{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:6XD5SFKNRQS4MJX5PJNANZ63O5","short_pith_number":"pith:6XD5SFKN","canonical_record":{"source":{"id":"2503.20725","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-26T17:08:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1ef197afcf925f2cde4504f12a1f76a2ca4542ee9f54c9d5e1592e3a298c13f2","abstract_canon_sha256":"3a880273c2993931441b906e1aa6f92eb3fbc2fa45c6c9e4bfa26248c997b580"},"schema_version":"1.0"},"canonical_sha256":"f5c7d9154d8c25c626fd7a5a06e7db777b42c1f0dcfa9f688e152343652bf1ed","source":{"kind":"arxiv","id":"2503.20725","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.20725","created_at":"2026-07-05T10:39:43Z"},{"alias_kind":"arxiv_version","alias_value":"2503.20725v1","created_at":"2026-07-05T10:39:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.20725","created_at":"2026-07-05T10:39:43Z"},{"alias_kind":"pith_short_12","alias_value":"6XD5SFKNRQS4","created_at":"2026-07-05T10:39:43Z"},{"alias_kind":"pith_short_16","alias_value":"6XD5SFKNRQS4MJX5","created_at":"2026-07-05T10:39:43Z"},{"alias_kind":"pith_short_8","alias_value":"6XD5SFKN","created_at":"2026-07-05T10:39:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:6XD5SFKNRQS4MJX5PJNANZ63O5","target":"record","payload":{"canonical_record":{"source":{"id":"2503.20725","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-26T17:08:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1ef197afcf925f2cde4504f12a1f76a2ca4542ee9f54c9d5e1592e3a298c13f2","abstract_canon_sha256":"3a880273c2993931441b906e1aa6f92eb3fbc2fa45c6c9e4bfa26248c997b580"},"schema_version":"1.0"},"canonical_sha256":"f5c7d9154d8c25c626fd7a5a06e7db777b42c1f0dcfa9f688e152343652bf1ed","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:39:43.165519Z","signature_b64":"InFzBRRx93jqeU9u86qmaiSatn/jnclx2s1w3w5KY6b/j7sUCSpiPE8r13R076v1sn+/AruBHfg9Qe6t0kOZBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f5c7d9154d8c25c626fd7a5a06e7db777b42c1f0dcfa9f688e152343652bf1ed","last_reissued_at":"2026-07-05T10:39:43.165016Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:39:43.165016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.20725","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":"2026-07-05T10:39:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wZPF+rhtJD3M3R1vkep5KbF3BLD5A03m2y+uQiQkLe22vII6bMXLxmlxMypIZFUFV8RkulrOo/kqAtXzeH/5AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T09:14:59.316713Z"},"content_sha256":"dc0a0932c51912f8fa0144225e821bea365a299d804837dca703375482052174","schema_version":"1.0","event_id":"sha256:dc0a0932c51912f8fa0144225e821bea365a299d804837dca703375482052174"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:6XD5SFKNRQS4MJX5PJNANZ63O5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Continual learning via probabilistic exchangeable sequence modelling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Christopher Yau, Hanwen Xing","submitted_at":"2025-03-26T17:08:20Z","abstract_excerpt":"Continual learning (CL) refers to the ability to continuously learn and accumulate new knowledge while retaining useful information from past experiences. Although numerous CL methods have been proposed in recent years, it is not straightforward to deploy them directly to real-world decision-making problems due to their computational cost and lack of uncertainty quantification. To address these issues, we propose CL-BRUNO, a probabilistic, Neural Process-based CL model that performs scalable and tractable Bayesian update and prediction. Our proposed approach uses deep-generative models to crea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.20725","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/2503.20725/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":"2026-07-05T10:39:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9Wo1HPqVeVgQ/6JvyQZ2Nj/HoG5sv2xUfShH4LXjufauzmzte2HRo/OE+Yk8GRej6PjowJIBroscgf8b9ADIAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T09:14:59.317082Z"},"content_sha256":"86d28207abda160809dd317514bd5e0f4842db06913dfc20594f8fb32b6ae1cf","schema_version":"1.0","event_id":"sha256:86d28207abda160809dd317514bd5e0f4842db06913dfc20594f8fb32b6ae1cf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6XD5SFKNRQS4MJX5PJNANZ63O5/bundle.json","state_url":"https://pith.science/pith/6XD5SFKNRQS4MJX5PJNANZ63O5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6XD5SFKNRQS4MJX5PJNANZ63O5/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":"2026-07-06T09:14:59Z","links":{"resolver":"https://pith.science/pith/6XD5SFKNRQS4MJX5PJNANZ63O5","bundle":"https://pith.science/pith/6XD5SFKNRQS4MJX5PJNANZ63O5/bundle.json","state":"https://pith.science/pith/6XD5SFKNRQS4MJX5PJNANZ63O5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6XD5SFKNRQS4MJX5PJNANZ63O5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6XD5SFKNRQS4MJX5PJNANZ63O5","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":"3a880273c2993931441b906e1aa6f92eb3fbc2fa45c6c9e4bfa26248c997b580","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-26T17:08:20Z","title_canon_sha256":"1ef197afcf925f2cde4504f12a1f76a2ca4542ee9f54c9d5e1592e3a298c13f2"},"schema_version":"1.0","source":{"id":"2503.20725","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.20725","created_at":"2026-07-05T10:39:43Z"},{"alias_kind":"arxiv_version","alias_value":"2503.20725v1","created_at":"2026-07-05T10:39:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.20725","created_at":"2026-07-05T10:39:43Z"},{"alias_kind":"pith_short_12","alias_value":"6XD5SFKNRQS4","created_at":"2026-07-05T10:39:43Z"},{"alias_kind":"pith_short_16","alias_value":"6XD5SFKNRQS4MJX5","created_at":"2026-07-05T10:39:43Z"},{"alias_kind":"pith_short_8","alias_value":"6XD5SFKN","created_at":"2026-07-05T10:39:43Z"}],"graph_snapshots":[{"event_id":"sha256:86d28207abda160809dd317514bd5e0f4842db06913dfc20594f8fb32b6ae1cf","target":"graph","created_at":"2026-07-05T10:39:43Z","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/2503.20725/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Continual learning (CL) refers to the ability to continuously learn and accumulate new knowledge while retaining useful information from past experiences. Although numerous CL methods have been proposed in recent years, it is not straightforward to deploy them directly to real-world decision-making problems due to their computational cost and lack of uncertainty quantification. To address these issues, we propose CL-BRUNO, a probabilistic, Neural Process-based CL model that performs scalable and tractable Bayesian update and prediction. Our proposed approach uses deep-generative models to crea","authors_text":"Christopher Yau, Hanwen Xing","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-26T17:08:20Z","title":"Continual learning via probabilistic exchangeable sequence modelling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.20725","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:dc0a0932c51912f8fa0144225e821bea365a299d804837dca703375482052174","target":"record","created_at":"2026-07-05T10:39:43Z","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":"3a880273c2993931441b906e1aa6f92eb3fbc2fa45c6c9e4bfa26248c997b580","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-26T17:08:20Z","title_canon_sha256":"1ef197afcf925f2cde4504f12a1f76a2ca4542ee9f54c9d5e1592e3a298c13f2"},"schema_version":"1.0","source":{"id":"2503.20725","kind":"arxiv","version":1}},"canonical_sha256":"f5c7d9154d8c25c626fd7a5a06e7db777b42c1f0dcfa9f688e152343652bf1ed","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f5c7d9154d8c25c626fd7a5a06e7db777b42c1f0dcfa9f688e152343652bf1ed","first_computed_at":"2026-07-05T10:39:43.165016Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:39:43.165016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"InFzBRRx93jqeU9u86qmaiSatn/jnclx2s1w3w5KY6b/j7sUCSpiPE8r13R076v1sn+/AruBHfg9Qe6t0kOZBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:39:43.165519Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.20725","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dc0a0932c51912f8fa0144225e821bea365a299d804837dca703375482052174","sha256:86d28207abda160809dd317514bd5e0f4842db06913dfc20594f8fb32b6ae1cf"],"state_sha256":"bbfb7300eed10ccb341662ad5ba4317b3a1471a89b7fcd6ccca865a9e97d5655"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KFAiuaY0QHw8/iqwrM2m+hBElDqoWhYTcDNUGDwn7BqeR+BYhRN3IiO8e1hr4Y+ekXYBCtkdKu8DWmFo+hkpAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T09:14:59.319093Z","bundle_sha256":"3bcce29dd97cec8982934a97b193a3eae7556167c6515d4584f276085ced929a"}}