{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:K7CXLUGAHVAVHQGV5VLLGRIMUD","short_pith_number":"pith:K7CXLUGA","canonical_record":{"source":{"id":"1805.05758","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-15T13:37:33Z","cross_cats_sorted":[],"title_canon_sha256":"d937b9efb7a6dccfa18ee128a304f570fece31602025a07efd5bbcf0e5d508dc","abstract_canon_sha256":"70a9093308f22f1491390b4e9c78fd63c586cf0e3c65ffb83fd214ff4d2c99ab"},"schema_version":"1.0"},"canonical_sha256":"57c575d0c03d4153c0d5ed56b3450ca0f0b2f2ee6b3ef5e39e75c9db0dade21b","source":{"kind":"arxiv","id":"1805.05758","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.05758","created_at":"2026-05-18T00:15:53Z"},{"alias_kind":"arxiv_version","alias_value":"1805.05758v1","created_at":"2026-05-18T00:15:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.05758","created_at":"2026-05-18T00:15:53Z"},{"alias_kind":"pith_short_12","alias_value":"K7CXLUGAHVAV","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"K7CXLUGAHVAVHQGV","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"K7CXLUGA","created_at":"2026-05-18T12:32:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:K7CXLUGAHVAVHQGV5VLLGRIMUD","target":"record","payload":{"canonical_record":{"source":{"id":"1805.05758","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-15T13:37:33Z","cross_cats_sorted":[],"title_canon_sha256":"d937b9efb7a6dccfa18ee128a304f570fece31602025a07efd5bbcf0e5d508dc","abstract_canon_sha256":"70a9093308f22f1491390b4e9c78fd63c586cf0e3c65ffb83fd214ff4d2c99ab"},"schema_version":"1.0"},"canonical_sha256":"57c575d0c03d4153c0d5ed56b3450ca0f0b2f2ee6b3ef5e39e75c9db0dade21b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:53.779950Z","signature_b64":"ilsyQALxFbfWk7x7iRIjUvUp5IHhGhPu4uZ+kW/6EibPREAM/ayMSM0iE91pzmjjRyqmLVHQb8Ph8sV/6NFhCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57c575d0c03d4153c0d5ed56b3450ca0f0b2f2ee6b3ef5e39e75c9db0dade21b","last_reissued_at":"2026-05-18T00:15:53.779197Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:53.779197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.05758","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":"2026-05-18T00:15:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k5SIMiu957LEJeor2CKLWVousZ8Nm6Z2+e+3Q+J/o9oS4iUfmS4tiWfqEUFvtMR4AlC3MYN2eP7f3GwvE6RZDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T19:24:05.397373Z"},"content_sha256":"210eb6c8607713402be3c15f7bf78d50cd511b4aa9d5551cd935115f4acdddff","schema_version":"1.0","event_id":"sha256:210eb6c8607713402be3c15f7bf78d50cd511b4aa9d5551cd935115f4acdddff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:K7CXLUGAHVAVHQGV5VLLGRIMUD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Continuous Learning in a Hierarchical Multiscale Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Clement Delangue, Julien Chaumond, Thomas Wolf","submitted_at":"2018-05-15T13:37:33Z","abstract_excerpt":"We reformulate the problem of encoding a multi-scale representation of a sequence in a language model by casting it in a continuous learning framework. We propose a hierarchical multi-scale language model in which short time-scale dependencies are encoded in the hidden state of a lower-level recurrent neural network while longer time-scale dependencies are encoded in the dynamic of the lower-level network by having a meta-learner update the weights of the lower-level neural network in an online meta-learning fashion. We use elastic weights consolidation as a higher-level to prevent catastrophi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.05758","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":""},"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":"2026-05-18T00:15:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iwDc640zRA9NuUKUthAPpdZoGk23XDoEFWuxhu8VeGZ4jzbNkAUzAoeZLyLbD9GZbIxXWqvkKdRYT/GqZRXMAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T19:24:05.397972Z"},"content_sha256":"1ef58e2f10fe227eb8e1823318b969b0f91f96ecb715057547fbd271d2564338","schema_version":"1.0","event_id":"sha256:1ef58e2f10fe227eb8e1823318b969b0f91f96ecb715057547fbd271d2564338"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K7CXLUGAHVAVHQGV5VLLGRIMUD/bundle.json","state_url":"https://pith.science/pith/K7CXLUGAHVAVHQGV5VLLGRIMUD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K7CXLUGAHVAVHQGV5VLLGRIMUD/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":"2026-06-02T19:24:05Z","links":{"resolver":"https://pith.science/pith/K7CXLUGAHVAVHQGV5VLLGRIMUD","bundle":"https://pith.science/pith/K7CXLUGAHVAVHQGV5VLLGRIMUD/bundle.json","state":"https://pith.science/pith/K7CXLUGAHVAVHQGV5VLLGRIMUD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K7CXLUGAHVAVHQGV5VLLGRIMUD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:K7CXLUGAHVAVHQGV5VLLGRIMUD","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":"70a9093308f22f1491390b4e9c78fd63c586cf0e3c65ffb83fd214ff4d2c99ab","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-15T13:37:33Z","title_canon_sha256":"d937b9efb7a6dccfa18ee128a304f570fece31602025a07efd5bbcf0e5d508dc"},"schema_version":"1.0","source":{"id":"1805.05758","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.05758","created_at":"2026-05-18T00:15:53Z"},{"alias_kind":"arxiv_version","alias_value":"1805.05758v1","created_at":"2026-05-18T00:15:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.05758","created_at":"2026-05-18T00:15:53Z"},{"alias_kind":"pith_short_12","alias_value":"K7CXLUGAHVAV","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"K7CXLUGAHVAVHQGV","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"K7CXLUGA","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:1ef58e2f10fe227eb8e1823318b969b0f91f96ecb715057547fbd271d2564338","target":"graph","created_at":"2026-05-18T00:15:53Z","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"},"paper":{"abstract_excerpt":"We reformulate the problem of encoding a multi-scale representation of a sequence in a language model by casting it in a continuous learning framework. We propose a hierarchical multi-scale language model in which short time-scale dependencies are encoded in the hidden state of a lower-level recurrent neural network while longer time-scale dependencies are encoded in the dynamic of the lower-level network by having a meta-learner update the weights of the lower-level neural network in an online meta-learning fashion. We use elastic weights consolidation as a higher-level to prevent catastrophi","authors_text":"Clement Delangue, Julien Chaumond, Thomas Wolf","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-15T13:37:33Z","title":"Continuous Learning in a Hierarchical Multiscale Neural Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.05758","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":"sha256:210eb6c8607713402be3c15f7bf78d50cd511b4aa9d5551cd935115f4acdddff","target":"record","created_at":"2026-05-18T00:15:53Z","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":"70a9093308f22f1491390b4e9c78fd63c586cf0e3c65ffb83fd214ff4d2c99ab","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-15T13:37:33Z","title_canon_sha256":"d937b9efb7a6dccfa18ee128a304f570fece31602025a07efd5bbcf0e5d508dc"},"schema_version":"1.0","source":{"id":"1805.05758","kind":"arxiv","version":1}},"canonical_sha256":"57c575d0c03d4153c0d5ed56b3450ca0f0b2f2ee6b3ef5e39e75c9db0dade21b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"57c575d0c03d4153c0d5ed56b3450ca0f0b2f2ee6b3ef5e39e75c9db0dade21b","first_computed_at":"2026-05-18T00:15:53.779197Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:53.779197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ilsyQALxFbfWk7x7iRIjUvUp5IHhGhPu4uZ+kW/6EibPREAM/ayMSM0iE91pzmjjRyqmLVHQb8Ph8sV/6NFhCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:53.779950Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.05758","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:210eb6c8607713402be3c15f7bf78d50cd511b4aa9d5551cd935115f4acdddff","sha256:1ef58e2f10fe227eb8e1823318b969b0f91f96ecb715057547fbd271d2564338"],"state_sha256":"8a9a9eab46950b9f0507d9a228e602cf32899c691d1f3415f0169a8e4b4255b3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"drqGQxqhSXHDOHaEMyhKs6lsB1GpVNp/f32POu0ehyWyJ7m5PdUcLACdYq312gOTAWLp0Zn2u+af05wfKKR5Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T19:24:05.401083Z","bundle_sha256":"fcb88550ead3d3404414351d05664f05e52f7e46077359b686fb2a7b2d99e585"}}