{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:RZKHOAEL5L3744O3TV5DV3P2FD","short_pith_number":"pith:RZKHOAEL","canonical_record":{"source":{"id":"2502.06210","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-10T07:21:44Z","cross_cats_sorted":[],"title_canon_sha256":"f5c5ee8ef79533e07857ea8813f26327cadd99c4e3669b7a17a60554fc1e2ab9","abstract_canon_sha256":"d3eed2a6e02d4e5f3c84b4c1dd5fb4275b920892cbe2486a5ddaab07f70b7682"},"schema_version":"1.0"},"canonical_sha256":"8e5477008beaf7fe71db9d7a3aedfa28cbb2ab347e790999a0454e09a7768d40","source":{"kind":"arxiv","id":"2502.06210","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.06210","created_at":"2026-07-05T11:45:55Z"},{"alias_kind":"arxiv_version","alias_value":"2502.06210v2","created_at":"2026-07-05T11:45:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.06210","created_at":"2026-07-05T11:45:55Z"},{"alias_kind":"pith_short_12","alias_value":"RZKHOAEL5L37","created_at":"2026-07-05T11:45:55Z"},{"alias_kind":"pith_short_16","alias_value":"RZKHOAEL5L3744O3","created_at":"2026-07-05T11:45:55Z"},{"alias_kind":"pith_short_8","alias_value":"RZKHOAEL","created_at":"2026-07-05T11:45:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:RZKHOAEL5L3744O3TV5DV3P2FD","target":"record","payload":{"canonical_record":{"source":{"id":"2502.06210","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-10T07:21:44Z","cross_cats_sorted":[],"title_canon_sha256":"f5c5ee8ef79533e07857ea8813f26327cadd99c4e3669b7a17a60554fc1e2ab9","abstract_canon_sha256":"d3eed2a6e02d4e5f3c84b4c1dd5fb4275b920892cbe2486a5ddaab07f70b7682"},"schema_version":"1.0"},"canonical_sha256":"8e5477008beaf7fe71db9d7a3aedfa28cbb2ab347e790999a0454e09a7768d40","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:55.829756Z","signature_b64":"hPqzLP6M5ic4QanS/ceoSykGV0qzsQUyn8UUWT6zdCdiSWKhl7nyKOrOBxOSnEzsB5e3dTWyqptcIqvQgbHbBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e5477008beaf7fe71db9d7a3aedfa28cbb2ab347e790999a0454e09a7768d40","last_reissued_at":"2026-07-05T11:45:55.829132Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:55.829132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.06210","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:45:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J3YWZJkRyS/+cT0JvE1P063Cq5AEWnS8PuIYR9fHlP0kn7CES+ZAhMCehqaPxid47lFPfF5gG2dcqt24pNXPCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T20:23:06.547303Z"},"content_sha256":"a2df1ff231a24725c7d91f1cbec7e47d6bb76f27d6adfe59e575fd80ff3042f0","schema_version":"1.0","event_id":"sha256:a2df1ff231a24725c7d91f1cbec7e47d6bb76f27d6adfe59e575fd80ff3042f0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:RZKHOAEL5L3744O3TV5DV3P2FD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Achieving Deep Continual Learning via Evolution","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aojun Lu, Chunhui Ding, Jiahao Fan, Jiancheng Lv, Junchao Ke, Yanan Sun","submitted_at":"2025-02-10T07:21:44Z","abstract_excerpt":"Deep neural networks, despite their remarkable success, remain fundamentally limited in their ability to perform Continual Learning (CL). While most current methods aim to enhance the capabilities of a single model, Inspired by the collective learning mechanisms of human populations, we introduce Evolving Continual Learning (ECL), a framework that maintains and evolves a diverse population of neural network models. ECL continually searches for an optimal architecture for each introduced incremental task. This tailored model is trained on the corresponding task and archived as a specialized exp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.06210","kind":"arxiv","version":2},"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/2502.06210/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":"2026-07-05T11:45:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x0kCU6wBh9XhLdEXJzhtwxtTRdXSjYrgQlo3EbLJwiYUCsxjO+BQQYsbWwhVLKhv54MAynz8k0hwuFXIA3fvCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T20:23:06.547799Z"},"content_sha256":"a1c15f86da2b4399613e722aff945d41f34c05d47dd02eda7c5e5fd624dcd134","schema_version":"1.0","event_id":"sha256:a1c15f86da2b4399613e722aff945d41f34c05d47dd02eda7c5e5fd624dcd134"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RZKHOAEL5L3744O3TV5DV3P2FD/bundle.json","state_url":"https://pith.science/pith/RZKHOAEL5L3744O3TV5DV3P2FD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RZKHOAEL5L3744O3TV5DV3P2FD/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":"2026-08-18T20:23:06Z","links":{"resolver":"https://pith.science/pith/RZKHOAEL5L3744O3TV5DV3P2FD","bundle":"https://pith.science/pith/RZKHOAEL5L3744O3TV5DV3P2FD/bundle.json","state":"https://pith.science/pith/RZKHOAEL5L3744O3TV5DV3P2FD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RZKHOAEL5L3744O3TV5DV3P2FD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:RZKHOAEL5L3744O3TV5DV3P2FD","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":"d3eed2a6e02d4e5f3c84b4c1dd5fb4275b920892cbe2486a5ddaab07f70b7682","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-10T07:21:44Z","title_canon_sha256":"f5c5ee8ef79533e07857ea8813f26327cadd99c4e3669b7a17a60554fc1e2ab9"},"schema_version":"1.0","source":{"id":"2502.06210","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.06210","created_at":"2026-07-05T11:45:55Z"},{"alias_kind":"arxiv_version","alias_value":"2502.06210v2","created_at":"2026-07-05T11:45:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.06210","created_at":"2026-07-05T11:45:55Z"},{"alias_kind":"pith_short_12","alias_value":"RZKHOAEL5L37","created_at":"2026-07-05T11:45:55Z"},{"alias_kind":"pith_short_16","alias_value":"RZKHOAEL5L3744O3","created_at":"2026-07-05T11:45:55Z"},{"alias_kind":"pith_short_8","alias_value":"RZKHOAEL","created_at":"2026-07-05T11:45:55Z"}],"graph_snapshots":[{"event_id":"sha256:a1c15f86da2b4399613e722aff945d41f34c05d47dd02eda7c5e5fd624dcd134","target":"graph","created_at":"2026-07-05T11:45:55Z","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/2502.06210/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks, despite their remarkable success, remain fundamentally limited in their ability to perform Continual Learning (CL). While most current methods aim to enhance the capabilities of a single model, Inspired by the collective learning mechanisms of human populations, we introduce Evolving Continual Learning (ECL), a framework that maintains and evolves a diverse population of neural network models. ECL continually searches for an optimal architecture for each introduced incremental task. This tailored model is trained on the corresponding task and archived as a specialized exp","authors_text":"Aojun Lu, Chunhui Ding, Jiahao Fan, Jiancheng Lv, Junchao Ke, Yanan Sun","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-10T07:21:44Z","title":"Achieving Deep Continual Learning via Evolution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.06210","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:a2df1ff231a24725c7d91f1cbec7e47d6bb76f27d6adfe59e575fd80ff3042f0","target":"record","created_at":"2026-07-05T11:45:55Z","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":"d3eed2a6e02d4e5f3c84b4c1dd5fb4275b920892cbe2486a5ddaab07f70b7682","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-10T07:21:44Z","title_canon_sha256":"f5c5ee8ef79533e07857ea8813f26327cadd99c4e3669b7a17a60554fc1e2ab9"},"schema_version":"1.0","source":{"id":"2502.06210","kind":"arxiv","version":2}},"canonical_sha256":"8e5477008beaf7fe71db9d7a3aedfa28cbb2ab347e790999a0454e09a7768d40","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8e5477008beaf7fe71db9d7a3aedfa28cbb2ab347e790999a0454e09a7768d40","first_computed_at":"2026-07-05T11:45:55.829132Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:45:55.829132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hPqzLP6M5ic4QanS/ceoSykGV0qzsQUyn8UUWT6zdCdiSWKhl7nyKOrOBxOSnEzsB5e3dTWyqptcIqvQgbHbBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:45:55.829756Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.06210","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a2df1ff231a24725c7d91f1cbec7e47d6bb76f27d6adfe59e575fd80ff3042f0","sha256:a1c15f86da2b4399613e722aff945d41f34c05d47dd02eda7c5e5fd624dcd134"],"state_sha256":"d4db8ffda8a3f203c54b47c4e2d89db1c2661251c8291908b0f8aea4c944e195"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"huMLbnJNARiW+/4J8ULMPJ9k1xqEhetjYcuZK68cBEAK14rRCtXaW1qabmrWIGZCpKpJpmCwNDy/wvNIJOCJDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T20:23:06.552912Z","bundle_sha256":"e883d3baae4be8dc45dfa2008200e36e4ef079741960a814c6cd0f731ef736ca"}}