{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7FASFOL7BSGY2HXOFSXATUM6FX","short_pith_number":"pith:7FASFOL7","schema_version":"1.0","canonical_sha256":"f94122b97f0c8d8d1eee2cae09d19e2de76a3e263fe8548eb466b131d6c444c2","source":{"kind":"arxiv","id":"2311.07601","version":4},"attestation_state":"computed","paper":{"title":"Online Advertisements with LLMs: Opportunities and Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CY","authors_text":"Keivan Rezaei, MohammadTaghi Hajiaghayi, Soheil Feizi, Suho Shin","submitted_at":"2023-11-11T02:13:32Z","abstract_excerpt":"This paper explores the potential for leveraging Large Language Models (LLM) in the realm of online advertising systems. We introduce a general framework for LLM advertisement, consisting of modification, bidding, prediction, and auction modules. Different design considerations for each module are presented. These design choices are evaluated and discussed based on essential desiderata required to maintain a sustainable system. Further fundamental questions regarding practicality, efficiency, and implementation challenges are raised for future research. Finally, we exposit how recent approache"},"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":"2311.07601","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2023-11-11T02:13:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ea2016b41a27470049caaf87714281d098b2c3278b43ca68b9e15d9f5791ab51","abstract_canon_sha256":"fe9ce576277189c88d1b73e6d06730107e961b8b8e5bcb9c6562eb3d2d1a4238"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:04:30.334408Z","signature_b64":"sqygVBJ5LSgWXO6eneZINfETpoJZ9FjJvSpVlD3MTBBgLj1D360fv/fuUVRYxDYYU99aT4jYHyeEzAOFlgbYBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f94122b97f0c8d8d1eee2cae09d19e2de76a3e263fe8548eb466b131d6c444c2","last_reissued_at":"2026-07-05T09:04:30.333821Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:04:30.333821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online Advertisements with LLMs: Opportunities and Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CY","authors_text":"Keivan Rezaei, MohammadTaghi Hajiaghayi, Soheil Feizi, Suho Shin","submitted_at":"2023-11-11T02:13:32Z","abstract_excerpt":"This paper explores the potential for leveraging Large Language Models (LLM) in the realm of online advertising systems. We introduce a general framework for LLM advertisement, consisting of modification, bidding, prediction, and auction modules. Different design considerations for each module are presented. These design choices are evaluated and discussed based on essential desiderata required to maintain a sustainable system. Further fundamental questions regarding practicality, efficiency, and implementation challenges are raised for future research. Finally, we exposit how recent approache"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.07601","kind":"arxiv","version":4},"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/2311.07601/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":"2311.07601","created_at":"2026-07-05T09:04:30.333902+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.07601v4","created_at":"2026-07-05T09:04:30.333902+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.07601","created_at":"2026-07-05T09:04:30.333902+00:00"},{"alias_kind":"pith_short_12","alias_value":"7FASFOL7BSGY","created_at":"2026-07-05T09:04:30.333902+00:00"},{"alias_kind":"pith_short_16","alias_value":"7FASFOL7BSGY2HXO","created_at":"2026-07-05T09:04:30.333902+00:00"},{"alias_kind":"pith_short_8","alias_value":"7FASFOL7","created_at":"2026-07-05T09:04:30.333902+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09151","citing_title":"Customization under Fire: Plugin Poisoning in Text-to-Image Ecosystem","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2512.05958","citing_title":"MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18673","citing_title":"Generative AI Advertising as a Problem of Trustworthy Commercial Intervention","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16474","citing_title":"LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10964","citing_title":"Mechanism Design for Quality-Preserving LLM Advertising","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09918","citing_title":"NaiAD: Initiate Data-Driven Research for LLM Advertising","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7FASFOL7BSGY2HXOFSXATUM6FX","json":"https://pith.science/pith/7FASFOL7BSGY2HXOFSXATUM6FX.json","graph_json":"https://pith.science/api/pith-number/7FASFOL7BSGY2HXOFSXATUM6FX/graph.json","events_json":"https://pith.science/api/pith-number/7FASFOL7BSGY2HXOFSXATUM6FX/events.json","paper":"https://pith.science/paper/7FASFOL7"},"agent_actions":{"view_html":"https://pith.science/pith/7FASFOL7BSGY2HXOFSXATUM6FX","download_json":"https://pith.science/pith/7FASFOL7BSGY2HXOFSXATUM6FX.json","view_paper":"https://pith.science/paper/7FASFOL7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.07601&json=true","fetch_graph":"https://pith.science/api/pith-number/7FASFOL7BSGY2HXOFSXATUM6FX/graph.json","fetch_events":"https://pith.science/api/pith-number/7FASFOL7BSGY2HXOFSXATUM6FX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7FASFOL7BSGY2HXOFSXATUM6FX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7FASFOL7BSGY2HXOFSXATUM6FX/action/storage_attestation","attest_author":"https://pith.science/pith/7FASFOL7BSGY2HXOFSXATUM6FX/action/author_attestation","sign_citation":"https://pith.science/pith/7FASFOL7BSGY2HXOFSXATUM6FX/action/citation_signature","submit_replication":"https://pith.science/pith/7FASFOL7BSGY2HXOFSXATUM6FX/action/replication_record"}},"created_at":"2026-07-05T09:04:30.333902+00:00","updated_at":"2026-07-05T09:04:30.333902+00:00"}