{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:P5WI7PJ4JIEZMLV6POVMSDIB5C","short_pith_number":"pith:P5WI7PJ4","canonical_record":{"source":{"id":"2501.10861","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-18T19:58:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6bd03a94a929d782b202b4cf7427637731dc53f271a60e039464567a14e56ff5","abstract_canon_sha256":"81d4f9bb975075c7361f1104f9180c26e4767ea5dc668f752f8486a8e6b65824"},"schema_version":"1.0"},"canonical_sha256":"7f6c8fbd3c4a09962ebe7baac90d01e8b6e4365cace5880b2cee86723aec47de","source":{"kind":"arxiv","id":"2501.10861","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.10861","created_at":"2026-07-05T10:02:59Z"},{"alias_kind":"arxiv_version","alias_value":"2501.10861v1","created_at":"2026-07-05T10:02:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10861","created_at":"2026-07-05T10:02:59Z"},{"alias_kind":"pith_short_12","alias_value":"P5WI7PJ4JIEZ","created_at":"2026-07-05T10:02:59Z"},{"alias_kind":"pith_short_16","alias_value":"P5WI7PJ4JIEZMLV6","created_at":"2026-07-05T10:02:59Z"},{"alias_kind":"pith_short_8","alias_value":"P5WI7PJ4","created_at":"2026-07-05T10:02:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:P5WI7PJ4JIEZMLV6POVMSDIB5C","target":"record","payload":{"canonical_record":{"source":{"id":"2501.10861","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-18T19:58:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6bd03a94a929d782b202b4cf7427637731dc53f271a60e039464567a14e56ff5","abstract_canon_sha256":"81d4f9bb975075c7361f1104f9180c26e4767ea5dc668f752f8486a8e6b65824"},"schema_version":"1.0"},"canonical_sha256":"7f6c8fbd3c4a09962ebe7baac90d01e8b6e4365cace5880b2cee86723aec47de","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:02:59.814897Z","signature_b64":"tBxk2FGepSuzwp2jdPSP6rNZ68+cq/2uUIpoHi5LHUu0+n8BZpj2fJpBnyEwMw3TDtOm4YapNRG3WW/d3IfsDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f6c8fbd3c4a09962ebe7baac90d01e8b6e4365cace5880b2cee86723aec47de","last_reissued_at":"2026-07-05T10:02:59.814477Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:02:59.814477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.10861","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":"2026-07-05T10:02:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jJOEWlePKpECLBUwkKSoho040ON8iHIz2/XNeeqVi7wDvhHi6Nvm1408YhPmlPRHFc4Sk9QNyK0jixMGAW0tDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T10:14:09.760923Z"},"content_sha256":"1d92083d42595e0867b6870fe31d0eef21435d672954e1c6f65f565dadadc643","schema_version":"1.0","event_id":"sha256:1d92083d42595e0867b6870fe31d0eef21435d672954e1c6f65f565dadadc643"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:P5WI7PJ4JIEZMLV6POVMSDIB5C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Christopher Angelini, Nidhal Bouaynaya","submitted_at":"2025-01-18T19:58:53Z","abstract_excerpt":"When fine-tuning Deep Neural Networks (DNNs) to new data, DNNs are prone to overwriting network parameters required for task-specific functionality on previously learned tasks, resulting in a loss of performance on those tasks. We propose using parameter-based uncertainty to determine which parameters are relevant to a network's learned function and regularize training to prevent change in these important parameters. We approach this regularization in two ways: (1), we constrain critical parameters from significant changes by associating more critical parameters with lower learning rates, ther"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10861","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/2501.10861/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":"2026-07-05T10:02:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YIHD/Aq+qsk/3eAkCAjRdd/KJtQ+F4zNvcv/7iP1+9Cf8YK8aqdaW7mFwrqCDoy4W+H/dMV1kibuPREWHG9XCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T10:14:09.761556Z"},"content_sha256":"923b9a4d582e0cb46921616f63b9786ce86c1309aaae568ed3988f8d56899b18","schema_version":"1.0","event_id":"sha256:923b9a4d582e0cb46921616f63b9786ce86c1309aaae568ed3988f8d56899b18"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P5WI7PJ4JIEZMLV6POVMSDIB5C/bundle.json","state_url":"https://pith.science/pith/P5WI7PJ4JIEZMLV6POVMSDIB5C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P5WI7PJ4JIEZMLV6POVMSDIB5C/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":"2026-08-15T10:14:09Z","links":{"resolver":"https://pith.science/pith/P5WI7PJ4JIEZMLV6POVMSDIB5C","bundle":"https://pith.science/pith/P5WI7PJ4JIEZMLV6POVMSDIB5C/bundle.json","state":"https://pith.science/pith/P5WI7PJ4JIEZMLV6POVMSDIB5C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P5WI7PJ4JIEZMLV6POVMSDIB5C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:P5WI7PJ4JIEZMLV6POVMSDIB5C","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":"81d4f9bb975075c7361f1104f9180c26e4767ea5dc668f752f8486a8e6b65824","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-18T19:58:53Z","title_canon_sha256":"6bd03a94a929d782b202b4cf7427637731dc53f271a60e039464567a14e56ff5"},"schema_version":"1.0","source":{"id":"2501.10861","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.10861","created_at":"2026-07-05T10:02:59Z"},{"alias_kind":"arxiv_version","alias_value":"2501.10861v1","created_at":"2026-07-05T10:02:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10861","created_at":"2026-07-05T10:02:59Z"},{"alias_kind":"pith_short_12","alias_value":"P5WI7PJ4JIEZ","created_at":"2026-07-05T10:02:59Z"},{"alias_kind":"pith_short_16","alias_value":"P5WI7PJ4JIEZMLV6","created_at":"2026-07-05T10:02:59Z"},{"alias_kind":"pith_short_8","alias_value":"P5WI7PJ4","created_at":"2026-07-05T10:02:59Z"}],"graph_snapshots":[{"event_id":"sha256:923b9a4d582e0cb46921616f63b9786ce86c1309aaae568ed3988f8d56899b18","target":"graph","created_at":"2026-07-05T10:02:59Z","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/2501.10861/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"When fine-tuning Deep Neural Networks (DNNs) to new data, DNNs are prone to overwriting network parameters required for task-specific functionality on previously learned tasks, resulting in a loss of performance on those tasks. We propose using parameter-based uncertainty to determine which parameters are relevant to a network's learned function and regularize training to prevent change in these important parameters. We approach this regularization in two ways: (1), we constrain critical parameters from significant changes by associating more critical parameters with lower learning rates, ther","authors_text":"Christopher Angelini, Nidhal Bouaynaya","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10861","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":"sha256:1d92083d42595e0867b6870fe31d0eef21435d672954e1c6f65f565dadadc643","target":"record","created_at":"2026-07-05T10:02:59Z","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":"81d4f9bb975075c7361f1104f9180c26e4767ea5dc668f752f8486a8e6b65824","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-18T19:58:53Z","title_canon_sha256":"6bd03a94a929d782b202b4cf7427637731dc53f271a60e039464567a14e56ff5"},"schema_version":"1.0","source":{"id":"2501.10861","kind":"arxiv","version":1}},"canonical_sha256":"7f6c8fbd3c4a09962ebe7baac90d01e8b6e4365cace5880b2cee86723aec47de","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f6c8fbd3c4a09962ebe7baac90d01e8b6e4365cace5880b2cee86723aec47de","first_computed_at":"2026-07-05T10:02:59.814477Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:02:59.814477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tBxk2FGepSuzwp2jdPSP6rNZ68+cq/2uUIpoHi5LHUu0+n8BZpj2fJpBnyEwMw3TDtOm4YapNRG3WW/d3IfsDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:02:59.814897Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.10861","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1d92083d42595e0867b6870fe31d0eef21435d672954e1c6f65f565dadadc643","sha256:923b9a4d582e0cb46921616f63b9786ce86c1309aaae568ed3988f8d56899b18"],"state_sha256":"8605689dc5f04a98f4eae8bf6c5ccfcc63e450ac3d8bd7d0390b6bbaada862f1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MCfJg/tRgdx2lIPtmVCxEbTwtrEbvdRGraWQ3oe4XghmSQpI/Nu12a/ICG/iJVzmNr3WdSCgwuZS/DwHcTKjBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T10:14:09.766991Z","bundle_sha256":"b08b7db573093a2ab40fc9d12b5f3ec64d45bb23a23375d4b690e171ced68bd3"}}