{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AARYYN4T3ARB3MGI5FNC37LTQ3","short_pith_number":"pith:AARYYN4T","schema_version":"1.0","canonical_sha256":"00238c3793d8221db0c8e95a2dfd7386ed2130bf43a6bac7607863928dc8f210","source":{"kind":"arxiv","id":"2607.26500","version":1},"attestation_state":"computed","paper":{"title":"Multi-Decoder OneRec: Controllable Generative Retrieval for Multi-Objective Industrial Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"GuoPing Tang, Jian Liang, Jing Liu, Liyin Hong, Naifu Zhou, Ruiming Tang, Shuo Su, Wei Jiang, Wenwu Ou, Xiao Lv, Xiaoyou Zhou, Yiqing Yang, You Wang, Zhao Liu","submitted_at":"2026-07-29T05:54:19Z","abstract_excerpt":"Industrial recommender systems build candidate pools by assigning explicit quotas to objective-specific retrieval routes. This design offers quota control but increasingly fragments modeling, training, and serving as the route set grows. Semantic-ID-based generative retrieval provides a unified alternative, yet a single decoder entangles objective policies and limits candidate complementarity. We propose Multi-Decoder OneRec, a controllable framework that combines shared representations, isolated objective adaptation, and coordinated decoding. All objectives share a user-context module and the"},"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":"2607.26500","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-07-29T05:54:19Z","cross_cats_sorted":[],"title_canon_sha256":"c8e542d4336dc516f6a5e2365030d11dd7cacba42c8704e9e90747255e07eacd","abstract_canon_sha256":"95bc9a772282e936ef9e49572ba9f764d5fb7b6c414a603654ae8b5a36b0037d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00238c3793d8221db0c8e95a2dfd7386ed2130bf43a6bac7607863928dc8f210","last_reissued_at":"2026-07-30T01:20:41.364776Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-30T01:20:41.364776Z"},"graph_snapshot":{"paper":{"title":"Multi-Decoder OneRec: Controllable Generative Retrieval for Multi-Objective Industrial Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"GuoPing Tang, Jian Liang, Jing Liu, Liyin Hong, Naifu Zhou, Ruiming Tang, Shuo Su, Wei Jiang, Wenwu Ou, Xiao Lv, Xiaoyou Zhou, Yiqing Yang, You Wang, Zhao Liu","submitted_at":"2026-07-29T05:54:19Z","abstract_excerpt":"Industrial recommender systems build candidate pools by assigning explicit quotas to objective-specific retrieval routes. This design offers quota control but increasingly fragments modeling, training, and serving as the route set grows. Semantic-ID-based generative retrieval provides a unified alternative, yet a single decoder entangles objective policies and limits candidate complementarity. We propose Multi-Decoder OneRec, a controllable framework that combines shared representations, isolated objective adaptation, and coordinated decoding. All objectives share a user-context module and the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.26500","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/2607.26500/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":"2607.26500","created_at":"2026-07-30T01:20:41.370579+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.26500v1","created_at":"2026-07-30T01:20:41.370579+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.26500","created_at":"2026-07-30T01:20:41.370579+00:00"},{"alias_kind":"pith_short_12","alias_value":"AARYYN4T3ARB","created_at":"2026-07-30T01:20:41.370579+00:00"},{"alias_kind":"pith_short_16","alias_value":"AARYYN4T3ARB3MGI","created_at":"2026-07-30T01:20:41.370579+00:00"},{"alias_kind":"pith_short_8","alias_value":"AARYYN4T","created_at":"2026-07-30T01:20:41.370579+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/AARYYN4T3ARB3MGI5FNC37LTQ3","json":"https://pith.science/pith/AARYYN4T3ARB3MGI5FNC37LTQ3.json","graph_json":"https://pith.science/api/pith-number/AARYYN4T3ARB3MGI5FNC37LTQ3/graph.json","events_json":"https://pith.science/api/pith-number/AARYYN4T3ARB3MGI5FNC37LTQ3/events.json","paper":"https://pith.science/paper/AARYYN4T"},"agent_actions":{"view_html":"https://pith.science/pith/AARYYN4T3ARB3MGI5FNC37LTQ3","download_json":"https://pith.science/pith/AARYYN4T3ARB3MGI5FNC37LTQ3.json","view_paper":"https://pith.science/paper/AARYYN4T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.26500&json=true","fetch_graph":"https://pith.science/api/pith-number/AARYYN4T3ARB3MGI5FNC37LTQ3/graph.json","fetch_events":"https://pith.science/api/pith-number/AARYYN4T3ARB3MGI5FNC37LTQ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AARYYN4T3ARB3MGI5FNC37LTQ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AARYYN4T3ARB3MGI5FNC37LTQ3/action/storage_attestation","attest_author":"https://pith.science/pith/AARYYN4T3ARB3MGI5FNC37LTQ3/action/author_attestation","sign_citation":"https://pith.science/pith/AARYYN4T3ARB3MGI5FNC37LTQ3/action/citation_signature","submit_replication":"https://pith.science/pith/AARYYN4T3ARB3MGI5FNC37LTQ3/action/replication_record"}},"created_at":"2026-07-30T01:20:41.370579+00:00","updated_at":"2026-07-30T01:20:41.370579+00:00"}