{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OVV3A63PSC565FIGO3SGIL5E6C","short_pith_number":"pith:OVV3A63P","schema_version":"1.0","canonical_sha256":"756bb07b6f90bbee950676e4642fa4f0a32185932195823a6a5767f6dc9113fa","source":{"kind":"arxiv","id":"2407.10979","version":2},"attestation_state":"computed","paper":{"title":"Diffusion Model-based Incentive Mechanism with Prospect Theory for Edge AIGC Services in 6G IoT","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Changyan Yi, Dusit Niyato, Jiangming Jin, Jiangtian Nie, Jinbo Wen, Xiaohuan Li, Yang Zhang, Yue Zhong","submitted_at":"2024-06-10T15:01:50Z","abstract_excerpt":"The fusion of the Internet of Things (IoT) with Sixth-Generation (6G) technology has significant potential to revolutionize the IoT landscape. With the ultra-reliable and low-latency communication capabilities of 6G, 6G-IoT networks can transmit high-quality and diverse data to enhance edge learning. Artificial Intelligence-Generated Content (AIGC) harnesses advanced AI algorithms to automatically generate various types of content. The emergence of edge AIGC integrates with edge networks, facilitating real-time provision of customized AIGC services by deploying AIGC models on edge devices. How"},"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":"2407.10979","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-06-10T15:01:50Z","cross_cats_sorted":[],"title_canon_sha256":"50ba10ccf57e83aadf4230873c1d0b8f40773e46a664a54c1a843dfe2e31c6d9","abstract_canon_sha256":"f4d7b397ebf778bd51fa7aa322e5c3821b7e073b0b9b924f30b28c600e4e7580"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:48:15.587253Z","signature_b64":"75WQ9uezGhm1EcHHz5aKnTUcezZpdubH34bCxmhvSiNTrvD/KnVdv5uuiuoczN2TkpyiyC0jRwRxnH7rbbfeDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"756bb07b6f90bbee950676e4642fa4f0a32185932195823a6a5767f6dc9113fa","last_reissued_at":"2026-07-05T08:48:15.586821Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:48:15.586821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diffusion Model-based Incentive Mechanism with Prospect Theory for Edge AIGC Services in 6G IoT","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Changyan Yi, Dusit Niyato, Jiangming Jin, Jiangtian Nie, Jinbo Wen, Xiaohuan Li, Yang Zhang, Yue Zhong","submitted_at":"2024-06-10T15:01:50Z","abstract_excerpt":"The fusion of the Internet of Things (IoT) with Sixth-Generation (6G) technology has significant potential to revolutionize the IoT landscape. With the ultra-reliable and low-latency communication capabilities of 6G, 6G-IoT networks can transmit high-quality and diverse data to enhance edge learning. Artificial Intelligence-Generated Content (AIGC) harnesses advanced AI algorithms to automatically generate various types of content. The emergence of edge AIGC integrates with edge networks, facilitating real-time provision of customized AIGC services by deploying AIGC models on edge devices. How"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.10979","kind":"arxiv","version":2},"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/2407.10979/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":"2407.10979","created_at":"2026-07-05T08:48:15.586887+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.10979v2","created_at":"2026-07-05T08:48:15.586887+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.10979","created_at":"2026-07-05T08:48:15.586887+00:00"},{"alias_kind":"pith_short_12","alias_value":"OVV3A63PSC56","created_at":"2026-07-05T08:48:15.586887+00:00"},{"alias_kind":"pith_short_16","alias_value":"OVV3A63PSC565FIG","created_at":"2026-07-05T08:48:15.586887+00:00"},{"alias_kind":"pith_short_8","alias_value":"OVV3A63P","created_at":"2026-07-05T08:48:15.586887+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/OVV3A63PSC565FIGO3SGIL5E6C","json":"https://pith.science/pith/OVV3A63PSC565FIGO3SGIL5E6C.json","graph_json":"https://pith.science/api/pith-number/OVV3A63PSC565FIGO3SGIL5E6C/graph.json","events_json":"https://pith.science/api/pith-number/OVV3A63PSC565FIGO3SGIL5E6C/events.json","paper":"https://pith.science/paper/OVV3A63P"},"agent_actions":{"view_html":"https://pith.science/pith/OVV3A63PSC565FIGO3SGIL5E6C","download_json":"https://pith.science/pith/OVV3A63PSC565FIGO3SGIL5E6C.json","view_paper":"https://pith.science/paper/OVV3A63P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.10979&json=true","fetch_graph":"https://pith.science/api/pith-number/OVV3A63PSC565FIGO3SGIL5E6C/graph.json","fetch_events":"https://pith.science/api/pith-number/OVV3A63PSC565FIGO3SGIL5E6C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OVV3A63PSC565FIGO3SGIL5E6C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OVV3A63PSC565FIGO3SGIL5E6C/action/storage_attestation","attest_author":"https://pith.science/pith/OVV3A63PSC565FIGO3SGIL5E6C/action/author_attestation","sign_citation":"https://pith.science/pith/OVV3A63PSC565FIGO3SGIL5E6C/action/citation_signature","submit_replication":"https://pith.science/pith/OVV3A63PSC565FIGO3SGIL5E6C/action/replication_record"}},"created_at":"2026-07-05T08:48:15.586887+00:00","updated_at":"2026-07-05T08:48:15.586887+00:00"}