{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:R3LCBVNSYMFCTBEN6KP75LZBBQ","short_pith_number":"pith:R3LCBVNS","canonical_record":{"source":{"id":"2305.16261","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-05-25T17:15:00Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"1e57677e5e74de4d0617005745226422cff88deab59f136bfc3eaa69501f0f97","abstract_canon_sha256":"28058fc24882cfe3240f617bc0733e39731b4da7e3280d6b5caf842672332093"},"schema_version":"1.0"},"canonical_sha256":"8ed620d5b2c30a29848df29ffeaf210c05b6cd66ad854decfd2bfb7d24e4a944","source":{"kind":"arxiv","id":"2305.16261","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.16261","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"arxiv_version","alias_value":"2305.16261v2","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16261","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"pith_short_12","alias_value":"R3LCBVNSYMFC","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"pith_short_16","alias_value":"R3LCBVNSYMFCTBEN","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"pith_short_8","alias_value":"R3LCBVNS","created_at":"2026-07-05T07:06:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:R3LCBVNSYMFCTBEN6KP75LZBBQ","target":"record","payload":{"canonical_record":{"source":{"id":"2305.16261","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-05-25T17:15:00Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"1e57677e5e74de4d0617005745226422cff88deab59f136bfc3eaa69501f0f97","abstract_canon_sha256":"28058fc24882cfe3240f617bc0733e39731b4da7e3280d6b5caf842672332093"},"schema_version":"1.0"},"canonical_sha256":"8ed620d5b2c30a29848df29ffeaf210c05b6cd66ad854decfd2bfb7d24e4a944","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:06:27.860079Z","signature_b64":"1Ped+HiZ9qc8iy4uyL/XYQ/HrVLE567F9fnHKQLKjQgN6G8CwhCF3kEwfXl8rw9MZrtZxnEiGPK3SyfDmpqDAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ed620d5b2c30a29848df29ffeaf210c05b6cd66ad854decfd2bfb7d24e4a944","last_reissued_at":"2026-07-05T07:06:27.859227Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:06:27.859227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.16261","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":"2026-07-05T07:06:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zPYb5kHZCw40cAVE7D6l6gzue1xkPmPw+lCafdeFOqVGY3UX5x1P6XbCWHmCGGgfdFuyjKoaHGJKde2wHrwaAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T17:38:25.682841Z"},"content_sha256":"bbc76aefcc80724961158f5b3c30f2987f311f36b83fcadbc67ec4baeb17342c","schema_version":"1.0","event_id":"sha256:bbc76aefcc80724961158f5b3c30f2987f311f36b83fcadbc67ec4baeb17342c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:R3LCBVNSYMFCTBEN6KP75LZBBQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Trans-Dimensional Generative Modeling via Jump Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Andrew Campbell, Arnaud Doucet, Christian Weilbach, Tom Rainforth, Valentin De Bortoli, William Harvey","submitted_at":"2023-05-25T17:15:00Z","abstract_excerpt":"We propose a new class of generative models that naturally handle data of varying dimensionality by jointly modeling the state and dimension of each datapoint. The generative process is formulated as a jump diffusion process that makes jumps between different dimensional spaces. We first define a dimension destroying forward noising process, before deriving the dimension creating time-reversed generative process along with a novel evidence lower bound training objective for learning to approximate it. Simulating our learned approximation to the time-reversed generative process then provides an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16261","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/2305.16261/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":"2026-07-05T07:06:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"elgeBxTUy/TC1CNtYZfWKvFvIpEiVjmh6V2CKsNQHJFig4TZAvVpls0c4Jg5Y+lRf7SCItFaoosmiD3ptpfNCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T17:38:25.683774Z"},"content_sha256":"6564d90b1d3f02cbfff30202dde14e185afcfc08fed0bfab0e11e8df3922dfbe","schema_version":"1.0","event_id":"sha256:6564d90b1d3f02cbfff30202dde14e185afcfc08fed0bfab0e11e8df3922dfbe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R3LCBVNSYMFCTBEN6KP75LZBBQ/bundle.json","state_url":"https://pith.science/pith/R3LCBVNSYMFCTBEN6KP75LZBBQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R3LCBVNSYMFCTBEN6KP75LZBBQ/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":"2026-08-09T17:38:25Z","links":{"resolver":"https://pith.science/pith/R3LCBVNSYMFCTBEN6KP75LZBBQ","bundle":"https://pith.science/pith/R3LCBVNSYMFCTBEN6KP75LZBBQ/bundle.json","state":"https://pith.science/pith/R3LCBVNSYMFCTBEN6KP75LZBBQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R3LCBVNSYMFCTBEN6KP75LZBBQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:R3LCBVNSYMFCTBEN6KP75LZBBQ","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":"28058fc24882cfe3240f617bc0733e39731b4da7e3280d6b5caf842672332093","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-05-25T17:15:00Z","title_canon_sha256":"1e57677e5e74de4d0617005745226422cff88deab59f136bfc3eaa69501f0f97"},"schema_version":"1.0","source":{"id":"2305.16261","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.16261","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"arxiv_version","alias_value":"2305.16261v2","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16261","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"pith_short_12","alias_value":"R3LCBVNSYMFC","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"pith_short_16","alias_value":"R3LCBVNSYMFCTBEN","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"pith_short_8","alias_value":"R3LCBVNS","created_at":"2026-07-05T07:06:27Z"}],"graph_snapshots":[{"event_id":"sha256:6564d90b1d3f02cbfff30202dde14e185afcfc08fed0bfab0e11e8df3922dfbe","target":"graph","created_at":"2026-07-05T07:06:27Z","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/2305.16261/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a new class of generative models that naturally handle data of varying dimensionality by jointly modeling the state and dimension of each datapoint. The generative process is formulated as a jump diffusion process that makes jumps between different dimensional spaces. We first define a dimension destroying forward noising process, before deriving the dimension creating time-reversed generative process along with a novel evidence lower bound training objective for learning to approximate it. Simulating our learned approximation to the time-reversed generative process then provides an","authors_text":"Andrew Campbell, Arnaud Doucet, Christian Weilbach, Tom Rainforth, Valentin De Bortoli, William Harvey","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-05-25T17:15:00Z","title":"Trans-Dimensional Generative Modeling via Jump Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16261","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:bbc76aefcc80724961158f5b3c30f2987f311f36b83fcadbc67ec4baeb17342c","target":"record","created_at":"2026-07-05T07:06:27Z","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":"28058fc24882cfe3240f617bc0733e39731b4da7e3280d6b5caf842672332093","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-05-25T17:15:00Z","title_canon_sha256":"1e57677e5e74de4d0617005745226422cff88deab59f136bfc3eaa69501f0f97"},"schema_version":"1.0","source":{"id":"2305.16261","kind":"arxiv","version":2}},"canonical_sha256":"8ed620d5b2c30a29848df29ffeaf210c05b6cd66ad854decfd2bfb7d24e4a944","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ed620d5b2c30a29848df29ffeaf210c05b6cd66ad854decfd2bfb7d24e4a944","first_computed_at":"2026-07-05T07:06:27.859227Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:06:27.859227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1Ped+HiZ9qc8iy4uyL/XYQ/HrVLE567F9fnHKQLKjQgN6G8CwhCF3kEwfXl8rw9MZrtZxnEiGPK3SyfDmpqDAg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:06:27.860079Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.16261","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bbc76aefcc80724961158f5b3c30f2987f311f36b83fcadbc67ec4baeb17342c","sha256:6564d90b1d3f02cbfff30202dde14e185afcfc08fed0bfab0e11e8df3922dfbe"],"state_sha256":"c09bc7fbf3ebe6e21cb1e0e1dba300b988463c4111c9bda8e78221abb0cd1486"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oSyg1yiQ5rX8a+JgHuml1sgBNm5kfIRN6kg6N5G/DsZHMF0xGAM8Q+G+6dlkbRF5u327AGoAMD41w9nntEXBCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T17:38:25.691311Z","bundle_sha256":"9b7f67394673b940fde2e016d40782a3e8b388d4f27ea11f34ac13bafb13a431"}}