{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ANKCUHTIRPLURKNPLSJ2N6SFRW","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":"24b1f9b43f498a42aca490ff3e6e19e9dd3741bf9e5c8ba02361606e0702c1c6","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-01-19T03:03:27Z","title_canon_sha256":"cffd458b79a3cf1edb3643b40f0b1f3f2c525a473093f888c4872ed1d69be197"},"schema_version":"1.0","source":{"id":"2501.10926","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.10926","created_at":"2026-07-05T10:03:00Z"},{"alias_kind":"arxiv_version","alias_value":"2501.10926v1","created_at":"2026-07-05T10:03:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10926","created_at":"2026-07-05T10:03:00Z"},{"alias_kind":"pith_short_12","alias_value":"ANKCUHTIRPLU","created_at":"2026-07-05T10:03:00Z"},{"alias_kind":"pith_short_16","alias_value":"ANKCUHTIRPLURKNP","created_at":"2026-07-05T10:03:00Z"},{"alias_kind":"pith_short_8","alias_value":"ANKCUHTI","created_at":"2026-07-05T10:03:00Z"}],"graph_snapshots":[{"event_id":"sha256:2d6803d3bac497c40f541d0616c931945b48ffcb2fa7948fad81d65898b89f9d","target":"graph","created_at":"2026-07-05T10:03:00Z","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/2501.10926/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Differing from the conventional communication system paradigm that models information source as a sequence of (i.i.d. or stationary) random variables, the semantic approach aims at extracting and sending the high-level features of the content deeply contained in the source, thereby breaking the performance limits from the statistical information theory. As a pioneering work in this area, the deep learning-enabled semantic communication (DeepSC) constitutes a novel algorithmic framework based on the transformer--which is a deep learning tool widely used to process text numerically. The main goa","authors_text":"Kaiming Shen, Mingxiao Li, Shuguang Cui","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-01-19T03:03:27Z","title":"A Semantic Approach to Successive Interference Cancellation for Multiple Access Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10926","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":"sha256:59cf286c93ee2e9db5399a7ffe3b199f40a53c5881a4ba015c1150a6e8f0d191","target":"record","created_at":"2026-07-05T10:03:00Z","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":"24b1f9b43f498a42aca490ff3e6e19e9dd3741bf9e5c8ba02361606e0702c1c6","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-01-19T03:03:27Z","title_canon_sha256":"cffd458b79a3cf1edb3643b40f0b1f3f2c525a473093f888c4872ed1d69be197"},"schema_version":"1.0","source":{"id":"2501.10926","kind":"arxiv","version":1}},"canonical_sha256":"03542a1e688bd748a9af5c93a6fa458da47b4730692a511bb5a70b36e2352996","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"03542a1e688bd748a9af5c93a6fa458da47b4730692a511bb5a70b36e2352996","first_computed_at":"2026-07-05T10:03:00.843445Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:03:00.843445Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lOP6orhs9zOFxypOwfnPx/VNAB83zlU0TsKHcjFCk+lEB75/9tfjG75rJ3OhyIH5c2rChp6qjFnPAh9hBLYLAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:03:00.844025Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.10926","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:59cf286c93ee2e9db5399a7ffe3b199f40a53c5881a4ba015c1150a6e8f0d191","sha256:2d6803d3bac497c40f541d0616c931945b48ffcb2fa7948fad81d65898b89f9d"],"state_sha256":"800fb376cca89d91ccd2c66e4d44947eab15cea169672319cb0d26f08fc9f4a4"}