{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:IC4J7MY2EKSDYCJ64Q3PEPWHOM","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":"8c2518ab0e44849295a62f4d4f155b2c99a6d24bdfd65b2cd40ff609c3708c12","cross_cats_sorted":["cs.LG","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2022-06-16T16:57:43Z","title_canon_sha256":"c1bdbd844e63dd8f0dab544eadc696d26be6060192863552727cdc698cb670e4"},"schema_version":"1.0","source":{"id":"2206.08297","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.08297","created_at":"2026-07-05T09:52:36Z"},{"alias_kind":"arxiv_version","alias_value":"2206.08297v3","created_at":"2026-07-05T09:52:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.08297","created_at":"2026-07-05T09:52:36Z"},{"alias_kind":"pith_short_12","alias_value":"IC4J7MY2EKSD","created_at":"2026-07-05T09:52:36Z"},{"alias_kind":"pith_short_16","alias_value":"IC4J7MY2EKSDYCJ6","created_at":"2026-07-05T09:52:36Z"},{"alias_kind":"pith_short_8","alias_value":"IC4J7MY2","created_at":"2026-07-05T09:52:36Z"}],"graph_snapshots":[{"event_id":"sha256:fc0617a619e6cc58b7e46de03897b7fe8c84d7628553f490e44f01c6dfbf8ae6","target":"graph","created_at":"2026-07-05T09:52:36Z","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/2206.08297/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modeling long-term dependencies for audio signals is a particularly challenging problem, as even small-time scales yield on the order of a hundred thousand samples. With the recent advent of Transformers, neural architectures became good at modeling dependencies over longer time scales, but they suffered from quadratic constraints to scale them. We propose a generative auto-regressive architecture that can model audio waveforms over quite a large context, greater than 500,000 samples. Our work is adapted to learn time dependencies by learning a latent representation by a CNN front-end, and the","authors_text":"Prateek Verma","cross_cats":["cs.LG","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2022-06-16T16:57:43Z","title":"A Language Model With Million Context Length For Raw Audio"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.08297","kind":"arxiv","version":3},"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":"sha256:53f061b1d220402fd3c9d3056e176e04566e0ce04e6088df4fe11808fa7ee60f","target":"record","created_at":"2026-07-05T09:52:36Z","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":"8c2518ab0e44849295a62f4d4f155b2c99a6d24bdfd65b2cd40ff609c3708c12","cross_cats_sorted":["cs.LG","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2022-06-16T16:57:43Z","title_canon_sha256":"c1bdbd844e63dd8f0dab544eadc696d26be6060192863552727cdc698cb670e4"},"schema_version":"1.0","source":{"id":"2206.08297","kind":"arxiv","version":3}},"canonical_sha256":"40b89fb31a22a43c093ee436f23ec77319a48aa6ce807e60a09d175985cb17ed","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"40b89fb31a22a43c093ee436f23ec77319a48aa6ce807e60a09d175985cb17ed","first_computed_at":"2026-07-05T09:52:36.231957Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:52:36.231957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pwkg073TjTRe9GF71Wqn0bLT+YWiwG+C8OECFblmSrDDlx5zB1YmHcSraGjZIjOpbKLg5z9ZOMan+L5CMgJXAw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:52:36.232444Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.08297","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53f061b1d220402fd3c9d3056e176e04566e0ce04e6088df4fe11808fa7ee60f","sha256:fc0617a619e6cc58b7e46de03897b7fe8c84d7628553f490e44f01c6dfbf8ae6"],"state_sha256":"cfc490c2ae50ae739d233dda173f4763faac706b954c3dfefaed086c3aae86b5"}