{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","short_pith_number":"pith:5MCWIDCZ","canonical_record":{"source":{"id":"2607.18466","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-20T19:35:24Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"372165e7468f7c446e456b9d599ad1fd124a4b5b09b6224b1c11cc1594acfafc","abstract_canon_sha256":"1df5f2727515b511661f3758a098672547e86a32da8de1082eb24053d3040a76"},"schema_version":"1.0"},"canonical_sha256":"eb05640c5934190a32d42cb1cd4c59f6890b25175d7e222e33b3775ac8c23818","source":{"kind":"arxiv","id":"2607.18466","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.18466","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"arxiv_version","alias_value":"2607.18466v1","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.18466","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"pith_short_12","alias_value":"5MCWIDCZGQMQ","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"pith_short_16","alias_value":"5MCWIDCZGQMQUMWU","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"pith_short_8","alias_value":"5MCWIDCZ","created_at":"2026-07-22T00:22:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"record","payload":{"canonical_record":{"source":{"id":"2607.18466","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-20T19:35:24Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"372165e7468f7c446e456b9d599ad1fd124a4b5b09b6224b1c11cc1594acfafc","abstract_canon_sha256":"1df5f2727515b511661f3758a098672547e86a32da8de1082eb24053d3040a76"},"schema_version":"1.0"},"canonical_sha256":"eb05640c5934190a32d42cb1cd4c59f6890b25175d7e222e33b3775ac8c23818","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-22T00:22:50.014428Z","signature_b64":"fzWA6sUqbQz7oKHVGJPIKzNhw4zHOk5dYuh0YFpYXywgWBJJ0Qh4fwQVIApZbdU4uEZ+LAdxfiD2Hg5px6JnAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb05640c5934190a32d42cb1cd4c59f6890b25175d7e222e33b3775ac8c23818","last_reissued_at":"2026-07-22T00:22:50.013576Z","signature_status":"signed_v1","first_computed_at":"2026-07-22T00:22:50.013576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.18466","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":"2026-07-22T00:22:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DbIZzggzsG8P00vNskIlJz02+Vn9LziosZKfgG0dKxBPsf9qkxan5jF0aTvD+yGbXiFwhBstW28ydszIkf9PBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.870275Z"},"content_sha256":"0fcfe53f3b3b3731d947305b28530ae68cb1cdd6cbde1f34ee64c42abcf4d852","schema_version":"1.0","event_id":"sha256:0fcfe53f3b3b3731d947305b28530ae68cb1cdd6cbde1f34ee64c42abcf4d852"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ECoNGS: Efficient Compressive Neural Gaussian Splats for Volume Visualization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Chaoli Wang, Kaiyuan Tang","submitted_at":"2026-07-20T19:35:24Z","abstract_excerpt":"Recent advances in differentiable Gaussian splatting have highlighted the potential of primitive-based approaches as alternative scene representations for interactive, high-quality, volume visualization (VolVis) of large datasets. However, the explicit nature of current primitive-based methods, combined with isolated optimization for each VolVis scene, results in redundant, non-compact representations. We present ECoNGS, an efficient compressive neural Gaussian splatting framework for VolVis scene representation. ECoNGS employs lightweight neural networks to dynamically predict implicit, edita"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.18466","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/2607.18466/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":"2026-07-22T00:22:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s6ipm3xACWi24zLJ6MJf1SzEfwNEsy2sbzbCodyh9OfpU6XRjYtr8GcVfW5Sxw21zhRVKLzyWBquyLovRWwnBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.870842Z"},"content_sha256":"6a005dfa0a458fdb19ea85373f23c508a07f91278833d79af3412e59eb64e4a0","schema_version":"1.0","event_id":"sha256:6a005dfa0a458fdb19ea85373f23c508a07f91278833d79af3412e59eb64e4a0"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"integrity","payload":{"note":"Identifier '10.1007/978-3-319-10599-4_72' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Z. Zhang, P. Luo, C. C. Loy, and X. Tang. Facial landmark detection by deep multi-task learning. InProceedings of European Conference on Computer Vision, pp. 94–108, 2014. doi:10.1007/978-3-319-10599-4_72","arxiv_id":"2607.18466","detector":"doi_compliance","evidence":{"doi":"10.1007/978-3-319-10599-4_72","arxiv_id":null,"ref_index":65,"raw_excerpt":"Z. Zhang, P. Luo, C. C. Loy, and X. Tang. Facial landmark detection by deep multi-task learning. InProceedings of European Conference on Computer Vision, pp. 94–108, 2014. doi:10.1007/978-3-319-10599-4_72","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":65,"audited_at":"2026-08-01T15:29:47.380510Z","event_type":"pith.integrity.v1","detected_doi":"10.1007/978-3-319-10599-4_72","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"c52563528a204f5b599096cdc49fd5ce05af8ac0f3afa315557fb566cb9a1a08","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":16110,"payload_sha256":"0820966547694a5f84d94e328359a8249a5409f3caeff8be9e8d6be3ac80aa65","signature_b64":"cUr/iTO8WmNCIJ7/0C/wNe1SMj0pQTtXVJGH78HlZy7E4kX22/svXzNng1bwJQFUVSD1WT/ln47GFnMwcJu3Aw==","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":"2026-08-01T15:33:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dfKu91nV2XMM6QOSrS488xcUGOD3H0gYroMBTSfOu6lj5ZuFgx9waaKSNUtV7DBq2OmvK7EAi/GgvgQw0gSEBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.876002Z"},"content_sha256":"6aeef358b26402c97d6ade57ad9aa787fba4594f8082e84bdfa76a7f010db558","schema_version":"1.0","event_id":"sha256:6aeef358b26402c97d6ade57ad9aa787fba4594f8082e84bdfa76a7f010db558"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"integrity","payload":{"note":"Identifier '10.48550/arxiv.2450.20721' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Y . Wang, Z. Li, L. Guo, W. Yang, A. C. Kot, and B. Wen. ContextGS: Compact 3D Gaussian splatting with anchor level context model. In Proceedings of Advances in Neural Information Processing Systems, 2024. doi:10.48550/arXiv.2450.207212","arxiv_id":"2607.18466","detector":"doi_compliance","evidence":{"doi":"10.48550/arxiv.2450.20721","arxiv_id":null,"ref_index":54,"raw_excerpt":"Y . Wang, Z. Li, L. Guo, W. Yang, A. C. Kot, and B. Wen. ContextGS: Compact 3D Gaussian splatting with anchor level context model. In Proceedings of Advances in Neural Information Processing Systems, 2024. doi:10.48550/arXiv.2450.207212","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":54,"audited_at":"2026-08-01T15:29:47.380510Z","event_type":"pith.integrity.v1","detected_doi":"10.48550/arxiv.2450.20721","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"1f6d8ed31b9a47c9b78c8ecf2e3631a6d420b027ac9aa15d7795360765502f7f","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":16109,"payload_sha256":"9557d77911af9434df1467cf9b374afc84b70baa21fe267f792f3f8e5cba312b","signature_b64":"0zjpuDyOhDO1f2VqYF67moeSY/v7ZDQUrET/eHPV4ehnMXSjC5SfGG9uXGZBk6bbGJKaF6YMuGYgYAShsLTrDg==","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":"2026-08-01T15:33:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3Vn8ztIegfYOCcEKSjsG5aWYuoyV2jW4YojpvEekiaR7STtX8Fb5qlizbh361laRI/Q4nof7+zFAdMZI4I72Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.876623Z"},"content_sha256":"eec12cfe5f968a3d83b20a0e0249e5f18d5638ecc291a0ca498b465ecf3c202b","schema_version":"1.0","event_id":"sha256:eec12cfe5f968a3d83b20a0e0249e5f18d5638ecc291a0ca498b465ecf3c202b"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"integrity","payload":{"note":"Identifier '10.1109/tvcg.2022' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"C. Wang and J. Han. DL4SciVis: A state-of-the-art survey on deep learning for scientific visualization.IEEE Transactions on Visualization and Computer Graphics, 29(8):3714–3733, 2023. doi: 10.1109/TVCG.2022. 31678961","arxiv_id":"2607.18466","detector":"doi_compliance","evidence":{"doi":"10.1109/tvcg.2022","arxiv_id":null,"ref_index":52,"raw_excerpt":"C. Wang and J. Han. DL4SciVis: A state-of-the-art survey on deep learning for scientific visualization.IEEE Transactions on Visualization and Computer Graphics, 29(8):3714–3733, 2023. doi: 10.1109/TVCG.2022. 31678961","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":52,"audited_at":"2026-08-01T15:29:47.380510Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/tvcg.2022","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"b686c80ee8595c1530f635766bdc91402ef997d328e25b55c955aad2ab5d8b8e","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":16108,"payload_sha256":"203891f5fd57c808fca1e1ad27207244201efeebf73c90a2a0f5921c5772ae70","signature_b64":"rEgqS3SdZl9xZHWy2XiFmfHdOMB7dzn8snh6ei/7C4Iy4TEAqNeB3bK3oVj09pkZSmI8jtQ6mTLPkSOWAWRcDw==","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":"2026-08-01T15:33:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vWmhZm6OjGkQ1TrU8UErJmBKbh7nbe90TcPePysl8TPU8IbYLvoDw0NTgOtGF/bAtkFyvjY147hKJKr3chbnDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.877056Z"},"content_sha256":"974a134d4ae73808839ff73c76763ade022a98fb29a6e4f3424bb19eee644db7","schema_version":"1.0","event_id":"sha256:974a134d4ae73808839ff73c76763ade022a98fb29a6e4f3424bb19eee644db7"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"integrity","payload":{"note":"Identifier '10.1016/j.cag.2024' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"K. Tang and C. Wang. STSR-INR: Spatiotemporal super-resolution for time-varying multivariate volumetric data via implicit neural representa- tion.Computers & Graphics, 119:103874, 2024. doi: 10.1016/j.cag.2024. 01.0012","arxiv_id":"2607.18466","detector":"doi_compliance","evidence":{"doi":"10.1016/j.cag.2024","arxiv_id":null,"ref_index":48,"raw_excerpt":"K. Tang and C. Wang. STSR-INR: Spatiotemporal super-resolution for time-varying multivariate volumetric data via implicit neural representa- tion.Computers & Graphics, 119:103874, 2024. doi: 10.1016/j.cag.2024. 01.0012","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":48,"audited_at":"2026-08-01T15:29:47.380510Z","event_type":"pith.integrity.v1","detected_doi":"10.1016/j.cag.2024","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"cf967adf4c21ebee75e4409c3d99be17e5965f0560f4f4fe997549558bfe2ad0","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":16107,"payload_sha256":"fff700f6b428283a921fc23785edbb553ff3afe17fb62631c0666956b3ad6293","signature_b64":"SpRHk5foILktYlqVdYrpP3KYgkbRRnPl2JXRDqSYAOkioHnQFJQ7WM0oIxj7EqpWy2kyLoeO5z1HlVRWQlsyDA==","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":"2026-08-01T15:33:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JAl0VtQQm7OxppvqYzHbMpGacgm/oVunB6zAxZD+nY8s9aMKkQgiQsus/6TNI3jMKauZYPT3XyhKmTp6uJEpBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.877455Z"},"content_sha256":"31afda08631b41bfc33e6840eb27fab05ab2fbb40f0f83f7c6a5799f9a6d2bbe","schema_version":"1.0","event_id":"sha256:31afda08631b41bfc33e6840eb27fab05ab2fbb40f0f83f7c6a5799f9a6d2bbe"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"integrity","payload":{"note":"Identifier '10.1007/978-3-031-72627-9_182' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"doi:10.1007/978-3-031-72627-9_182, 3","arxiv_id":"2607.18466","detector":"doi_compliance","evidence":{"doi":"10.1007/978-3-031-72627-9_182","arxiv_id":null,"ref_index":39,"raw_excerpt":"doi:10.1007/978-3-031-72627-9_182, 3","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":39,"audited_at":"2026-08-01T15:29:47.380510Z","event_type":"pith.integrity.v1","detected_doi":"10.1007/978-3-031-72627-9_182","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"74f11b5a2a0b688eb6a9a55f914f13934bb8e2ab29cf9b195488a310fef53631","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":16106,"payload_sha256":"b9b1c011d87dd01f95821ae54e57dbbda2c6240c35e20099fa47545a3f8076fa","signature_b64":"Tho6GBpOXAOKWK6t7qUrY3iWE9AXFnQDS+biehEEZo8ShbphL0VgSVUc71rF7VGOa0A/m6pA5fTQV7OyOIA+Bw==","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":"2026-08-01T15:33:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LyF8dCHy6Ndy1TDrNaekWo0xrPr/w2VTfYILLMASXM5/doosjd2x6IAwcdpApXXY1kA3zPeThvuiQSHaWvPqAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.877840Z"},"content_sha256":"1f0ea26e66ef7a0a59a0e2a9d18cc984db77314a526dc33ff5920369b098c093","schema_version":"1.0","event_id":"sha256:1f0ea26e66ef7a0a59a0e2a9d18cc984db77314a526dc33ff5920369b098c093"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"integrity","payload":{"note":"Identifier '10.1016/j.cag.2004.08.0142' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"M. Sainz and R. Pajarola. Point-based rendering techniques.Computers & Graphics, 28(6):869–879, 2004. doi:10.1016/j.cag.2004.08.0142","arxiv_id":"2607.18466","detector":"doi_compliance","evidence":{"doi":"10.1016/j.cag.2004.08.0142","arxiv_id":null,"ref_index":38,"raw_excerpt":"M. Sainz and R. Pajarola. Point-based rendering techniques.Computers & Graphics, 28(6):869–879, 2004. doi:10.1016/j.cag.2004.08.0142","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":38,"audited_at":"2026-08-01T15:29:47.380510Z","event_type":"pith.integrity.v1","detected_doi":"10.1016/j.cag.2004.08.0142","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"70c942e092440770ccedd1b50b1f0bc6af6a908319b5035fab51046622d393b4","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":16105,"payload_sha256":"4a5d48506cec6da6f19f6bc4099a927d174350488bc042233b21d882643cb745","signature_b64":"hDRqBWYNCCfJCn03VIzzmkILRq3GMS+DvneTsThPkWOg3I1UtBmwyzTG7b2IO0OyIxFZOwCrzDvjiHYbKpO7CA==","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":"2026-08-01T15:33:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j+HKNgsLkczEQZU8zySZ5DPdH7jS8tyAK5AtbffdsudfbCqGSWMEvh61DuC3nNdEv0z9rvbeMfjwxnbX7/ADDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.878240Z"},"content_sha256":"8196487c5e82ca46bc32c9420e3c06127cb71063c6bb075ed560007254754d89","schema_version":"1.0","event_id":"sha256:8196487c5e82ca46bc32c9420e3c06127cb71063c6bb075ed560007254754d89"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"integrity","payload":{"note":"Identifier '10.1007/978-3-030-58452-8_241' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. InProceedings of European Conference on Computer Vision, pp. 405–421, 2020. doi:10.","arxiv_id":"2607.18466","detector":"doi_compliance","evidence":{"doi":"10.1007/978-3-030-58452-8_241","arxiv_id":null,"ref_index":35,"raw_excerpt":"B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. InProceedings of European Conference on Computer Vision, pp. 405–421, 2020. doi:10.1007/978-3-030-58452-8_241, 3","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":35,"audited_at":"2026-08-01T15:29:47.380510Z","event_type":"pith.integrity.v1","detected_doi":"10.1007/978-3-030-58452-8_241","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"b61bcd1966508b3dacef31a335bd22fd7542416c889531c37a7f3a3bfadcf916","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":16104,"payload_sha256":"126e6cddd6553170e6f20081541f0d013b6d09aecab99ed493af613f83b57096","signature_b64":"pqcka+3iKD1xtoci1oSncvwEu4a5ZGzMbV/oOODSmBdStTtM5GTpHmAYFX7sX8eYl1JZd+jo6X/zjBOO55FTDA==","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":"2026-08-01T15:33:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OG94lizo6AtSpT6rnGyTkgSHvhXiB2NYOxVzDP+VNi0WD2UiSwTHDnfyrsJQZpNvKuzAPKx/MDkFNF7zP4siDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.878638Z"},"content_sha256":"e5b9651f5920949c38a657ce5e814574218b1faf33bb46809d7191baa53ef6d6","schema_version":"1.0","event_id":"sha256:e5b9651f5920949c38a657ce5e814574218b1faf33bb46809d7191baa53ef6d6"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"integrity","payload":{"note":"Identifier '10.1111/cgf.142952' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Y . Lu, K. Jiang, J. A. Levine, and M. Berger. Compressive neural represen- tations of volumetric scalar fields.Computer Graphics Forum, 40(3):135– 146, 2021. doi:10.1111/cgf.142952","arxiv_id":"2607.18466","detector":"doi_compliance","evidence":{"doi":"10.1111/cgf.142952","arxiv_id":null,"ref_index":33,"raw_excerpt":"Y . Lu, K. Jiang, J. A. Levine, and M. Berger. Compressive neural represen- tations of volumetric scalar fields.Computer Graphics Forum, 40(3):135– 146, 2021. doi:10.1111/cgf.142952","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":33,"audited_at":"2026-08-01T15:29:47.380510Z","event_type":"pith.integrity.v1","detected_doi":"10.1111/cgf.142952","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"bbced3cd3991c6295ffa813280482e7cbb11ac7cd760c7135072631e8ac35bfa","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":16103,"payload_sha256":"9369fcea528b9d233002188bb04676605c2d105269758353ec3008b61ebe1473","signature_b64":"IPfCNvfm6zMFW6bmqRrd+OhASuhG6k8hLMk7/0wX27CzfRsrXCbsTUVhz9oGJ0808Vzh0YU7wYbv6LaES1owDg==","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":"2026-08-01T15:33:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1+zy43awPV+DfTQXq9MB4N3+rh0QxL0yh4P4uLa/LEI/clhGXBgTvIcxG79UD1pwWMCmWCu5dJ65lnTdd1/TCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.879028Z"},"content_sha256":"8718fe6584a7b3edc51d9cf6e02d3c16931f30c8656be06d81bbdc1b3cf09c07","schema_version":"1.0","event_id":"sha256:8718fe6584a7b3edc51d9cf6e02d3c16931f30c8656be06d81bbdc1b3cf09c07"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"integrity","payload":{"note":"Identifier '10.1145/35924331' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis. 3D Gaussian splatting for real-time radiance field rendering.ACM Transactions on Graphics, 42(4):139:1–139:14, 2023. doi:10.1145/35924331, 2, 3, 5, 12","arxiv_id":"2607.18466","detector":"doi_compliance","evidence":{"doi":"10.1145/35924331","arxiv_id":null,"ref_index":26,"raw_excerpt":"B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis. 3D Gaussian splatting for real-time radiance field rendering.ACM Transactions on Graphics, 42(4):139:1–139:14, 2023. doi:10.1145/35924331, 2, 3, 5, 12","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":26,"audited_at":"2026-08-01T15:29:47.380510Z","event_type":"pith.integrity.v1","detected_doi":"10.1145/35924331","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"ccf2c4f93e401b6542c176f5f49122bb466b88ad41a355558a727d55035201bd","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":16102,"payload_sha256":"0a8e9b20c83c46ed3266a0e3aecffe697d9385e6777ad8565109ed9c5b13d7cf","signature_b64":"/dZo1IlmZUV6c4cFyx2nrX/PI1vDOGCDtPjQb+84FzWG3/4tPmdiMnfOucpoS0c6u0fn5R564Y5PWhix2w+cCA==","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":"2026-08-01T15:33:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eBPVHvSYQokLh3sCjLvEH/6GZ1b1sw3GD+1RBmiUbhLPlfGpFsNAz0mNNp+t4jdFd4Bpa2bZyU/PMXv/qngPCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.879469Z"},"content_sha256":"d9bfc181a4e8ae65190007e5e66edcd59a4648c72fa51c0013aaee365f0d34a8","schema_version":"1.0","event_id":"sha256:d9bfc181a4e8ae65190007e5e66edcd59a4648c72fa51c0013aaee365f0d34a8"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"integrity","payload":{"note":"Identifier '10.1109/tvcg.2020' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"J. Han and C. Wang. SSR-TVD: Spatial super-resolution for time-varying data analysis and visualization.IEEE Transactions on Visualization and Computer Graphics, 28(6):2445–2456, 2022. doi: 10.1109/TVCG.2020. 30321232","arxiv_id":"2607.18466","detector":"doi_compliance","evidence":{"doi":"10.1109/tvcg.2020","arxiv_id":null,"ref_index":18,"raw_excerpt":"J. Han and C. Wang. SSR-TVD: Spatial super-resolution for time-varying data analysis and visualization.IEEE Transactions on Visualization and Computer Graphics, 28(6):2445–2456, 2022. doi: 10.1109/TVCG.2020. 30321232","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":18,"audited_at":"2026-08-01T15:29:47.380510Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/tvcg.2020","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"5a72e9908dd24408cce604e9d68c9c27bb1ad068242d5df7a88f53ac7edc06a5","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":16101,"payload_sha256":"95f2f5f6365b07c40784de4c51cbf7358f8a694039a64453337d177f84d21753","signature_b64":"TuxFv4nUuSyn6lTLN6iVtzJki7afdIPpCTDRowCEuvUUa7nEVQvUBETNSs3C9PXxaizJQOPm4TkyHGfK7qKsAQ==","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":"2026-08-01T15:33:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XxuNHWt4VNo8vvn619tXqQ+f9xVoEA5K14XK+cfixbPHxTrvK5FbbepA3QOUufn0qTms6BPqhvafrOlkNfFfAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.879887Z"},"content_sha256":"23d0c4f20693bf1ff6665ba0d46b2188033ad1c28d6a0dbc1368c717242c8457","schema_version":"1.0","event_id":"sha256:23d0c4f20693bf1ff6665ba0d46b2188033ad1c28d6a0dbc1368c717242c8457"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"integrity","payload":{"note":"Identifier '10.1007/978-3-031-72995-9_52' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"J. Gao, C. Gu, Y . Lin, Z. Li, H. Zhu, X. Cao et al. Relightable 3D Gaussians: Realistic point cloud relighting with BRDF decomposition and ray tracing. InProceedings of European Conference on Computer Vision, pp. 73–89, 2024. doi:10.1007/9","arxiv_id":"2607.18466","detector":"doi_compliance","evidence":{"doi":"10.1007/978-3-031-72995-9_52","arxiv_id":null,"ref_index":15,"raw_excerpt":"J. Gao, C. Gu, Y . Lin, Z. Li, H. Zhu, X. Cao et al. Relightable 3D Gaussians: Realistic point cloud relighting with BRDF decomposition and ray tracing. InProceedings of European Conference on Computer Vision, pp. 73–89, 2024. doi:10.1007/978-3-031-72995-9_52","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":15,"audited_at":"2026-08-01T15:29:47.380510Z","event_type":"pith.integrity.v1","detected_doi":"10.1007/978-3-031-72995-9_52","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"2f49bc342376492a70bedf0caf4cd8102802d18e3c93f6f59e1b649271538d21","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":16100,"payload_sha256":"8a0618f23b063dc668f36771c7dd3ea9a41c22a2c9c0a62724ec1723cc0bb0e6","signature_b64":"6b6EmB8wDTpJWIWHw/U/qMWVdI6nfoIClf2cjXUhP23qYwbZ07kx4d0TDc/eTAFS+5SF7xRftd6UEqYr6ykQAQ==","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":"2026-08-01T15:33:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0Yu0VxMoEIphfSBIZdQNEANnYt25KzscYDXe3s/y5vO5Hgx5umsEn770o6libjk/DrkeZITkoeFiAKQSscAHAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.880329Z"},"content_sha256":"d203ddc7a84a41bf1fcec52838446bc012590fdf6fbe055ee76e24411d3f4f0f","schema_version":"1.0","event_id":"sha256:d203ddc7a84a41bf1fcec52838446bc012590fdf6fbe055ee76e24411d3f4f0f"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","target":"integrity","payload":{"note":"Identifier '10.1016/j.nucengdes.2018.11.00512' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Y . J. Cho and H. Y . Yoon. Numerical analysis of the ROCOM boron dilu- tion benchmark experiment using the CUPID code.Nuclear Engineering and Design, 341:167–175, 2019. doi:10.1016/j.nucengdes.2018.11.00512","arxiv_id":"2607.18466","detector":"doi_compliance","evidence":{"doi":"10.1016/j.nucengdes.2018.11.00512","arxiv_id":null,"ref_index":11,"raw_excerpt":"Y . J. Cho and H. Y . Yoon. Numerical analysis of the ROCOM boron dilu- tion benchmark experiment using the CUPID code.Nuclear Engineering and Design, 341:167–175, 2019. doi:10.1016/j.nucengdes.2018.11.00512","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":11,"audited_at":"2026-08-01T15:29:47.380510Z","event_type":"pith.integrity.v1","detected_doi":"10.1016/j.nucengdes.2018.11.00512","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"7e4f967c3722ef7efc7e7a4af2dde2b2756c89d52dbea4719cde3d0c953cb4c0","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":16099,"payload_sha256":"4d4292f5617f2bb54d4a8a699c80cf5dc970a441b120c48d41a37233c94a96f9","signature_b64":"WP8Yujln8i6xXmLrhAWtOJ4M1MjZxvSpkOib7BYLPvIGzIiwG8SJ0PS5Zw/YrIzM4CfknABTlNDJwvN/cRI3Ag==","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":"2026-08-01T15:33:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wPfoPUPENacaOWNggIUqT7MRmXRQP7ynuH/ZHv3fqvkZ/rYdiJBQyRxGj7BXxmaD7OdpxTojrVO2zk9p1XprCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:56:25.880804Z"},"content_sha256":"b5d137bfc744633cf5ef5467e0ed88706236975bf109376b15e0869237746076","schema_version":"1.0","event_id":"sha256:b5d137bfc744633cf5ef5467e0ed88706236975bf109376b15e0869237746076"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62/bundle.json","state_url":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5MCWIDCZGQMQUMWUFSY42TCZ62/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":"2026-08-08T15:56:25Z","links":{"resolver":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62","bundle":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62/bundle.json","state":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5MCWIDCZGQMQUMWUFSY42TCZ62/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","merge_version":"pith-open-graph-merge-v1","event_count":14,"valid_event_count":14,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1df5f2727515b511661f3758a098672547e86a32da8de1082eb24053d3040a76","cross_cats_sorted":["cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-20T19:35:24Z","title_canon_sha256":"372165e7468f7c446e456b9d599ad1fd124a4b5b09b6224b1c11cc1594acfafc"},"schema_version":"1.0","source":{"id":"2607.18466","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.18466","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"arxiv_version","alias_value":"2607.18466v1","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.18466","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"pith_short_12","alias_value":"5MCWIDCZGQMQ","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"pith_short_16","alias_value":"5MCWIDCZGQMQUMWU","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"pith_short_8","alias_value":"5MCWIDCZ","created_at":"2026-07-22T00:22:50Z"}],"graph_snapshots":[{"event_id":"sha256:6a005dfa0a458fdb19ea85373f23c508a07f91278833d79af3412e59eb64e4a0","target":"graph","created_at":"2026-07-22T00:22:50Z","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/2607.18466/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in differentiable Gaussian splatting have highlighted the potential of primitive-based approaches as alternative scene representations for interactive, high-quality, volume visualization (VolVis) of large datasets. However, the explicit nature of current primitive-based methods, combined with isolated optimization for each VolVis scene, results in redundant, non-compact representations. We present ECoNGS, an efficient compressive neural Gaussian splatting framework for VolVis scene representation. ECoNGS employs lightweight neural networks to dynamically predict implicit, edita","authors_text":"Chaoli Wang, Kaiyuan Tang","cross_cats":["cs.GR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-20T19:35:24Z","title":"ECoNGS: Efficient Compressive Neural Gaussian Splats for Volume Visualization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.18466","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":"sha256:0fcfe53f3b3b3731d947305b28530ae68cb1cdd6cbde1f34ee64c42abcf4d852","target":"record","created_at":"2026-07-22T00:22:50Z","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":"1df5f2727515b511661f3758a098672547e86a32da8de1082eb24053d3040a76","cross_cats_sorted":["cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-20T19:35:24Z","title_canon_sha256":"372165e7468f7c446e456b9d599ad1fd124a4b5b09b6224b1c11cc1594acfafc"},"schema_version":"1.0","source":{"id":"2607.18466","kind":"arxiv","version":1}},"canonical_sha256":"eb05640c5934190a32d42cb1cd4c59f6890b25175d7e222e33b3775ac8c23818","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eb05640c5934190a32d42cb1cd4c59f6890b25175d7e222e33b3775ac8c23818","first_computed_at":"2026-07-22T00:22:50.013576Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-22T00:22:50.013576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fzWA6sUqbQz7oKHVGJPIKzNhw4zHOk5dYuh0YFpYXywgWBJJ0Qh4fwQVIApZbdU4uEZ+LAdxfiD2Hg5px6JnAw==","signature_status":"signed_v1","signed_at":"2026-07-22T00:22:50.014428Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.18466","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:1f0ea26e66ef7a0a59a0e2a9d18cc984db77314a526dc33ff5920369b098c093","sha256:23d0c4f20693bf1ff6665ba0d46b2188033ad1c28d6a0dbc1368c717242c8457","sha256:31afda08631b41bfc33e6840eb27fab05ab2fbb40f0f83f7c6a5799f9a6d2bbe","sha256:6aeef358b26402c97d6ade57ad9aa787fba4594f8082e84bdfa76a7f010db558","sha256:8196487c5e82ca46bc32c9420e3c06127cb71063c6bb075ed560007254754d89","sha256:8718fe6584a7b3edc51d9cf6e02d3c16931f30c8656be06d81bbdc1b3cf09c07","sha256:974a134d4ae73808839ff73c76763ade022a98fb29a6e4f3424bb19eee644db7","sha256:b5d137bfc744633cf5ef5467e0ed88706236975bf109376b15e0869237746076","sha256:d203ddc7a84a41bf1fcec52838446bc012590fdf6fbe055ee76e24411d3f4f0f","sha256:d9bfc181a4e8ae65190007e5e66edcd59a4648c72fa51c0013aaee365f0d34a8","sha256:e5b9651f5920949c38a657ce5e814574218b1faf33bb46809d7191baa53ef6d6","sha256:eec12cfe5f968a3d83b20a0e0249e5f18d5638ecc291a0ca498b465ecf3c202b"]}],"invalid_events":[],"applied_event_ids":["sha256:0fcfe53f3b3b3731d947305b28530ae68cb1cdd6cbde1f34ee64c42abcf4d852","sha256:6a005dfa0a458fdb19ea85373f23c508a07f91278833d79af3412e59eb64e4a0"],"state_sha256":"5f24efa372cbeb324ee9aab048568ba5275c37339b6686d1f3941857ae52376a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SKked6mBB5PcEp8JBydn2znjwGgHrcBtdF0dC5Ipr8zRC9bl00KX6c1tAC9iVLgCoyRn0wQc2uVcKOGuuFFzAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T15:56:25.887407Z","bundle_sha256":"6e004b0c46229f651f9e4ccf2228e797fe556653fde22a59c945dfdb0eda54c1"}}