{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:HDHXRZIY5DECCIVH5ET4EUOFLI","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":"48fa44d94b54055d4c63574e287366a557266280587f7d19ca72cdd7a6b1435f","cross_cats_sorted":["cs.LG","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-07-29T15:29:18Z","title_canon_sha256":"d0caf07b8dc2b2ac07b14fdb27ae61b6ffc112f101c7f7d6f906e861a0dbc657"},"schema_version":"1.0","source":{"id":"2207.14742","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.14742","created_at":"2026-07-05T05:05:49Z"},{"alias_kind":"arxiv_version","alias_value":"2207.14742v2","created_at":"2026-07-05T05:05:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.14742","created_at":"2026-07-05T05:05:49Z"},{"alias_kind":"pith_short_12","alias_value":"HDHXRZIY5DEC","created_at":"2026-07-05T05:05:49Z"},{"alias_kind":"pith_short_16","alias_value":"HDHXRZIY5DECCIVH","created_at":"2026-07-05T05:05:49Z"},{"alias_kind":"pith_short_8","alias_value":"HDHXRZIY","created_at":"2026-07-05T05:05:49Z"}],"graph_snapshots":[{"event_id":"sha256:0a6099164c66bd3997571800400fcc79e62634c90c0d39e56d3345b246bd6190","target":"graph","created_at":"2026-07-05T05:05:49Z","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/2207.14742/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we propose a fully differentiable graph neural network (GNN)-based architecture for channel decoding and showcase a competitive decoding performance for various coding schemes, such as low-density parity-check (LDPC) and BCH codes. The idea is to let a neural network (NN) learn a generalized message passing algorithm over a given graph that represents the forward error correction (FEC) code structure by replacing node and edge message updates with trainable functions. Contrary to many other deep learning-based decoding approaches, the proposed solution enjoys scalability to arbit","authors_text":"Alexander Keller, Fay\\c{c}al A\\\"it Aoudia, Jakob Hoydis, Sebastian Cammerer","cross_cats":["cs.LG","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-07-29T15:29:18Z","title":"Graph Neural Networks for Channel Decoding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.14742","kind":"arxiv","version":2},"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:a595eb0b3116462f3a92254a7e715f78bb74d7df9fae343840b517c6c46ae667","target":"record","created_at":"2026-07-05T05:05:49Z","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":"48fa44d94b54055d4c63574e287366a557266280587f7d19ca72cdd7a6b1435f","cross_cats_sorted":["cs.LG","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-07-29T15:29:18Z","title_canon_sha256":"d0caf07b8dc2b2ac07b14fdb27ae61b6ffc112f101c7f7d6f906e861a0dbc657"},"schema_version":"1.0","source":{"id":"2207.14742","kind":"arxiv","version":2}},"canonical_sha256":"38cf78e518e8c82122a7e927c251c55a0d860b2bc59cb6651426026778e3dd31","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38cf78e518e8c82122a7e927c251c55a0d860b2bc59cb6651426026778e3dd31","first_computed_at":"2026-07-05T05:05:49.704795Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:05:49.704795Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CeAGb0kmNuCyltFlcIXqra4LdQkrd9gZMuKB59DapQ0uaEirr9iCKg8uia9iladB6sjQgBafAldEd6UMkxm1DA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:05:49.705211Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.14742","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a595eb0b3116462f3a92254a7e715f78bb74d7df9fae343840b517c6c46ae667","sha256:0a6099164c66bd3997571800400fcc79e62634c90c0d39e56d3345b246bd6190"],"state_sha256":"8a3410356c0d3efcb55368d7db190279ddabcbff23b40d02bd5fe6aface02b18"}