{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SHL7VGPIZYEYMSDU4MAG2PCX4V","short_pith_number":"pith:SHL7VGPI","schema_version":"1.0","canonical_sha256":"91d7fa99e8ce09864874e3006d3c57e54f57b049751a46719b89ec4f0c9a2f32","source":{"kind":"arxiv","id":"2606.10357","version":1},"attestation_state":"computed","paper":{"title":"Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Dongxu Liang, Haohao Qu, Jun Wang, Li Qing, Shijie Wang, Wenqi Fan, Yuxin Chen, Zhou Jindong, Zhuohang Jiang","submitted_at":"2026-06-09T03:13:52Z","abstract_excerpt":"Cross-domain recommendation is a core problem in content-to-e-commerce platforms. Its objective is to leverage user interactions with content to infer potential purchasing intent on the e-commerce side, thereby enhancing conversion rates and commercial value. However, in real industrial scenarios, cross-domain recommendation faces multiple challenges: significant semantic gaps exist between different domains, and user cross-domain behavior sequences are often massive in scale and rich in noise. Although large language models (LLMs) possess powerful semantic understanding and reasoning capabili"},"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":"2606.10357","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-06-09T03:13:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"464f8e6b5340b6bcdd5034db3ddef56d759fa94378073b75e3f2fec9d599a6c7","abstract_canon_sha256":"06425b5d4116f7fc7ce5cd6a447bf1e147b6e25f1dae54d4f8215b171bbb8d0b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:10:13.508772Z","signature_b64":"KNk4GKEkVCvWeWxCMrjoeAtK4amydPXwAEcruVi2uIfkbY5Yli7D0FRyHRiNINHM2gk7sQilHSqh32Mqbj1YDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91d7fa99e8ce09864874e3006d3c57e54f57b049751a46719b89ec4f0c9a2f32","last_reissued_at":"2026-06-10T01:10:13.507872Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:10:13.507872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Dongxu Liang, Haohao Qu, Jun Wang, Li Qing, Shijie Wang, Wenqi Fan, Yuxin Chen, Zhou Jindong, Zhuohang Jiang","submitted_at":"2026-06-09T03:13:52Z","abstract_excerpt":"Cross-domain recommendation is a core problem in content-to-e-commerce platforms. Its objective is to leverage user interactions with content to infer potential purchasing intent on the e-commerce side, thereby enhancing conversion rates and commercial value. However, in real industrial scenarios, cross-domain recommendation faces multiple challenges: significant semantic gaps exist between different domains, and user cross-domain behavior sequences are often massive in scale and rich in noise. Although large language models (LLMs) possess powerful semantic understanding and reasoning capabili"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10357","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/2606.10357/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":"2606.10357","created_at":"2026-06-10T01:10:13.508019+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.10357v1","created_at":"2026-06-10T01:10:13.508019+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10357","created_at":"2026-06-10T01:10:13.508019+00:00"},{"alias_kind":"pith_short_12","alias_value":"SHL7VGPIZYEY","created_at":"2026-06-10T01:10:13.508019+00:00"},{"alias_kind":"pith_short_16","alias_value":"SHL7VGPIZYEYMSDU","created_at":"2026-06-10T01:10:13.508019+00:00"},{"alias_kind":"pith_short_8","alias_value":"SHL7VGPI","created_at":"2026-06-10T01:10:13.508019+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/SHL7VGPIZYEYMSDU4MAG2PCX4V","json":"https://pith.science/pith/SHL7VGPIZYEYMSDU4MAG2PCX4V.json","graph_json":"https://pith.science/api/pith-number/SHL7VGPIZYEYMSDU4MAG2PCX4V/graph.json","events_json":"https://pith.science/api/pith-number/SHL7VGPIZYEYMSDU4MAG2PCX4V/events.json","paper":"https://pith.science/paper/SHL7VGPI"},"agent_actions":{"view_html":"https://pith.science/pith/SHL7VGPIZYEYMSDU4MAG2PCX4V","download_json":"https://pith.science/pith/SHL7VGPIZYEYMSDU4MAG2PCX4V.json","view_paper":"https://pith.science/paper/SHL7VGPI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.10357&json=true","fetch_graph":"https://pith.science/api/pith-number/SHL7VGPIZYEYMSDU4MAG2PCX4V/graph.json","fetch_events":"https://pith.science/api/pith-number/SHL7VGPIZYEYMSDU4MAG2PCX4V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SHL7VGPIZYEYMSDU4MAG2PCX4V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SHL7VGPIZYEYMSDU4MAG2PCX4V/action/storage_attestation","attest_author":"https://pith.science/pith/SHL7VGPIZYEYMSDU4MAG2PCX4V/action/author_attestation","sign_citation":"https://pith.science/pith/SHL7VGPIZYEYMSDU4MAG2PCX4V/action/citation_signature","submit_replication":"https://pith.science/pith/SHL7VGPIZYEYMSDU4MAG2PCX4V/action/replication_record"}},"created_at":"2026-06-10T01:10:13.508019+00:00","updated_at":"2026-06-10T01:10:13.508019+00:00"}