{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6ROQFBNYLOXBOCOJ5PCGHCSLPO","short_pith_number":"pith:6ROQFBNY","schema_version":"1.0","canonical_sha256":"f45d0285b85bae1709c9ebc4638a4b7b95c98ae0d352aeec767c0812d7e159bf","source":{"kind":"arxiv","id":"2505.17549","version":3},"attestation_state":"computed","paper":{"title":"EGA-V2: An End-to-end Generative Framework for Industrial Advertising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Fan Yang, Jiangke Fan, Teng Zhang, Xingxing Wang, Yongkang Wang, Ze Wang, Zuowu Zheng","submitted_at":"2025-05-23T06:55:02Z","abstract_excerpt":"Traditional online industrial advertising systems suffer from the limitations of multi-stage cascaded architectures, which often discard high-potential candidates prematurely and distribute decision logic across disconnected modules. While recent generative recommendation approaches provide end-to-end solutions, they fail to address critical advertising requirements of key components for real-world deployment, such as explicit bidding, creative selection, ad allocation, and payment computation. To bridge this gap, we introduce End-to-End Generative Advertising (EGA-V2), the first unified frame"},"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":"2505.17549","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-05-23T06:55:02Z","cross_cats_sorted":[],"title_canon_sha256":"a4b8f5aa27f631c57350ce608ec2808ad34e20b99c684fc64bee84624b5e17b4","abstract_canon_sha256":"7bea06792c3f1931b187ee46c6db88295a3e3d0a388fd87a1479f350aff11fe4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:26.440431Z","signature_b64":"FQzNvJ7ss++uKjzFyze+y0N4YBAriXsptqSZe4BJ2ePCnCuTTK98otC8tbfGA7wvQlSBG4+ISh/jyUvtGDyyBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f45d0285b85bae1709c9ebc4638a4b7b95c98ae0d352aeec767c0812d7e159bf","last_reissued_at":"2026-07-05T11:13:26.439992Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:26.439992Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EGA-V2: An End-to-end Generative Framework for Industrial Advertising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Fan Yang, Jiangke Fan, Teng Zhang, Xingxing Wang, Yongkang Wang, Ze Wang, Zuowu Zheng","submitted_at":"2025-05-23T06:55:02Z","abstract_excerpt":"Traditional online industrial advertising systems suffer from the limitations of multi-stage cascaded architectures, which often discard high-potential candidates prematurely and distribute decision logic across disconnected modules. While recent generative recommendation approaches provide end-to-end solutions, they fail to address critical advertising requirements of key components for real-world deployment, such as explicit bidding, creative selection, ad allocation, and payment computation. To bridge this gap, we introduce End-to-End Generative Advertising (EGA-V2), the first unified frame"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.17549","kind":"arxiv","version":3},"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/2505.17549/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":"2505.17549","created_at":"2026-07-05T11:13:26.440046+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.17549v3","created_at":"2026-07-05T11:13:26.440046+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.17549","created_at":"2026-07-05T11:13:26.440046+00:00"},{"alias_kind":"pith_short_12","alias_value":"6ROQFBNYLOXB","created_at":"2026-07-05T11:13:26.440046+00:00"},{"alias_kind":"pith_short_16","alias_value":"6ROQFBNYLOXBOCOJ","created_at":"2026-07-05T11:13:26.440046+00:00"},{"alias_kind":"pith_short_8","alias_value":"6ROQFBNY","created_at":"2026-07-05T11:13:26.440046+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.13533","citing_title":"OneRetrieval: Unifying Multi-Branch E-commerce Retrieval with an Editable Generative Model","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06970","citing_title":"SSRLive: Live Streaming Recommendation with Dynamic Semantic ID","ref_index":51,"is_internal_anchor":false},{"citing_arxiv_id":"2510.27157","citing_title":"A Survey on Generative Recommendation: Data, Model, and Tasks","ref_index":243,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02684","citing_title":"MBGR: Multi-Business Prediction for Generative Recommendation at Meituan","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05803","citing_title":"UniVA: Unified Value Alignment for Generative Recommendation in Online Advertising at Tencent","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05329","citing_title":"Semantic Trimming and Auxiliary Multi-step Prediction for Generative Recommendation","ref_index":62,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6ROQFBNYLOXBOCOJ5PCGHCSLPO","json":"https://pith.science/pith/6ROQFBNYLOXBOCOJ5PCGHCSLPO.json","graph_json":"https://pith.science/api/pith-number/6ROQFBNYLOXBOCOJ5PCGHCSLPO/graph.json","events_json":"https://pith.science/api/pith-number/6ROQFBNYLOXBOCOJ5PCGHCSLPO/events.json","paper":"https://pith.science/paper/6ROQFBNY"},"agent_actions":{"view_html":"https://pith.science/pith/6ROQFBNYLOXBOCOJ5PCGHCSLPO","download_json":"https://pith.science/pith/6ROQFBNYLOXBOCOJ5PCGHCSLPO.json","view_paper":"https://pith.science/paper/6ROQFBNY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.17549&json=true","fetch_graph":"https://pith.science/api/pith-number/6ROQFBNYLOXBOCOJ5PCGHCSLPO/graph.json","fetch_events":"https://pith.science/api/pith-number/6ROQFBNYLOXBOCOJ5PCGHCSLPO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6ROQFBNYLOXBOCOJ5PCGHCSLPO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6ROQFBNYLOXBOCOJ5PCGHCSLPO/action/storage_attestation","attest_author":"https://pith.science/pith/6ROQFBNYLOXBOCOJ5PCGHCSLPO/action/author_attestation","sign_citation":"https://pith.science/pith/6ROQFBNYLOXBOCOJ5PCGHCSLPO/action/citation_signature","submit_replication":"https://pith.science/pith/6ROQFBNYLOXBOCOJ5PCGHCSLPO/action/replication_record"}},"created_at":"2026-07-05T11:13:26.440046+00:00","updated_at":"2026-07-05T11:13:26.440046+00:00"}