.07774v5","created_at":"2026-06-09T02:07:20.203631+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.07774","created_at":"2026-06-09T02:07:20.203631+00:00"},{"alias_kind":"pith_short_12","alias_value":"3KVME2A27BHY","created_at":"2026-06-09T02:07:20.203631+00:00"},{"alias_kind":"pith_short_16","alias_value":"3KVME2A27BHYHMKZ","created_at":"2026-06-09T02:07:20.203631+00:00"},{"alias_kind":"pith_short_8","alias_value":"3KVME2A2","created_at":"2026-06-09T02:07:20.203631+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2605.23702","citing_title":"TubiFM: Unified Item, Carousel, and Search Ranking for Streaming Discovery","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11553","citing_title":"TwiSTAR:Think Fast, Think Slow, Then Act,Generative Recommendation with Adaptive Reasoning","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3KVME2A27BHYHMKZFB32MI4DRU","json":"https://pith.science/pith/3KVME2A27BHYHMKZFB32MI4DRU.json","graph_json":"https://pith.science/api/pith-number/3KVME2A27BHYHMKZFB32MI4DRU/graph.json","events_json":"https://pith.science/api/pith-number/3KVME2A27BHYHMKZFB32MI4DRU/events.json","paper":"https://pith.science/paper/3KVME2A2"},"agent_actions":{"view_html":"https://pith.science/pith/3KVME2A27BHYHMKZFB32MI4DRU","download_json":"https://pith.science/pith/3KVME2A27BHYHMKZFB32MI4DRU.json","view_paper":"https://pith.science/paper/3KVME2A2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.07774&json=true","fetch_graph":"https://pith.science/api/pith-number/3KVME2A27BHYHMKZFB32MI4DRU/graph.json","fetch_events":"https://pith.science/api/pith-number/3KVME2A27BHYHMKZFB32MI4DRU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3KVME2A27BHYHMKZFB32MI4DRU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3KVME2A27BHYHMKZFB32MI4DRU/action/storage_attestation","attest_author":"https://pith.science/pith/3KVME2A27BHYHMKZFB32MI4DRU/action/author_attestation","sign_citation":"https://pith.science/pith/3KVME2A27BHYHMKZFB32MI4DRU/action/citation_signature","submit_replication":"https://pith.science/pith/3KVME2A27BHYHMKZFB32MI4DRU/action/replication_record"}},"created_at":"2026-06-09T02:07:20.203631+00:00","updated_at":"2026-06-09T02:07:20.203631+00:00"}