{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FAEEP4FXLBOX47ZAMPIY2HEN7L","short_pith_number":"pith:FAEEP4FX","schema_version":"1.0","canonical_sha256":"280847f0b7585d7e7f2063d18d1c8dfaf39b6185adb2ba3b485738bc0f66f29c","source":{"kind":"arxiv","id":"2509.10061","version":1},"attestation_state":"computed","paper":{"title":"Semantic Rate-Distortion Theory with Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Chuan Zhou, Geoffrey Ye Li, Shuai Yuan, Tong Ye, Yi-Qun Zhao, Zhi-Ming Ma","submitted_at":"2025-09-12T08:48:47Z","abstract_excerpt":"Artificial intelligence (AI) is ushering in a new era for communication. As a result, the establishment of a semantic communication framework is putting on the agenda. Based on a realistic semantic communication model, this paper develops a rate-distortion framework for semantic compression. Different from the existing works primarily focusing on decoder-side estimation of intrinsic meaning and ignoring its inherent issues, such as ambiguity and polysemy, we exploit a constraint of conditional semantic probability distortion to effectively capture the essential features of practical semantic e"},"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":"2509.10061","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-09-12T08:48:47Z","cross_cats_sorted":["eess.SP","math.IT"],"title_canon_sha256":"11d7b93145adf17e2c0228c42816ebb9d3b6dff456b445b2a46b17b6926061e9","abstract_canon_sha256":"c27ea5a31ede980e66ba21f9505bd57713c18ce68f3d9afe28c9f9d1852ca074"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:11:01.747391Z","signature_b64":"N2tA0rNg7MTP/5pXlylY93fmey38NbEYwCuHDP4M3VlPKEG1Ti89j3TSF6T4nsKA2ziZ8c1SJW7DgkcKQ7wJAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"280847f0b7585d7e7f2063d18d1c8dfaf39b6185adb2ba3b485738bc0f66f29c","last_reissued_at":"2026-07-05T12:11:01.746824Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:11:01.746824Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semantic Rate-Distortion Theory with Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Chuan Zhou, Geoffrey Ye Li, Shuai Yuan, Tong Ye, Yi-Qun Zhao, Zhi-Ming Ma","submitted_at":"2025-09-12T08:48:47Z","abstract_excerpt":"Artificial intelligence (AI) is ushering in a new era for communication. As a result, the establishment of a semantic communication framework is putting on the agenda. Based on a realistic semantic communication model, this paper develops a rate-distortion framework for semantic compression. Different from the existing works primarily focusing on decoder-side estimation of intrinsic meaning and ignoring its inherent issues, such as ambiguity and polysemy, we exploit a constraint of conditional semantic probability distortion to effectively capture the essential features of practical semantic e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.10061","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/2509.10061/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":"2509.10061","created_at":"2026-07-05T12:11:01.746886+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.10061v1","created_at":"2026-07-05T12:11:01.746886+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.10061","created_at":"2026-07-05T12:11:01.746886+00:00"},{"alias_kind":"pith_short_12","alias_value":"FAEEP4FXLBOX","created_at":"2026-07-05T12:11:01.746886+00:00"},{"alias_kind":"pith_short_16","alias_value":"FAEEP4FXLBOX47ZA","created_at":"2026-07-05T12:11:01.746886+00:00"},{"alias_kind":"pith_short_8","alias_value":"FAEEP4FX","created_at":"2026-07-05T12:11:01.746886+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.15698","citing_title":"Rate-Distortion Theory for Deductive Sources under Closure Fidelity","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FAEEP4FXLBOX47ZAMPIY2HEN7L","json":"https://pith.science/pith/FAEEP4FXLBOX47ZAMPIY2HEN7L.json","graph_json":"https://pith.science/api/pith-number/FAEEP4FXLBOX47ZAMPIY2HEN7L/graph.json","events_json":"https://pith.science/api/pith-number/FAEEP4FXLBOX47ZAMPIY2HEN7L/events.json","paper":"https://pith.science/paper/FAEEP4FX"},"agent_actions":{"view_html":"https://pith.science/pith/FAEEP4FXLBOX47ZAMPIY2HEN7L","download_json":"https://pith.science/pith/FAEEP4FXLBOX47ZAMPIY2HEN7L.json","view_paper":"https://pith.science/paper/FAEEP4FX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.10061&json=true","fetch_graph":"https://pith.science/api/pith-number/FAEEP4FXLBOX47ZAMPIY2HEN7L/graph.json","fetch_events":"https://pith.science/api/pith-number/FAEEP4FXLBOX47ZAMPIY2HEN7L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FAEEP4FXLBOX47ZAMPIY2HEN7L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FAEEP4FXLBOX47ZAMPIY2HEN7L/action/storage_attestation","attest_author":"https://pith.science/pith/FAEEP4FXLBOX47ZAMPIY2HEN7L/action/author_attestation","sign_citation":"https://pith.science/pith/FAEEP4FXLBOX47ZAMPIY2HEN7L/action/citation_signature","submit_replication":"https://pith.science/pith/FAEEP4FXLBOX47ZAMPIY2HEN7L/action/replication_record"}},"created_at":"2026-07-05T12:11:01.746886+00:00","updated_at":"2026-07-05T12:11:01.746886+00:00"}