{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YACCISXVIZHF3PLYKYKRJGKVSJ","short_pith_number":"pith:YACCISXV","schema_version":"1.0","canonical_sha256":"c004244af5464e5dbd78561514995592625b4911e76c98f3c2c069c96691e89a","source":{"kind":"arxiv","id":"2501.14102","version":1},"attestation_state":"computed","paper":{"title":"5G LDPC Linear Transformer for Channel Decoding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Fernando Pinero, Mario Hernandez","submitted_at":"2025-01-23T21:29:30Z","abstract_excerpt":"This work introduces a novel, fully differentiable linear-time complexity transformer decoder and a transformer decoder to correct 5G New Radio (NR) LDPC. We propose a scalable approach to decode linear block codes with $O(n)$ complexity rather than $O(n^2)$ for regular transformers. The architectures' performances are compared to Belief Propagation (BP), the production-level decoding algorithm used for 5G New Radio (NR) LDPC codes. We achieve bit error rate performance that matches a regular Transformer decoder and surpases one iteration BP, also achieving competitive time performance against"},"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":"2501.14102","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-23T21:29:30Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"1b6ef374cac500b58091a30e7ea8319f90f55118b19b26c1ee3a5e47e6d8f173","abstract_canon_sha256":"9d384a53bc6af5b7730fd67163361b4d8a38ae2a6f78124cb151730a4df15632"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:04:45.456243Z","signature_b64":"vF9TsAvoG2pe7nve9sISUOoFYRHxPL35uuXCfz5Tp2K45/ZdPRI0mk+Gm5OSTmpfBoGCXKpf/qcGSTCcx2/GAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c004244af5464e5dbd78561514995592625b4911e76c98f3c2c069c96691e89a","last_reissued_at":"2026-07-05T10:04:45.455858Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:04:45.455858Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"5G LDPC Linear Transformer for Channel Decoding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Fernando Pinero, Mario Hernandez","submitted_at":"2025-01-23T21:29:30Z","abstract_excerpt":"This work introduces a novel, fully differentiable linear-time complexity transformer decoder and a transformer decoder to correct 5G New Radio (NR) LDPC. We propose a scalable approach to decode linear block codes with $O(n)$ complexity rather than $O(n^2)$ for regular transformers. The architectures' performances are compared to Belief Propagation (BP), the production-level decoding algorithm used for 5G New Radio (NR) LDPC codes. We achieve bit error rate performance that matches a regular Transformer decoder and surpases one iteration BP, also achieving competitive time performance against"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14102","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/2501.14102/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":"2501.14102","created_at":"2026-07-05T10:04:45.455923+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.14102v1","created_at":"2026-07-05T10:04:45.455923+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14102","created_at":"2026-07-05T10:04:45.455923+00:00"},{"alias_kind":"pith_short_12","alias_value":"YACCISXVIZHF","created_at":"2026-07-05T10:04:45.455923+00:00"},{"alias_kind":"pith_short_16","alias_value":"YACCISXVIZHF3PLY","created_at":"2026-07-05T10:04:45.455923+00:00"},{"alias_kind":"pith_short_8","alias_value":"YACCISXV","created_at":"2026-07-05T10:04:45.455923+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/YACCISXVIZHF3PLYKYKRJGKVSJ","json":"https://pith.science/pith/YACCISXVIZHF3PLYKYKRJGKVSJ.json","graph_json":"https://pith.science/api/pith-number/YACCISXVIZHF3PLYKYKRJGKVSJ/graph.json","events_json":"https://pith.science/api/pith-number/YACCISXVIZHF3PLYKYKRJGKVSJ/events.json","paper":"https://pith.science/paper/YACCISXV"},"agent_actions":{"view_html":"https://pith.science/pith/YACCISXVIZHF3PLYKYKRJGKVSJ","download_json":"https://pith.science/pith/YACCISXVIZHF3PLYKYKRJGKVSJ.json","view_paper":"https://pith.science/paper/YACCISXV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.14102&json=true","fetch_graph":"https://pith.science/api/pith-number/YACCISXVIZHF3PLYKYKRJGKVSJ/graph.json","fetch_events":"https://pith.science/api/pith-number/YACCISXVIZHF3PLYKYKRJGKVSJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YACCISXVIZHF3PLYKYKRJGKVSJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YACCISXVIZHF3PLYKYKRJGKVSJ/action/storage_attestation","attest_author":"https://pith.science/pith/YACCISXVIZHF3PLYKYKRJGKVSJ/action/author_attestation","sign_citation":"https://pith.science/pith/YACCISXVIZHF3PLYKYKRJGKVSJ/action/citation_signature","submit_replication":"https://pith.science/pith/YACCISXVIZHF3PLYKYKRJGKVSJ/action/replication_record"}},"created_at":"2026-07-05T10:04:45.455923+00:00","updated_at":"2026-07-05T10:04:45.455923+00:00"}