{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:U5JVNL4643DLPE4ZBZPATU6C2A","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"267c0ac3fa9bd02d64be6f554211ec8b777515d2379fcb1701737e1c11a8df4b","cross_cats_sorted":["math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2025-09-11T14:37:40Z","title_canon_sha256":"350052932dacc92055c00a69a7f0c0dead7400773b649edf64e8ccbfdce6437d"},"schema_version":"1.0","source":{"id":"2509.09499","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.09499","created_at":"2026-07-05T12:09:37Z"},{"alias_kind":"arxiv_version","alias_value":"2509.09499v1","created_at":"2026-07-05T12:09:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09499","created_at":"2026-07-05T12:09:37Z"},{"alias_kind":"pith_short_12","alias_value":"U5JVNL4643DL","created_at":"2026-07-05T12:09:37Z"},{"alias_kind":"pith_short_16","alias_value":"U5JVNL4643DLPE4Z","created_at":"2026-07-05T12:09:37Z"},{"alias_kind":"pith_short_8","alias_value":"U5JVNL46","created_at":"2026-07-05T12:09:37Z"}],"graph_snapshots":[{"event_id":"sha256:b07ae4a6cf8a0b488b1752849f6a5fd20dd88a860702bce34ff9927ff85f45c1","target":"graph","created_at":"2026-07-05T12:09:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2509.09499/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose a mixture of semantics (MoS) transmission strategy for wireless semantic communication systems based on generative artificial intelligence (AI). At the transmitter, we divide an image into regions of interest (ROI) and reigons of non-interest (RONI) to extract their semantic information respectively. Semantic information of ROI can be allocated more bandwidth, while RONI can be represented in a compact form for transmission. At the receiver, a diffusion model reconstructs the full image using the received semantic information of ROI and RONI. Compared to existing gene","authors_text":"Junjie Ni, Meixia Tao, Tong Wu, Wenjun Zhang, Yin Xu, Zhiyong Chen","cross_cats":["math.IT"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2025-09-11T14:37:40Z","title":"Mixture of Semantics Transmission for Generative AI-Enabled Semantic Communication Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09499","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:99ce8b6e2b2036c7b5f59beed376ca5619a19eb99cd5b2d70477263e639bdc85","target":"record","created_at":"2026-07-05T12:09:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"267c0ac3fa9bd02d64be6f554211ec8b777515d2379fcb1701737e1c11a8df4b","cross_cats_sorted":["math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2025-09-11T14:37:40Z","title_canon_sha256":"350052932dacc92055c00a69a7f0c0dead7400773b649edf64e8ccbfdce6437d"},"schema_version":"1.0","source":{"id":"2509.09499","kind":"arxiv","version":1}},"canonical_sha256":"a75356af9ee6c6b793990e5e09d3c2d032d2993c8ec84344ff8a8470951eb53a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a75356af9ee6c6b793990e5e09d3c2d032d2993c8ec84344ff8a8470951eb53a","first_computed_at":"2026-07-05T12:09:37.097131Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:09:37.097131Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"o/4yFYOLY0y6Ridpu0jjgl72dUD9pDQqKfXPeXcIk09hLlx/JXaFtHK5LIm2/jMICyzWGMqKVsPIQ1jRr5cJAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T12:09:37.097577Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.09499","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:99ce8b6e2b2036c7b5f59beed376ca5619a19eb99cd5b2d70477263e639bdc85","sha256:b07ae4a6cf8a0b488b1752849f6a5fd20dd88a860702bce34ff9927ff85f45c1"],"state_sha256":"b759949a09c376d1ec64c6a6e933eb1675714a2e4e9269e33ebaf2247540873e"}