{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:7YVMDPYI5CPP6C567MVDJBQXEB","short_pith_number":"pith:7YVMDPYI","canonical_record":{"source":{"id":"2607.14320","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2026-07-15T19:32:58Z","cross_cats_sorted":[],"title_canon_sha256":"1869bfec01a40e5d1452220bb862f9d028754978c0b6c8b7a44c893b475ba501","abstract_canon_sha256":"3c195cfa3b9cdeac066dd1cc7b6d372469989708a3f43c3179e069ef88e42d08"},"schema_version":"1.0"},"canonical_sha256":"fe2ac1bf08e89eff0bbefb2a348617206897e9134081627bd5bb47e2a3c588a4","source":{"kind":"arxiv","id":"2607.14320","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.14320","created_at":"2026-07-17T00:21:05Z"},{"alias_kind":"arxiv_version","alias_value":"2607.14320v1","created_at":"2026-07-17T00:21:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.14320","created_at":"2026-07-17T00:21:05Z"},{"alias_kind":"pith_short_12","alias_value":"7YVMDPYI5CPP","created_at":"2026-07-17T00:21:05Z"},{"alias_kind":"pith_short_16","alias_value":"7YVMDPYI5CPP6C56","created_at":"2026-07-17T00:21:05Z"},{"alias_kind":"pith_short_8","alias_value":"7YVMDPYI","created_at":"2026-07-17T00:21:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:7YVMDPYI5CPP6C567MVDJBQXEB","target":"record","payload":{"canonical_record":{"source":{"id":"2607.14320","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2026-07-15T19:32:58Z","cross_cats_sorted":[],"title_canon_sha256":"1869bfec01a40e5d1452220bb862f9d028754978c0b6c8b7a44c893b475ba501","abstract_canon_sha256":"3c195cfa3b9cdeac066dd1cc7b6d372469989708a3f43c3179e069ef88e42d08"},"schema_version":"1.0"},"canonical_sha256":"fe2ac1bf08e89eff0bbefb2a348617206897e9134081627bd5bb47e2a3c588a4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-17T00:21:05.304712Z","signature_b64":"XU+jvNkGIxuL1vAo/q4qjZIXRTJjctHML24TjfL7dSOOAIH0vLptkMZ+GLbjXi5rbo4hHNjDDOkLs58dwMnSAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe2ac1bf08e89eff0bbefb2a348617206897e9134081627bd5bb47e2a3c588a4","last_reissued_at":"2026-07-17T00:21:05.303862Z","signature_status":"signed_v1","first_computed_at":"2026-07-17T00:21:05.303862Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.14320","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":"2026-07-17T00:21:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8w85h9Z5NQlNIdimi23oF0OknWDbBfEoYgU4oDWbkxbqw4RV2pyoV7BjKrhtKE8fgARPiixFZCipkhEexClqDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T08:09:11.315103Z"},"content_sha256":"138eb47579f5731d1d1c3048ee18bb5844bbdf20a47cbffe37507bba5ec775be","schema_version":"1.0","event_id":"sha256:138eb47579f5731d1d1c3048ee18bb5844bbdf20a47cbffe37507bba5ec775be"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:7YVMDPYI5CPP6C567MVDJBQXEB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Ge Wang, Jianxu Wang, Kang Chen, Mahmud Wasif Nafee, Wenjun Xia","submitted_at":"2026-07-15T19:32:58Z","abstract_excerpt":"Interior tomography reconstructs a region of interest (ROI) from truncated projection measurements. However, projection truncation makes the inverse problem severely ill-posed, leading to non-unique solutions, oversmoothing, and truncation-induced artifacts when conventional reconstruction methods are directly applied to interior tomography. Existing learning-based interior CT methods have shown promising performance, but their generalization across different truncation patterns, ROI sizes, and noise levels remains an important challenge. Meanwhile, current generative model-based reconstructio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.14320","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/2607.14320/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":"2026-07-17T00:21:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RjVugT3jihRmUI7/RX3Ooo5E7wvjz1mq7yUdwgdCLQi5iSiHqGzL1OIeXt5yGTDhm0zTl+fi1cs32kKKBHgaDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T08:09:11.316346Z"},"content_sha256":"745e02ae42969c2268d6a7b5d997ff1557d8818867dce531411b9d388f201179","schema_version":"1.0","event_id":"sha256:745e02ae42969c2268d6a7b5d997ff1557d8818867dce531411b9d388f201179"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:7YVMDPYI5CPP6C567MVDJBQXEB","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1088/0031-9155/56/18/011) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Z. Tian, X. Jia, K. Yuan, T. Pan, and S. B. Jiang, “Low-dose ct reconstruction via edge-preserving total variation regularization,” Physics in Medicine and Biology, vol. 56, no. 18, p. 5949–5967, Aug. 2011. [Online]. Available: http://dx.do","arxiv_id":"2607.14320","detector":"doi_compliance","evidence":{"ref_index":15,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1088/0031-","reconstructed_doi":"10.1088/0031-9155/56/18/011"},"severity":"advisory","ref_index":15,"audited_at":"2026-08-02T02:39:46.166612Z","event_type":"pith.integrity.v1","detected_doi":"10.1088/0031-9155/56/18/011","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"081f7062154ede7a0e15a3a4f1388fc5466281f6e26e9296f5f599b198fb55c7","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":17074,"payload_sha256":"f633f3c36ec70adf18cf2dcb21c2714bf272fa1b3b1939d70c37e5b910882d83","signature_b64":"ewXLvxVCL7Pv8kibMMXz58KbDpR3L5IxnMjpMrRYvWu+nyIY6p3A66AFYs1TfCdVcI82iiLFRKeIMP7w48MWBA==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-02T02:43:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W13wK5993U8nsg2tVZ8iWZDDPYzWyvfwadVSJHyyi1Xb1UkrpZXBQdXgnb0Z0KAJvhtUb7FMTCPoOZ+foHnfAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T08:09:11.321441Z"},"content_sha256":"42c0fc7e80f5a5ecc1c1197cc3830cf0e4d6ab493c955e1761dcce7e893e90e2","schema_version":"1.0","event_id":"sha256:42c0fc7e80f5a5ecc1c1197cc3830cf0e4d6ab493c955e1761dcce7e893e90e2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7YVMDPYI5CPP6C567MVDJBQXEB/bundle.json","state_url":"https://pith.science/pith/7YVMDPYI5CPP6C567MVDJBQXEB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7YVMDPYI5CPP6C567MVDJBQXEB/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":"2026-08-05T08:09:11Z","links":{"resolver":"https://pith.science/pith/7YVMDPYI5CPP6C567MVDJBQXEB","bundle":"https://pith.science/pith/7YVMDPYI5CPP6C567MVDJBQXEB/bundle.json","state":"https://pith.science/pith/7YVMDPYI5CPP6C567MVDJBQXEB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7YVMDPYI5CPP6C567MVDJBQXEB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:7YVMDPYI5CPP6C567MVDJBQXEB","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3c195cfa3b9cdeac066dd1cc7b6d372469989708a3f43c3179e069ef88e42d08","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2026-07-15T19:32:58Z","title_canon_sha256":"1869bfec01a40e5d1452220bb862f9d028754978c0b6c8b7a44c893b475ba501"},"schema_version":"1.0","source":{"id":"2607.14320","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.14320","created_at":"2026-07-17T00:21:05Z"},{"alias_kind":"arxiv_version","alias_value":"2607.14320v1","created_at":"2026-07-17T00:21:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.14320","created_at":"2026-07-17T00:21:05Z"},{"alias_kind":"pith_short_12","alias_value":"7YVMDPYI5CPP","created_at":"2026-07-17T00:21:05Z"},{"alias_kind":"pith_short_16","alias_value":"7YVMDPYI5CPP6C56","created_at":"2026-07-17T00:21:05Z"},{"alias_kind":"pith_short_8","alias_value":"7YVMDPYI","created_at":"2026-07-17T00:21:05Z"}],"graph_snapshots":[{"event_id":"sha256:745e02ae42969c2268d6a7b5d997ff1557d8818867dce531411b9d388f201179","target":"graph","created_at":"2026-07-17T00:21:05Z","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/2607.14320/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Interior tomography reconstructs a region of interest (ROI) from truncated projection measurements. However, projection truncation makes the inverse problem severely ill-posed, leading to non-unique solutions, oversmoothing, and truncation-induced artifacts when conventional reconstruction methods are directly applied to interior tomography. Existing learning-based interior CT methods have shown promising performance, but their generalization across different truncation patterns, ROI sizes, and noise levels remains an important challenge. Meanwhile, current generative model-based reconstructio","authors_text":"Ge Wang, Jianxu Wang, Kang Chen, Mahmud Wasif Nafee, Wenjun Xia","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2026-07-15T19:32:58Z","title":"FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.14320","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":"sha256:138eb47579f5731d1d1c3048ee18bb5844bbdf20a47cbffe37507bba5ec775be","target":"record","created_at":"2026-07-17T00:21:05Z","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":"3c195cfa3b9cdeac066dd1cc7b6d372469989708a3f43c3179e069ef88e42d08","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2026-07-15T19:32:58Z","title_canon_sha256":"1869bfec01a40e5d1452220bb862f9d028754978c0b6c8b7a44c893b475ba501"},"schema_version":"1.0","source":{"id":"2607.14320","kind":"arxiv","version":1}},"canonical_sha256":"fe2ac1bf08e89eff0bbefb2a348617206897e9134081627bd5bb47e2a3c588a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fe2ac1bf08e89eff0bbefb2a348617206897e9134081627bd5bb47e2a3c588a4","first_computed_at":"2026-07-17T00:21:05.303862Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-17T00:21:05.303862Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XU+jvNkGIxuL1vAo/q4qjZIXRTJjctHML24TjfL7dSOOAIH0vLptkMZ+GLbjXi5rbo4hHNjDDOkLs58dwMnSAA==","signature_status":"signed_v1","signed_at":"2026-07-17T00:21:05.304712Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.14320","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:138eb47579f5731d1d1c3048ee18bb5844bbdf20a47cbffe37507bba5ec775be","sha256:745e02ae42969c2268d6a7b5d997ff1557d8818867dce531411b9d388f201179","sha256:42c0fc7e80f5a5ecc1c1197cc3830cf0e4d6ab493c955e1761dcce7e893e90e2"],"state_sha256":"fb7cc5440e747d3a5f1643a6d8b7f3a067184c8d2e51e834cee88343bba701bb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SWXPm+0ptUONWEEbcTtdRaewb9C2SW4fYCk47vhMJ0t0cIxk0vPpai1CkCy2D3KWUy48PRwHt/Wy7t4KTuNnAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T08:09:11.324237Z","bundle_sha256":"35e1f16b9d9073c0beb033d4b5c915b116abbc200a978695979503ff9c90ad2c"}}