{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:7R24WGSGPPH4XRT7FJKDPJUCTE","short_pith_number":"pith:7R24WGSG","canonical_record":{"source":{"id":"2505.22408","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-28T14:37:57Z","cross_cats_sorted":[],"title_canon_sha256":"a677ed3327080c804619bf86ebd6a1993ce0a2ac1f9449a954a41971b0d80fd6","abstract_canon_sha256":"46f7d414cb4a1d96fd170b9b8b95d1759f9e9f4b77efce9c3f7c6efb3c01e9e5"},"schema_version":"1.0"},"canonical_sha256":"fc75cb1a467bcfcbc67f2a5437a6829939274773e74edc6d148fcae1df6b8d13","source":{"kind":"arxiv","id":"2505.22408","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.22408","created_at":"2026-07-05T11:11:23Z"},{"alias_kind":"arxiv_version","alias_value":"2505.22408v1","created_at":"2026-07-05T11:11:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.22408","created_at":"2026-07-05T11:11:23Z"},{"alias_kind":"pith_short_12","alias_value":"7R24WGSGPPH4","created_at":"2026-07-05T11:11:23Z"},{"alias_kind":"pith_short_16","alias_value":"7R24WGSGPPH4XRT7","created_at":"2026-07-05T11:11:23Z"},{"alias_kind":"pith_short_8","alias_value":"7R24WGSG","created_at":"2026-07-05T11:11:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:7R24WGSGPPH4XRT7FJKDPJUCTE","target":"record","payload":{"canonical_record":{"source":{"id":"2505.22408","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-28T14:37:57Z","cross_cats_sorted":[],"title_canon_sha256":"a677ed3327080c804619bf86ebd6a1993ce0a2ac1f9449a954a41971b0d80fd6","abstract_canon_sha256":"46f7d414cb4a1d96fd170b9b8b95d1759f9e9f4b77efce9c3f7c6efb3c01e9e5"},"schema_version":"1.0"},"canonical_sha256":"fc75cb1a467bcfcbc67f2a5437a6829939274773e74edc6d148fcae1df6b8d13","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:11:23.262176Z","signature_b64":"7bhmoj7P+lgyQFn5xkvIIMrxiDK9bds/6oONUkVKs2EkvWWhjjFEMoDDC85XRyhnbczWiYy0R7hOsycneCTVDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc75cb1a467bcfcbc67f2a5437a6829939274773e74edc6d148fcae1df6b8d13","last_reissued_at":"2026-07-05T11:11:23.261693Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:11:23.261693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.22408","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":"2026-07-05T11:11:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y79/CppXXZ4A8vDJuhuSSeza/xz339eg7ngP8gDh2kmoHz6cNgac4P7jnGvjWZyI9HwGz9aWGzANZYzWUyXvCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T21:25:02.811086Z"},"content_sha256":"5e822d26468e7fa0abe3ab52d6630b48dbc2175ba7fa6d4a132962cc6d8fea28","schema_version":"1.0","event_id":"sha256:5e822d26468e7fa0abe3ab52d6630b48dbc2175ba7fa6d4a132962cc6d8fea28"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:7R24WGSGPPH4XRT7FJKDPJUCTE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Frugal Incremental Generative Modeling using Variational Autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hichem Sahbi, Victor Enescu","submitted_at":"2025-05-28T14:37:57Z","abstract_excerpt":"Continual or incremental learning holds tremendous potential in deep learning with different challenges including catastrophic forgetting. The advent of powerful foundation and generative models has propelled this paradigm even further, making it one of the most viable solution to train these models. However, one of the persisting issues lies in the increasing volume of data particularly with replay-based methods. This growth introduces challenges with scalability since continuously expanding data becomes increasingly demanding as the number of tasks grows. In this paper, we attenuate this iss"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.22408","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/2505.22408/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":"2026-07-05T11:11:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Msf1hmoMVPQkhVJxemzhmQT0uH9wBEzEcCT1IQqrS/4qHskN3X6xVwnIjSXPkR+sOqf1HL5jD+JHUxphfhgiAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T21:25:02.811660Z"},"content_sha256":"9ddc173a7147e06423b7d7b7f0cf405fb47d6b364a2f09ebc6985b907f8368c9","schema_version":"1.0","event_id":"sha256:9ddc173a7147e06423b7d7b7f0cf405fb47d6b364a2f09ebc6985b907f8368c9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7R24WGSGPPH4XRT7FJKDPJUCTE/bundle.json","state_url":"https://pith.science/pith/7R24WGSGPPH4XRT7FJKDPJUCTE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7R24WGSGPPH4XRT7FJKDPJUCTE/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":"2026-08-07T21:25:02Z","links":{"resolver":"https://pith.science/pith/7R24WGSGPPH4XRT7FJKDPJUCTE","bundle":"https://pith.science/pith/7R24WGSGPPH4XRT7FJKDPJUCTE/bundle.json","state":"https://pith.science/pith/7R24WGSGPPH4XRT7FJKDPJUCTE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7R24WGSGPPH4XRT7FJKDPJUCTE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:7R24WGSGPPH4XRT7FJKDPJUCTE","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":"46f7d414cb4a1d96fd170b9b8b95d1759f9e9f4b77efce9c3f7c6efb3c01e9e5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-28T14:37:57Z","title_canon_sha256":"a677ed3327080c804619bf86ebd6a1993ce0a2ac1f9449a954a41971b0d80fd6"},"schema_version":"1.0","source":{"id":"2505.22408","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.22408","created_at":"2026-07-05T11:11:23Z"},{"alias_kind":"arxiv_version","alias_value":"2505.22408v1","created_at":"2026-07-05T11:11:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.22408","created_at":"2026-07-05T11:11:23Z"},{"alias_kind":"pith_short_12","alias_value":"7R24WGSGPPH4","created_at":"2026-07-05T11:11:23Z"},{"alias_kind":"pith_short_16","alias_value":"7R24WGSGPPH4XRT7","created_at":"2026-07-05T11:11:23Z"},{"alias_kind":"pith_short_8","alias_value":"7R24WGSG","created_at":"2026-07-05T11:11:23Z"}],"graph_snapshots":[{"event_id":"sha256:9ddc173a7147e06423b7d7b7f0cf405fb47d6b364a2f09ebc6985b907f8368c9","target":"graph","created_at":"2026-07-05T11:11:23Z","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/2505.22408/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Continual or incremental learning holds tremendous potential in deep learning with different challenges including catastrophic forgetting. The advent of powerful foundation and generative models has propelled this paradigm even further, making it one of the most viable solution to train these models. However, one of the persisting issues lies in the increasing volume of data particularly with replay-based methods. This growth introduces challenges with scalability since continuously expanding data becomes increasingly demanding as the number of tasks grows. In this paper, we attenuate this iss","authors_text":"Hichem Sahbi, Victor Enescu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-28T14:37:57Z","title":"Frugal Incremental Generative Modeling using Variational Autoencoders"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.22408","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":"sha256:5e822d26468e7fa0abe3ab52d6630b48dbc2175ba7fa6d4a132962cc6d8fea28","target":"record","created_at":"2026-07-05T11:11:23Z","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":"46f7d414cb4a1d96fd170b9b8b95d1759f9e9f4b77efce9c3f7c6efb3c01e9e5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-28T14:37:57Z","title_canon_sha256":"a677ed3327080c804619bf86ebd6a1993ce0a2ac1f9449a954a41971b0d80fd6"},"schema_version":"1.0","source":{"id":"2505.22408","kind":"arxiv","version":1}},"canonical_sha256":"fc75cb1a467bcfcbc67f2a5437a6829939274773e74edc6d148fcae1df6b8d13","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fc75cb1a467bcfcbc67f2a5437a6829939274773e74edc6d148fcae1df6b8d13","first_computed_at":"2026-07-05T11:11:23.261693Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:11:23.261693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7bhmoj7P+lgyQFn5xkvIIMrxiDK9bds/6oONUkVKs2EkvWWhjjFEMoDDC85XRyhnbczWiYy0R7hOsycneCTVDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:11:23.262176Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.22408","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5e822d26468e7fa0abe3ab52d6630b48dbc2175ba7fa6d4a132962cc6d8fea28","sha256:9ddc173a7147e06423b7d7b7f0cf405fb47d6b364a2f09ebc6985b907f8368c9"],"state_sha256":"c1de0fe9de4f033c53fa34c9ccd9c138f666b2a0aad447f56dd67eb08b296f27"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zoqCXnthj6Cv+0+eDOMzrz7iitXdnf5KyHIV16ANfP5CQNXoUlo5ImRcC1gCCnn5bqzisZCjVTq3tEp9GucIAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T21:25:02.816715Z","bundle_sha256":"70f40d46984a7504dc54bd0c3a70605142bbed426af53d002dac4083541762e6"}}