{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:V7DS22FR7YJMXT5OK5UWWTJ75R","short_pith_number":"pith:V7DS22FR","canonical_record":{"source":{"id":"2310.00154","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-29T21:23:27Z","cross_cats_sorted":["cs.AI","eess.SP"],"title_canon_sha256":"4ff41cffe57be68748456f986b355a2e95c906869464cd6cfe1aac529cc4cafe","abstract_canon_sha256":"5bb7bc2921b8332959c02e52eabbfdae971c86fa5253e48a125eeda09f973a87"},"schema_version":"1.0"},"canonical_sha256":"afc72d68b1fe12cbcfae57696b4d3fec6b7e49337c27b79e1f7b6c9280e9d0ff","source":{"kind":"arxiv","id":"2310.00154","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.00154","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"arxiv_version","alias_value":"2310.00154v2","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00154","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"pith_short_12","alias_value":"V7DS22FR7YJM","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"pith_short_16","alias_value":"V7DS22FR7YJMXT5O","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"pith_short_8","alias_value":"V7DS22FR","created_at":"2026-07-05T08:25:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:V7DS22FR7YJMXT5OK5UWWTJ75R","target":"record","payload":{"canonical_record":{"source":{"id":"2310.00154","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-29T21:23:27Z","cross_cats_sorted":["cs.AI","eess.SP"],"title_canon_sha256":"4ff41cffe57be68748456f986b355a2e95c906869464cd6cfe1aac529cc4cafe","abstract_canon_sha256":"5bb7bc2921b8332959c02e52eabbfdae971c86fa5253e48a125eeda09f973a87"},"schema_version":"1.0"},"canonical_sha256":"afc72d68b1fe12cbcfae57696b4d3fec6b7e49337c27b79e1f7b6c9280e9d0ff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:25:28.903085Z","signature_b64":"h8UFRNINxOKcOLIZSpds7UhZZxA1jBffDJbu8nXza8E8SAloTxXzsuYH7em5kuy1yK3gEzUYU9bDUtLucDH5DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afc72d68b1fe12cbcfae57696b4d3fec6b7e49337c27b79e1f7b6c9280e9d0ff","last_reissued_at":"2026-07-05T08:25:28.902642Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:25:28.902642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.00154","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":"2026-07-05T08:25:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WplU52tJsDQTR1zV8Zxi9P9P48f8pqVf/ZGoAR67sctfBWEvcQ6pPH29mghdxk/QWyvzQaFsDhi2ekt2vhLTDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:55:57.979761Z"},"content_sha256":"1f42b9e1e2797523d8972fa39e4338d7732a54f2899c58aa5c0135f1e803f2d3","schema_version":"1.0","event_id":"sha256:1f42b9e1e2797523d8972fa39e4338d7732a54f2899c58aa5c0135f1e803f2d3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:V7DS22FR7YJMXT5OK5UWWTJ75R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Primal Dual Continual Learning: Balancing Stability and Plasticity through Adaptive Memory Allocation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Alejandro Ribeiro, Juan Elenter, Navid Naderializadeh, Tara Javidi","submitted_at":"2023-09-29T21:23:27Z","abstract_excerpt":"Continual learning is inherently a constrained learning problem. The goal is to learn a predictor under a no-forgetting requirement. Although several prior studies formulate it as such, they do not solve the constrained problem explicitly. In this work, we show that it is both possible and beneficial to undertake the constrained optimization problem directly. To do this, we leverage recent results in constrained learning through Lagrangian duality. We focus on memory-based methods, where a small subset of samples from previous tasks can be stored in a replay buffer. In this setting, we analyze"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00154","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/2310.00154/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":"2026-07-05T08:25:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OhPoHM9+DMORCcbH4Od2alVoCW9idmDJM8ttcIa60ITkSNY3tkHu+Yx8a+pjdfO5jpTraEsmL4jSQWfxiMiLDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:55:57.981019Z"},"content_sha256":"ae6fb8ae04cf51e426071516332a72f3e914cd58ade6ca33867b6177a4e9c3d5","schema_version":"1.0","event_id":"sha256:ae6fb8ae04cf51e426071516332a72f3e914cd58ade6ca33867b6177a4e9c3d5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V7DS22FR7YJMXT5OK5UWWTJ75R/bundle.json","state_url":"https://pith.science/pith/V7DS22FR7YJMXT5OK5UWWTJ75R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V7DS22FR7YJMXT5OK5UWWTJ75R/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":"2026-08-08T15:55:57Z","links":{"resolver":"https://pith.science/pith/V7DS22FR7YJMXT5OK5UWWTJ75R","bundle":"https://pith.science/pith/V7DS22FR7YJMXT5OK5UWWTJ75R/bundle.json","state":"https://pith.science/pith/V7DS22FR7YJMXT5OK5UWWTJ75R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V7DS22FR7YJMXT5OK5UWWTJ75R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:V7DS22FR7YJMXT5OK5UWWTJ75R","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":"5bb7bc2921b8332959c02e52eabbfdae971c86fa5253e48a125eeda09f973a87","cross_cats_sorted":["cs.AI","eess.SP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-29T21:23:27Z","title_canon_sha256":"4ff41cffe57be68748456f986b355a2e95c906869464cd6cfe1aac529cc4cafe"},"schema_version":"1.0","source":{"id":"2310.00154","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.00154","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"arxiv_version","alias_value":"2310.00154v2","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00154","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"pith_short_12","alias_value":"V7DS22FR7YJM","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"pith_short_16","alias_value":"V7DS22FR7YJMXT5O","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"pith_short_8","alias_value":"V7DS22FR","created_at":"2026-07-05T08:25:28Z"}],"graph_snapshots":[{"event_id":"sha256:ae6fb8ae04cf51e426071516332a72f3e914cd58ade6ca33867b6177a4e9c3d5","target":"graph","created_at":"2026-07-05T08:25:28Z","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/2310.00154/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Continual learning is inherently a constrained learning problem. The goal is to learn a predictor under a no-forgetting requirement. Although several prior studies formulate it as such, they do not solve the constrained problem explicitly. In this work, we show that it is both possible and beneficial to undertake the constrained optimization problem directly. To do this, we leverage recent results in constrained learning through Lagrangian duality. We focus on memory-based methods, where a small subset of samples from previous tasks can be stored in a replay buffer. In this setting, we analyze","authors_text":"Alejandro Ribeiro, Juan Elenter, Navid Naderializadeh, Tara Javidi","cross_cats":["cs.AI","eess.SP"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-29T21:23:27Z","title":"Primal Dual Continual Learning: Balancing Stability and Plasticity through Adaptive Memory Allocation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00154","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":"sha256:1f42b9e1e2797523d8972fa39e4338d7732a54f2899c58aa5c0135f1e803f2d3","target":"record","created_at":"2026-07-05T08:25:28Z","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":"5bb7bc2921b8332959c02e52eabbfdae971c86fa5253e48a125eeda09f973a87","cross_cats_sorted":["cs.AI","eess.SP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-29T21:23:27Z","title_canon_sha256":"4ff41cffe57be68748456f986b355a2e95c906869464cd6cfe1aac529cc4cafe"},"schema_version":"1.0","source":{"id":"2310.00154","kind":"arxiv","version":2}},"canonical_sha256":"afc72d68b1fe12cbcfae57696b4d3fec6b7e49337c27b79e1f7b6c9280e9d0ff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"afc72d68b1fe12cbcfae57696b4d3fec6b7e49337c27b79e1f7b6c9280e9d0ff","first_computed_at":"2026-07-05T08:25:28.902642Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:25:28.902642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"h8UFRNINxOKcOLIZSpds7UhZZxA1jBffDJbu8nXza8E8SAloTxXzsuYH7em5kuy1yK3gEzUYU9bDUtLucDH5DA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:25:28.903085Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.00154","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1f42b9e1e2797523d8972fa39e4338d7732a54f2899c58aa5c0135f1e803f2d3","sha256:ae6fb8ae04cf51e426071516332a72f3e914cd58ade6ca33867b6177a4e9c3d5"],"state_sha256":"1f45cb6135ee333742701d0b24c96a3fa280dcf67643c3a3106c9e4523cff280"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"un1A893PxEi5CKHVRLJxDqZHAnqjf0DsqUtULFWuFeHpfGmksyz/+QWHBQ3Dhl/VxX5wVQgidRUYTyQvtfsQDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T15:55:57.991085Z","bundle_sha256":"609b41cb69c90492ee70d748b641394aaf58e595f4791986ed2729432efc7ddd"}}