{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WJZIFLISDGUZCT5VBPWBZRZMSD","short_pith_number":"pith:WJZIFLIS","schema_version":"1.0","canonical_sha256":"b27282ad1219a9914fb50bec1cc72c90f2122e55d9f33fc12e19ac20b9e6c40c","source":{"kind":"arxiv","id":"2508.08052","version":2},"attestation_state":"computed","paper":{"title":"On Understanding of the Dynamics of Model Capacity in Continual Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Krishnan Raghavan, Supriyo Chakraborty","submitted_at":"2025-08-11T14:52:56Z","abstract_excerpt":"The stability-plasticity dilemma, closely related to a neural network's (NN) capacity-its ability to represent tasks-is a fundamental challenge in continual learning (CL). Within this context, we introduce CL's effective model capacity (CLEMC) that characterizes the dynamic behavior of the stability-plasticity balance point. We develop a difference equation to model the evolution of the interplay between the NN, task data, and optimization procedure. We then leverage CLEMC to demonstrate that the effective capacity-and, by extension, the stability-plasticity balance point is inherently non-sta"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2508.08052","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-11T14:52:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0bccfc0c05e86de1c1c7867a76e5819e0aff8911c4e844908ca56848c6c74db3","abstract_canon_sha256":"d5fdd0d917de98cb68f14222798e5ea20a58beff71b5021df7282ac92d5d3d4f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:50.453343Z","signature_b64":"FPtkBIZGJYGTa1YfB068N1WDNKKRe1QMw9BL3sULYh/UAG8+d9OPJ4avgvzfvyam1vw/P9HggDVuTNx9+d2EAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b27282ad1219a9914fb50bec1cc72c90f2122e55d9f33fc12e19ac20b9e6c40c","last_reissued_at":"2026-07-05T11:53:50.452891Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:50.452891Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Understanding of the Dynamics of Model Capacity in Continual Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Krishnan Raghavan, Supriyo Chakraborty","submitted_at":"2025-08-11T14:52:56Z","abstract_excerpt":"The stability-plasticity dilemma, closely related to a neural network's (NN) capacity-its ability to represent tasks-is a fundamental challenge in continual learning (CL). Within this context, we introduce CL's effective model capacity (CLEMC) that characterizes the dynamic behavior of the stability-plasticity balance point. We develop a difference equation to model the evolution of the interplay between the NN, task data, and optimization procedure. We then leverage CLEMC to demonstrate that the effective capacity-and, by extension, the stability-plasticity balance point is inherently non-sta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.08052","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/2508.08052/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2508.08052","created_at":"2026-07-05T11:53:50.452948+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.08052v2","created_at":"2026-07-05T11:53:50.452948+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.08052","created_at":"2026-07-05T11:53:50.452948+00:00"},{"alias_kind":"pith_short_12","alias_value":"WJZIFLISDGUZ","created_at":"2026-07-05T11:53:50.452948+00:00"},{"alias_kind":"pith_short_16","alias_value":"WJZIFLISDGUZCT5V","created_at":"2026-07-05T11:53:50.452948+00:00"},{"alias_kind":"pith_short_8","alias_value":"WJZIFLIS","created_at":"2026-07-05T11:53:50.452948+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WJZIFLISDGUZCT5VBPWBZRZMSD","json":"https://pith.science/pith/WJZIFLISDGUZCT5VBPWBZRZMSD.json","graph_json":"https://pith.science/api/pith-number/WJZIFLISDGUZCT5VBPWBZRZMSD/graph.json","events_json":"https://pith.science/api/pith-number/WJZIFLISDGUZCT5VBPWBZRZMSD/events.json","paper":"https://pith.science/paper/WJZIFLIS"},"agent_actions":{"view_html":"https://pith.science/pith/WJZIFLISDGUZCT5VBPWBZRZMSD","download_json":"https://pith.science/pith/WJZIFLISDGUZCT5VBPWBZRZMSD.json","view_paper":"https://pith.science/paper/WJZIFLIS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.08052&json=true","fetch_graph":"https://pith.science/api/pith-number/WJZIFLISDGUZCT5VBPWBZRZMSD/graph.json","fetch_events":"https://pith.science/api/pith-number/WJZIFLISDGUZCT5VBPWBZRZMSD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WJZIFLISDGUZCT5VBPWBZRZMSD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WJZIFLISDGUZCT5VBPWBZRZMSD/action/storage_attestation","attest_author":"https://pith.science/pith/WJZIFLISDGUZCT5VBPWBZRZMSD/action/author_attestation","sign_citation":"https://pith.science/pith/WJZIFLISDGUZCT5VBPWBZRZMSD/action/citation_signature","submit_replication":"https://pith.science/pith/WJZIFLISDGUZCT5VBPWBZRZMSD/action/replication_record"}},"created_at":"2026-07-05T11:53:50.452948+00:00","updated_at":"2026-07-05T11:53:50.452948+00:00"}