{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:5NE2ASQFNL2GAIUYY6TPPDZZHM","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":"a56cb2b1f0dce1cc46d803ec908613e5ddc2a3fa00e7e969d47fc9a5ad484c17","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-11T14:40:40Z","title_canon_sha256":"a939d16910e6305f3a5d23f1374bbd68edc007dc10033d9b1739e1436bed1063"},"schema_version":"1.0","source":{"id":"2507.08637","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.08637","created_at":"2026-07-05T11:35:43Z"},{"alias_kind":"arxiv_version","alias_value":"2507.08637v1","created_at":"2026-07-05T11:35:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.08637","created_at":"2026-07-05T11:35:43Z"},{"alias_kind":"pith_short_12","alias_value":"5NE2ASQFNL2G","created_at":"2026-07-05T11:35:43Z"},{"alias_kind":"pith_short_16","alias_value":"5NE2ASQFNL2GAIUY","created_at":"2026-07-05T11:35:43Z"},{"alias_kind":"pith_short_8","alias_value":"5NE2ASQF","created_at":"2026-07-05T11:35:43Z"}],"graph_snapshots":[{"event_id":"sha256:841785006dc9a66b517f987c2084df189c5808cd8e4c28f75c5f79e2fcd34680","target":"graph","created_at":"2026-07-05T11:35:43Z","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/2507.08637/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transformer models are computationally costly on long sequences since regular attention has quadratic $O(n^2)$ time complexity. We introduce Wavelet-Enhanced Random Spectral Attention (WERSA), a novel mechanism of linear $O(n)$ time complexity that is pivotal to enable successful long-sequence processing without the performance trade-off. WERSA merges content-adaptive random spectral features together with multi-resolution Haar wavelets and learnable parameters to selectively attend to informative scales of data while preserving linear efficiency.\n  Large-scale comparisons \\textbf{on single GP","authors_text":"Vincenzo Dentamaro","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-11T14:40:40Z","title":"Scaling Attention to Very Long Sequences in Linear Time with Wavelet-Enhanced Random Spectral Attention (WERSA)"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.08637","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":"sha256:7229816cdcbbc0a7aceecaff89eb2cb7481cb623f170b47e112bf4d4b43a4a43","target":"record","created_at":"2026-07-05T11:35:43Z","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":"a56cb2b1f0dce1cc46d803ec908613e5ddc2a3fa00e7e969d47fc9a5ad484c17","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-11T14:40:40Z","title_canon_sha256":"a939d16910e6305f3a5d23f1374bbd68edc007dc10033d9b1739e1436bed1063"},"schema_version":"1.0","source":{"id":"2507.08637","kind":"arxiv","version":1}},"canonical_sha256":"eb49a04a056af4602298c7a6f78f393b2eda74dc878bdb5d16fd86ee8167a972","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eb49a04a056af4602298c7a6f78f393b2eda74dc878bdb5d16fd86ee8167a972","first_computed_at":"2026-07-05T11:35:43.736796Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:35:43.736796Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KNIL83k58Y4YtahSwdQcwlG+Ib45Uqk0+KHJmEm0yMI+LXWMJo/5PndPf+PBFZkUkPrbjH2r4i8N9M0CJ9ffBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:35:43.737391Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.08637","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7229816cdcbbc0a7aceecaff89eb2cb7481cb623f170b47e112bf4d4b43a4a43","sha256:841785006dc9a66b517f987c2084df189c5808cd8e4c28f75c5f79e2fcd34680"],"state_sha256":"203c5f611415792cde19cc9d2dfa83c928b5d168cc87df98220e602600bd1477"}