{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:YQXQLGQNR5GR7LAYLEB775UV2L","short_pith_number":"pith:YQXQLGQN","canonical_record":{"source":{"id":"2605.25177","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.NA","submitted_at":"2026-05-24T17:17:31Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"0d6b28183b22cbe25cd4faa2c22d2619d03c68df63106c92f1ca94f02cea0668","abstract_canon_sha256":"9c054515ca1fd7770344bac1f1c6f757d91e2cf14096316b9addbb4c6f4eb614"},"schema_version":"1.0"},"canonical_sha256":"c42f059a0d8f4d1fac185903fff695d2e10e26158ae01615a547f9e27320cd97","source":{"kind":"arxiv","id":"2605.25177","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.25177","created_at":"2026-05-26T02:04:21Z"},{"alias_kind":"arxiv_version","alias_value":"2605.25177v1","created_at":"2026-05-26T02:04:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.25177","created_at":"2026-05-26T02:04:21Z"},{"alias_kind":"pith_short_12","alias_value":"YQXQLGQNR5GR","created_at":"2026-05-26T02:04:21Z"},{"alias_kind":"pith_short_16","alias_value":"YQXQLGQNR5GR7LAY","created_at":"2026-05-26T02:04:21Z"},{"alias_kind":"pith_short_8","alias_value":"YQXQLGQN","created_at":"2026-05-26T02:04:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:YQXQLGQNR5GR7LAYLEB775UV2L","target":"record","payload":{"canonical_record":{"source":{"id":"2605.25177","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.NA","submitted_at":"2026-05-24T17:17:31Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"0d6b28183b22cbe25cd4faa2c22d2619d03c68df63106c92f1ca94f02cea0668","abstract_canon_sha256":"9c054515ca1fd7770344bac1f1c6f757d91e2cf14096316b9addbb4c6f4eb614"},"schema_version":"1.0"},"canonical_sha256":"c42f059a0d8f4d1fac185903fff695d2e10e26158ae01615a547f9e27320cd97","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:04:21.650001Z","signature_b64":"c39WijAmNEIGJTFGnyOVWQh9KbBCVeb4mQPCYOA/x1TIxW7e4pPMKdYfYv8S+zD4VYK2D3IRr7pAtz4MAfJKDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c42f059a0d8f4d1fac185903fff695d2e10e26158ae01615a547f9e27320cd97","last_reissued_at":"2026-05-26T02:04:21.649223Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:04:21.649223Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.25177","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":"2026-05-26T02:04:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zC2Ujl2FiHAKU4doiISwhcKB7lpBbnxktXY63+x5mMU0OrqotXeNWD59E5rKk0SoLRvWa2uk+30xMpN2DdmpDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T13:35:34.582586Z"},"content_sha256":"84c88786a7240feeac64465914ac573ec5d84ee24bf4541b88e0548f554e2b44","schema_version":"1.0","event_id":"sha256:84c88786a7240feeac64465914ac573ec5d84ee24bf4541b88e0548f554e2b44"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:YQXQLGQNR5GR7LAYLEB775UV2L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sampling Distributions as Regularization in Learned Inverse Problems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Jodi Mead, Sandra R. Babyale","submitted_at":"2026-05-24T17:17:31Z","abstract_excerpt":"Neural networks have emerged as effective tools for solving ill-posed inverse problems. In many scientific applications, however, observational training data are insufficient, and learned inverse operators must instead be trained on synthetic data generated from the forward model. This requires specifying unknown parameters in the forward model and solving the model to generate synthetic observations. Typically, the unknown parameters are sampled from a prescribed probability distribution. Here, we show that this sampling strategy is not a neutral preprocessing step, but instead defines an imp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.25177","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/2605.25177/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":"2026-05-26T02:04:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FJ5YfAHcBuvjbZ8ZepibMkqNa8m0QtztPPhGZIg7O+1c6QAczO7TER1JkpDXVOpCU4Uu2TmcF4kyoSNL+QWcDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T13:35:34.582969Z"},"content_sha256":"6ac770610dce63c13a2d52886ba2b68545ae62e736251697ba860cc549f59785","schema_version":"1.0","event_id":"sha256:6ac770610dce63c13a2d52886ba2b68545ae62e736251697ba860cc549f59785"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YQXQLGQNR5GR7LAYLEB775UV2L/bundle.json","state_url":"https://pith.science/pith/YQXQLGQNR5GR7LAYLEB775UV2L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YQXQLGQNR5GR7LAYLEB775UV2L/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":"2026-07-04T13:35:34Z","links":{"resolver":"https://pith.science/pith/YQXQLGQNR5GR7LAYLEB775UV2L","bundle":"https://pith.science/pith/YQXQLGQNR5GR7LAYLEB775UV2L/bundle.json","state":"https://pith.science/pith/YQXQLGQNR5GR7LAYLEB775UV2L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YQXQLGQNR5GR7LAYLEB775UV2L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:YQXQLGQNR5GR7LAYLEB775UV2L","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":"9c054515ca1fd7770344bac1f1c6f757d91e2cf14096316b9addbb4c6f4eb614","cross_cats_sorted":["cs.NA"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.NA","submitted_at":"2026-05-24T17:17:31Z","title_canon_sha256":"0d6b28183b22cbe25cd4faa2c22d2619d03c68df63106c92f1ca94f02cea0668"},"schema_version":"1.0","source":{"id":"2605.25177","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.25177","created_at":"2026-05-26T02:04:21Z"},{"alias_kind":"arxiv_version","alias_value":"2605.25177v1","created_at":"2026-05-26T02:04:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.25177","created_at":"2026-05-26T02:04:21Z"},{"alias_kind":"pith_short_12","alias_value":"YQXQLGQNR5GR","created_at":"2026-05-26T02:04:21Z"},{"alias_kind":"pith_short_16","alias_value":"YQXQLGQNR5GR7LAY","created_at":"2026-05-26T02:04:21Z"},{"alias_kind":"pith_short_8","alias_value":"YQXQLGQN","created_at":"2026-05-26T02:04:21Z"}],"graph_snapshots":[{"event_id":"sha256:6ac770610dce63c13a2d52886ba2b68545ae62e736251697ba860cc549f59785","target":"graph","created_at":"2026-05-26T02:04:21Z","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/2605.25177/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural networks have emerged as effective tools for solving ill-posed inverse problems. In many scientific applications, however, observational training data are insufficient, and learned inverse operators must instead be trained on synthetic data generated from the forward model. This requires specifying unknown parameters in the forward model and solving the model to generate synthetic observations. Typically, the unknown parameters are sampled from a prescribed probability distribution. Here, we show that this sampling strategy is not a neutral preprocessing step, but instead defines an imp","authors_text":"Jodi Mead, Sandra R. Babyale","cross_cats":["cs.NA"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.NA","submitted_at":"2026-05-24T17:17:31Z","title":"Sampling Distributions as Regularization in Learned Inverse Problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.25177","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":"sha256:84c88786a7240feeac64465914ac573ec5d84ee24bf4541b88e0548f554e2b44","target":"record","created_at":"2026-05-26T02:04:21Z","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":"9c054515ca1fd7770344bac1f1c6f757d91e2cf14096316b9addbb4c6f4eb614","cross_cats_sorted":["cs.NA"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.NA","submitted_at":"2026-05-24T17:17:31Z","title_canon_sha256":"0d6b28183b22cbe25cd4faa2c22d2619d03c68df63106c92f1ca94f02cea0668"},"schema_version":"1.0","source":{"id":"2605.25177","kind":"arxiv","version":1}},"canonical_sha256":"c42f059a0d8f4d1fac185903fff695d2e10e26158ae01615a547f9e27320cd97","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c42f059a0d8f4d1fac185903fff695d2e10e26158ae01615a547f9e27320cd97","first_computed_at":"2026-05-26T02:04:21.649223Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T02:04:21.649223Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"c39WijAmNEIGJTFGnyOVWQh9KbBCVeb4mQPCYOA/x1TIxW7e4pPMKdYfYv8S+zD4VYK2D3IRr7pAtz4MAfJKDw==","signature_status":"signed_v1","signed_at":"2026-05-26T02:04:21.650001Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.25177","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:84c88786a7240feeac64465914ac573ec5d84ee24bf4541b88e0548f554e2b44","sha256:6ac770610dce63c13a2d52886ba2b68545ae62e736251697ba860cc549f59785"],"state_sha256":"666b4f1cff203153f52233d2e404c378c78e343a1dc43c52f674912ec4cd264e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VrceHpVrbp4m/SoqRPdrcfKj1Bk6O8dl7YWh8RwHLoT9Je14mFRXUGdW/mo8iicuRWMbj1OHnftt8IKg97fdDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T13:35:34.585258Z","bundle_sha256":"6bd1d51ea0fa28b6e9d4a6efcfcc25c507930dc0b658b8fdeb27c4cf4f98cc16"}}