{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:5M6TCL23P3NC57YIFTJMON75KC","short_pith_number":"pith:5M6TCL23","canonical_record":{"source":{"id":"2305.11462","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-19T06:30:19Z","cross_cats_sorted":[],"title_canon_sha256":"218a523b4d83e940e58eaa1f493ed0a5d9bec0c9b84552fcd1002719d7fa5bf6","abstract_canon_sha256":"c5c2950b134a5993ef12ded8bbf4cda3833360f718d3d3ebe4480ff504f59241"},"schema_version":"1.0"},"canonical_sha256":"eb3d312f5b7eda2eff082cd2c737fd50a1eca45b537d0b13414d5ca3eb78e1e0","source":{"kind":"arxiv","id":"2305.11462","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.11462","created_at":"2026-07-05T06:11:47Z"},{"alias_kind":"arxiv_version","alias_value":"2305.11462v1","created_at":"2026-07-05T06:11:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.11462","created_at":"2026-07-05T06:11:47Z"},{"alias_kind":"pith_short_12","alias_value":"5M6TCL23P3NC","created_at":"2026-07-05T06:11:47Z"},{"alias_kind":"pith_short_16","alias_value":"5M6TCL23P3NC57YI","created_at":"2026-07-05T06:11:47Z"},{"alias_kind":"pith_short_8","alias_value":"5M6TCL23","created_at":"2026-07-05T06:11:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:5M6TCL23P3NC57YIFTJMON75KC","target":"record","payload":{"canonical_record":{"source":{"id":"2305.11462","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-19T06:30:19Z","cross_cats_sorted":[],"title_canon_sha256":"218a523b4d83e940e58eaa1f493ed0a5d9bec0c9b84552fcd1002719d7fa5bf6","abstract_canon_sha256":"c5c2950b134a5993ef12ded8bbf4cda3833360f718d3d3ebe4480ff504f59241"},"schema_version":"1.0"},"canonical_sha256":"eb3d312f5b7eda2eff082cd2c737fd50a1eca45b537d0b13414d5ca3eb78e1e0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:11:47.289436Z","signature_b64":"doBdU5kp2x/KPwIOBrWDBT7FqNfs8xvpMJXnyUr5rZw5DvH23iaBf/dA9RuWwrz+pmWj4WxMnJBxO56Rde/nDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb3d312f5b7eda2eff082cd2c737fd50a1eca45b537d0b13414d5ca3eb78e1e0","last_reissued_at":"2026-07-05T06:11:47.288971Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:11:47.288971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.11462","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:11:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9F+uFv8qDpvHe0P5QgNSu0PaDvfTvw+ohgpnUg84tqmU18V2Xcn6UQPjFxuqVvZ6Ou0LfDtY0U8Qcns/UUWACw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T04:45:32.433394Z"},"content_sha256":"e0fa82fc2730cea3378c5f4dc042d90a433732f65d98760e65e95e88d9550043","schema_version":"1.0","event_id":"sha256:e0fa82fc2730cea3378c5f4dc042d90a433732f65d98760e65e95e88d9550043"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:5M6TCL23P3NC57YIFTJMON75KC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Extending Memory for Language Modelling","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anupiya Nugaliyadde","submitted_at":"2023-05-19T06:30:19Z","abstract_excerpt":"Breakthroughs in deep learning and memory networks have made major advances in natural language understanding. Language is sequential and information carried through the sequence can be captured through memory networks. Learning the sequence is one of the key aspects in learning the language. However, memory networks are not capable of holding infinitely long sequences in their memories and are limited by various constraints such as the vanishing or exploding gradient problem. Therefore, natural language understanding models are affected when presented with long sequential text. We introduce L"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.11462","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/2305.11462/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:11:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CB2r+uwjNDcJRSVyPXTOsvHCIyWsmMsP5NcBJYdc9EUFuRLl7Nj2Y5RRwbFol/1ziO9WV4O1iqNXGKqJz94SDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T04:45:32.433904Z"},"content_sha256":"3f914842fcfb2474e35b7e8c22620e722ac755e3566429cd525532abf1c4a1b1","schema_version":"1.0","event_id":"sha256:3f914842fcfb2474e35b7e8c22620e722ac755e3566429cd525532abf1c4a1b1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5M6TCL23P3NC57YIFTJMON75KC/bundle.json","state_url":"https://pith.science/pith/5M6TCL23P3NC57YIFTJMON75KC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5M6TCL23P3NC57YIFTJMON75KC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T04:45:32Z","links":{"resolver":"https://pith.science/pith/5M6TCL23P3NC57YIFTJMON75KC","bundle":"https://pith.science/pith/5M6TCL23P3NC57YIFTJMON75KC/bundle.json","state":"https://pith.science/pith/5M6TCL23P3NC57YIFTJMON75KC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5M6TCL23P3NC57YIFTJMON75KC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:5M6TCL23P3NC57YIFTJMON75KC","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":"c5c2950b134a5993ef12ded8bbf4cda3833360f718d3d3ebe4480ff504f59241","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-19T06:30:19Z","title_canon_sha256":"218a523b4d83e940e58eaa1f493ed0a5d9bec0c9b84552fcd1002719d7fa5bf6"},"schema_version":"1.0","source":{"id":"2305.11462","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.11462","created_at":"2026-07-05T06:11:47Z"},{"alias_kind":"arxiv_version","alias_value":"2305.11462v1","created_at":"2026-07-05T06:11:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.11462","created_at":"2026-07-05T06:11:47Z"},{"alias_kind":"pith_short_12","alias_value":"5M6TCL23P3NC","created_at":"2026-07-05T06:11:47Z"},{"alias_kind":"pith_short_16","alias_value":"5M6TCL23P3NC57YI","created_at":"2026-07-05T06:11:47Z"},{"alias_kind":"pith_short_8","alias_value":"5M6TCL23","created_at":"2026-07-05T06:11:47Z"}],"graph_snapshots":[{"event_id":"sha256:3f914842fcfb2474e35b7e8c22620e722ac755e3566429cd525532abf1c4a1b1","target":"graph","created_at":"2026-07-05T06:11:47Z","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/2305.11462/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Breakthroughs in deep learning and memory networks have made major advances in natural language understanding. Language is sequential and information carried through the sequence can be captured through memory networks. Learning the sequence is one of the key aspects in learning the language. However, memory networks are not capable of holding infinitely long sequences in their memories and are limited by various constraints such as the vanishing or exploding gradient problem. Therefore, natural language understanding models are affected when presented with long sequential text. We introduce L","authors_text":"Anupiya Nugaliyadde","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-19T06:30:19Z","title":"Extending Memory for Language Modelling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.11462","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:e0fa82fc2730cea3378c5f4dc042d90a433732f65d98760e65e95e88d9550043","target":"record","created_at":"2026-07-05T06:11:47Z","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":"c5c2950b134a5993ef12ded8bbf4cda3833360f718d3d3ebe4480ff504f59241","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-19T06:30:19Z","title_canon_sha256":"218a523b4d83e940e58eaa1f493ed0a5d9bec0c9b84552fcd1002719d7fa5bf6"},"schema_version":"1.0","source":{"id":"2305.11462","kind":"arxiv","version":1}},"canonical_sha256":"eb3d312f5b7eda2eff082cd2c737fd50a1eca45b537d0b13414d5ca3eb78e1e0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eb3d312f5b7eda2eff082cd2c737fd50a1eca45b537d0b13414d5ca3eb78e1e0","first_computed_at":"2026-07-05T06:11:47.288971Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:11:47.288971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"doBdU5kp2x/KPwIOBrWDBT7FqNfs8xvpMJXnyUr5rZw5DvH23iaBf/dA9RuWwrz+pmWj4WxMnJBxO56Rde/nDg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:11:47.289436Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.11462","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e0fa82fc2730cea3378c5f4dc042d90a433732f65d98760e65e95e88d9550043","sha256:3f914842fcfb2474e35b7e8c22620e722ac755e3566429cd525532abf1c4a1b1"],"state_sha256":"aeb7bbba528d8008a5c24927fbf4a5e20a7607d5be19e7380f54fe8582bd7852"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0Jo6B42T0Y/4oy4nl1w0Vc3txlwyzMzZrwBVd2OcspNZ+td/Ybn6RBhpfROlvm5doQCJTZ4QzZ0sOEEdLHpHAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T04:45:32.438121Z","bundle_sha256":"a1552c63a7d6b0d31fcbfe301628dc0e5b757e8dcbccc36ad300f5451365d8e1"}}