{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:JOBLC4UFTYAXO2ITECPTPNA5YF","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":"0149068e17de92c4835b2057894f42575287ff923da70435b4db438bbfc38723","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-21T08:13:34Z","title_canon_sha256":"c9a5718d3c9f87be15c54da91194dced231cee552c0d3be2ce8ade0bfd6ca9a1"},"schema_version":"1.0","source":{"id":"2210.11794","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.11794","created_at":"2026-07-05T05:37:09Z"},{"alias_kind":"arxiv_version","alias_value":"2210.11794v2","created_at":"2026-07-05T05:37:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.11794","created_at":"2026-07-05T05:37:09Z"},{"alias_kind":"pith_short_12","alias_value":"JOBLC4UFTYAX","created_at":"2026-07-05T05:37:09Z"},{"alias_kind":"pith_short_16","alias_value":"JOBLC4UFTYAXO2IT","created_at":"2026-07-05T05:37:09Z"},{"alias_kind":"pith_short_8","alias_value":"JOBLC4UF","created_at":"2026-07-05T05:37:09Z"}],"graph_snapshots":[{"event_id":"sha256:9f14765bbf3c49cf2ecfd3fd39d24c2e7c0af7e94c49057607398431ca56f16f","target":"graph","created_at":"2026-07-05T05:37:09Z","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/2210.11794/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Efficient Transformers have been developed for long sequence modeling, due to their subquadratic memory and time complexity. Sparse Transformer is a popular approach to improving the efficiency of Transformers by restricting self-attention to locations specified by the predefined sparse patterns. However, leveraging sparsity may sacrifice expressiveness compared to full-attention, when important token correlations are multiple hops away. To combine advantages of both the efficiency of sparse transformer and the expressiveness of full-attention Transformer, we propose \\textit{Diffuser}, a new s","authors_text":"Aosong Feng, Irene Li, Rex Ying, Yuang Jiang","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-21T08:13:34Z","title":"Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.11794","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":"sha256:29130fde8fe9a86d6bd46dd6a871f3975a1c8d9fb45ef52cb94a05e956a09bab","target":"record","created_at":"2026-07-05T05:37:09Z","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":"0149068e17de92c4835b2057894f42575287ff923da70435b4db438bbfc38723","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-21T08:13:34Z","title_canon_sha256":"c9a5718d3c9f87be15c54da91194dced231cee552c0d3be2ce8ade0bfd6ca9a1"},"schema_version":"1.0","source":{"id":"2210.11794","kind":"arxiv","version":2}},"canonical_sha256":"4b82b172859e01776913209f37b41dc14b7525ca7cce80e2795e588a3bdfc66c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4b82b172859e01776913209f37b41dc14b7525ca7cce80e2795e588a3bdfc66c","first_computed_at":"2026-07-05T05:37:09.693582Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:37:09.693582Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"obnjxJocO7xzllbpYIGKGYpFwwu4fv5PSFrWaGktt/y33Xclns68dTJCN4qAJzDb+M5h0ZEYWKInygcg+7ZyCg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:37:09.694067Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.11794","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:29130fde8fe9a86d6bd46dd6a871f3975a1c8d9fb45ef52cb94a05e956a09bab","sha256:9f14765bbf3c49cf2ecfd3fd39d24c2e7c0af7e94c49057607398431ca56f16f"],"state_sha256":"dcbcb15a08a755f32efe099ad709d5e740550d0037eb3ddfa3d45efc4e336b36"}