{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JOAWZ4U6MF3ZW6XKJYNFROQPWP","short_pith_number":"pith:JOAWZ4U6","schema_version":"1.0","canonical_sha256":"4b816cf29e61779b7aea4e1a58ba0fb3f543ae41305f4ba85193217dd0170b1f","source":{"kind":"arxiv","id":"2405.04050","version":1},"attestation_state":"computed","paper":{"title":"Learning Linear Block Error Correction Codes","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","math.IT"],"primary_cat":"cs.IT","authors_text":"Lior Wolf, Yoni Choukroun","submitted_at":"2024-05-07T06:47:12Z","abstract_excerpt":"Error correction codes are a crucial part of the physical communication layer, ensuring the reliable transfer of data over noisy channels. The design of optimal linear block codes capable of being efficiently decoded is of major concern, especially for short block lengths. While neural decoders have recently demonstrated their advantage over classical decoding techniques, the neural design of the codes remains a challenge. In this work, we propose for the first time a unified encoder-decoder training of binary linear block codes. To this end, we adapt the coding setting to support efficient an"},"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":"2405.04050","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IT","submitted_at":"2024-05-07T06:47:12Z","cross_cats_sorted":["cs.AI","math.IT"],"title_canon_sha256":"709e4e9ec308450a92e9e6f6b1451c1038697dc08f2d798c0af1a1eeba690db6","abstract_canon_sha256":"88d4adc1ef1f8c842b51d6a66dfe7e5416a3e14b6510aeeb055058f3081883fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:16:31.164940Z","signature_b64":"AAfxe/GbYH0lOlcylEh+lVk7CmWXJrEkoR9SHGElW1mYC6Icdf9//8d8a5pc/qZTxuVXdXulSiglR/gKsjaVAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b816cf29e61779b7aea4e1a58ba0fb3f543ae41305f4ba85193217dd0170b1f","last_reissued_at":"2026-07-05T08:16:31.164597Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:16:31.164597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Linear Block Error Correction Codes","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","math.IT"],"primary_cat":"cs.IT","authors_text":"Lior Wolf, Yoni Choukroun","submitted_at":"2024-05-07T06:47:12Z","abstract_excerpt":"Error correction codes are a crucial part of the physical communication layer, ensuring the reliable transfer of data over noisy channels. The design of optimal linear block codes capable of being efficiently decoded is of major concern, especially for short block lengths. While neural decoders have recently demonstrated their advantage over classical decoding techniques, the neural design of the codes remains a challenge. In this work, we propose for the first time a unified encoder-decoder training of binary linear block codes. To this end, we adapt the coding setting to support efficient an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.04050","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/2405.04050/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":"2405.04050","created_at":"2026-07-05T08:16:31.164657+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.04050v1","created_at":"2026-07-05T08:16:31.164657+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.04050","created_at":"2026-07-05T08:16:31.164657+00:00"},{"alias_kind":"pith_short_12","alias_value":"JOAWZ4U6MF3Z","created_at":"2026-07-05T08:16:31.164657+00:00"},{"alias_kind":"pith_short_16","alias_value":"JOAWZ4U6MF3ZW6XK","created_at":"2026-07-05T08:16:31.164657+00:00"},{"alias_kind":"pith_short_8","alias_value":"JOAWZ4U6","created_at":"2026-07-05T08:16:31.164657+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.17834","citing_title":"Hybrid Mamba-Transformer Decoder for Error-Correcting Codes","ref_index":11,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JOAWZ4U6MF3ZW6XKJYNFROQPWP","json":"https://pith.science/pith/JOAWZ4U6MF3ZW6XKJYNFROQPWP.json","graph_json":"https://pith.science/api/pith-number/JOAWZ4U6MF3ZW6XKJYNFROQPWP/graph.json","events_json":"https://pith.science/api/pith-number/JOAWZ4U6MF3ZW6XKJYNFROQPWP/events.json","paper":"https://pith.science/paper/JOAWZ4U6"},"agent_actions":{"view_html":"https://pith.science/pith/JOAWZ4U6MF3ZW6XKJYNFROQPWP","download_json":"https://pith.science/pith/JOAWZ4U6MF3ZW6XKJYNFROQPWP.json","view_paper":"https://pith.science/paper/JOAWZ4U6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.04050&json=true","fetch_graph":"https://pith.science/api/pith-number/JOAWZ4U6MF3ZW6XKJYNFROQPWP/graph.json","fetch_events":"https://pith.science/api/pith-number/JOAWZ4U6MF3ZW6XKJYNFROQPWP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JOAWZ4U6MF3ZW6XKJYNFROQPWP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JOAWZ4U6MF3ZW6XKJYNFROQPWP/action/storage_attestation","attest_author":"https://pith.science/pith/JOAWZ4U6MF3ZW6XKJYNFROQPWP/action/author_attestation","sign_citation":"https://pith.science/pith/JOAWZ4U6MF3ZW6XKJYNFROQPWP/action/citation_signature","submit_replication":"https://pith.science/pith/JOAWZ4U6MF3ZW6XKJYNFROQPWP/action/replication_record"}},"created_at":"2026-07-05T08:16:31.164657+00:00","updated_at":"2026-07-05T08:16:31.164657+00:00"}