{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:JEQA333OOIT5OHTSOKO7AS7CLO","short_pith_number":"pith:JEQA333O","canonical_record":{"source":{"id":"2607.02746","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2026-07-02T20:30:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"67feb1f2750dd4c3f66bbee69d19f463f402657a4a962790c774d055c200d281","abstract_canon_sha256":"9bf62040693f82d266206769bcdeafe101994640bedaa0138f2912248389c5f8"},"schema_version":"1.0"},"canonical_sha256":"49200def6e7227d71e72729df04be25bb500465627c92bf9757b28797c29c473","source":{"kind":"arxiv","id":"2607.02746","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.02746","created_at":"2026-07-07T00:16:11Z"},{"alias_kind":"arxiv_version","alias_value":"2607.02746v1","created_at":"2026-07-07T00:16:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.02746","created_at":"2026-07-07T00:16:11Z"},{"alias_kind":"pith_short_12","alias_value":"JEQA333OOIT5","created_at":"2026-07-07T00:16:11Z"},{"alias_kind":"pith_short_16","alias_value":"JEQA333OOIT5OHTS","created_at":"2026-07-07T00:16:11Z"},{"alias_kind":"pith_short_8","alias_value":"JEQA333O","created_at":"2026-07-07T00:16:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:JEQA333OOIT5OHTSOKO7AS7CLO","target":"record","payload":{"canonical_record":{"source":{"id":"2607.02746","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2026-07-02T20:30:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"67feb1f2750dd4c3f66bbee69d19f463f402657a4a962790c774d055c200d281","abstract_canon_sha256":"9bf62040693f82d266206769bcdeafe101994640bedaa0138f2912248389c5f8"},"schema_version":"1.0"},"canonical_sha256":"49200def6e7227d71e72729df04be25bb500465627c92bf9757b28797c29c473","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T00:16:11.728488Z","signature_b64":"Zaw+gNZI0Itct3QWxW8Tzaeok8GppC57V61OusSg6ZblmDTwUc/avzQRy7lwBWUZWZjkrg07t3d//l8NjZ3/BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49200def6e7227d71e72729df04be25bb500465627c92bf9757b28797c29c473","last_reissued_at":"2026-07-07T00:16:11.727756Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T00:16:11.727756Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.02746","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":"2026-07-07T00:16:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KLqpR2UsH8tX3mMy4yhqTrcG7qq3Cxl2A7SXhRwPYnornWmxiOyrJq8o1vdCs5UE7Pg0h0dVsfRMMJ9I8t3/BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T00:20:04.389490Z"},"content_sha256":"937b41d817a32b92bd23ef58bfd71f30ddda594f37660c1cb20ccf6735d1530f","schema_version":"1.0","event_id":"sha256:937b41d817a32b92bd23ef58bfd71f30ddda594f37660c1cb20ccf6735d1530f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:JEQA333OOIT5OHTSOKO7AS7CLO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CodeJeNN: A simple C++ neural network generator for physics applications","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.comp-ph","authors_text":"Eric J Ching, Jay Arcities, Kamal Viswanath, Pavel Popov, Ryan F Johnson","submitted_at":"2026-07-02T20:30:40Z","abstract_excerpt":"Machine learning has shown speedups for numerical methods in physics applications, but integrating Python-based libraries into high-performance C++ solvers creates performance bottlenecks. We present CodeJeNN, which bridges this gap by auto-generating self-contained C++ code from trained Keras models for inference. This eliminates external dependencies through minimal inlined functions, allowing seamless integration into existing frameworks. We describe the Keras-to-C++ workflow, supported architectures, and limitations. CodeJeNN is demonstrated through inference benchmarks against Keras in ea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.02746","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/2607.02746/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":"2026-07-07T00:16:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z0MziJggxMQupEarwpTh+1BKqOjDblZRU8qdiOtpFH0KqG9Px7p4k5M62Em4I9Sid7FdSY2ThiR4ABGNXApHBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T00:20:04.391413Z"},"content_sha256":"f7e5d3102d3e09615d0158cc8445b8ccf4941dff9a5e7605d3806847926e976b","schema_version":"1.0","event_id":"sha256:f7e5d3102d3e09615d0158cc8445b8ccf4941dff9a5e7605d3806847926e976b"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:JEQA333OOIT5OHTSOKO7AS7CLO","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.48550/arXiv.1803.03735.URLhttp://arxiv.org/abs/1803.03735) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"K. K. Thekumparampil, C. Wang, S. Oh, L.-J. Li, Attention-based Graph Neural Network for Semi-supervised Learning (Mar. 2018).doi:10.48550/arXiv.1803. 03735. URLhttp://arxiv.org/abs/1803.03735","arxiv_id":"2607.02746","detector":"doi_compliance","evidence":{"ref_index":32,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"K. K. Thekumparampil, C. Wang, S. Oh, L.-J. Li, Attention-based Graph Neural Network for Semi-supervised Learning (Mar. 2018).doi:10.48550/arXiv.1803. 03735. URLhttp://arxiv.org/abs/1803.03735","reconstructed_doi":"10.48550/arXiv.1803.03735.URLhttp://arxiv.org/abs/1803.03735"},"severity":"advisory","ref_index":32,"audited_at":"2026-07-12T07:33:21.779993Z","event_type":"pith.integrity.v1","detected_doi":"10.48550/arXiv.1803.03735.URLhttp://arxiv.org/abs/1803.03735","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"8eaf282a28847f6e87c7eee5860747b3c0b97e8de7e98692b09baae0cdfdd637","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":12543,"payload_sha256":"7051f4718c944908f03ab26e1e9144a3749c02c9d69189f79f55eaf9ac6a613f","signature_b64":"F3GFPqHOfPKOrBpSA+qU1vSzPb/iXeeh53bqBjdmi6JEpIAkke3myugUgqnqSLresy1srNWTrPAymOCU05YwBw==","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":"2026-07-12T07:37:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wf2ksHN7BEzpZPpn4lni+Z5MiTx+nteEviVnJ6PBKNU4uizVZ/xYQgD0oxk1laJ0EmdPzufmyaTnK7cDrSEgCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T00:20:04.399874Z"},"content_sha256":"fddd66196bcc2936c86878d4c25a0ea274197fe1825ad1744f581ee55e643741","schema_version":"1.0","event_id":"sha256:fddd66196bcc2936c86878d4c25a0ea274197fe1825ad1744f581ee55e643741"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:JEQA333OOIT5OHTSOKO7AS7CLO","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/CVPR.2010.5539957.URLhttp://ieeexplore.ieee.org/document/5539957/) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"M. D. Zeiler, D. Krishnan, G. W. Taylor, R. Fergus, Deconvolutional networks, in: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recog- nition, IEEE, San Francisco, CA, USA, 2010, pp. 2528–2535.doi:10.1109/CVPR. 2010.5","arxiv_id":"2607.02746","detector":"doi_compliance","evidence":{"ref_index":24,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"M. D. Zeiler, D. Krishnan, G. W. Taylor, R. Fergus, Deconvolutional networks, in: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recog- nition, IEEE, San Francisco, CA, USA, 2010, pp. 2528–2535.doi:10.1109/CVPR. 2010.5","reconstructed_doi":"10.1109/CVPR.2010.5539957.URLhttp://ieeexplore.ieee.org/document/5539957/"},"severity":"advisory","ref_index":24,"audited_at":"2026-07-12T07:33:21.779993Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/CVPR.2010.5539957.URLhttp://ieeexplore.ieee.org/document/5539957/","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"f139fa36681beb731f703add40d5a24a2d926da2097a2513576d97147b700432","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":12542,"payload_sha256":"bb0d8a48f529ede77cc4ee821af15e3fbfc6019f150d32d09e63ce9321e66909","signature_b64":"vr5HI3tGkfnNFeZE54HtbXzA6sHle8qGQnZRgytXEBje7WItn+XmnaxhjeloLobtQE9UMK3mj7liV0EtpeuYBA==","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":"2026-07-12T07:37:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OeJrBq4y45KIOB8PWe0nDqxQdHMrH6a6qnspygitqFjHZoimolBE6ZC7rIpdWe8cGFKAx32TPlqakEopbgjfBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T00:20:04.400303Z"},"content_sha256":"8a9bff145831758857d473ab1666f0b59cc2557b3c0d1ef08cb6a691e82c8312","schema_version":"1.0","event_id":"sha256:8a9bff145831758857d473ab1666f0b59cc2557b3c0d1ef08cb6a691e82c8312"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:JEQA333OOIT5OHTSOKO7AS7CLO","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.48550/arXiv.1511.07289.URLhttp://arxiv.org/abs/1511.07289) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"D.-A. Clevert, T. Unterthiner, S. Hochreiter, Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) (Feb. 2016).doi:10.48550/arXiv. 1511.07289. URLhttp://arxiv.org/abs/1511.07289","arxiv_id":"2607.02746","detector":"doi_compliance","evidence":{"ref_index":17,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"D.-A. Clevert, T. Unterthiner, S. Hochreiter, Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) (Feb. 2016).doi:10.48550/arXiv. 1511.07289. URLhttp://arxiv.org/abs/1511.07289","reconstructed_doi":"10.48550/arXiv.1511.07289.URLhttp://arxiv.org/abs/1511.07289"},"severity":"advisory","ref_index":17,"audited_at":"2026-07-12T07:33:21.779993Z","event_type":"pith.integrity.v1","detected_doi":"10.48550/arXiv.1511.07289.URLhttp://arxiv.org/abs/1511.07289","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"017a029767b015934070621568a1f535179b2368186f4ad7cbdc659a596f5b39","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":12541,"payload_sha256":"a6ed49090b0eb4f55d3516afb7c54e05bceffaeb2857ba30ef2ddf8d2df55115","signature_b64":"mtAlGq3e1KE9CSpYmD726ADmLnPoQNtnn/l3b1uGqMKSMHXAl+GJENa4oBzZ87O1kUMbYci3fknF6xqlbBpXCg==","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":"2026-07-12T07:37:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zbk7om0v9bSqcSho/Bzdiafg+bv1DDsY98KpPJU+LfwXfZfpsQ62Yy8BYw3K8IZhMyMG8KZUWUwsB3WX2eQwCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T00:20:04.402065Z"},"content_sha256":"961c79cda125033ef1bbd9747ca8ee9247e99e875d8f11bf36753f9d44eeab01","schema_version":"1.0","event_id":"sha256:961c79cda125033ef1bbd9747ca8ee9247e99e875d8f11bf36753f9d44eeab01"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO/bundle.json","state_url":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JEQA333OOIT5OHTSOKO7AS7CLO/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":"2026-08-15T00:20:04Z","links":{"resolver":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO","bundle":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO/bundle.json","state":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JEQA333OOIT5OHTSOKO7AS7CLO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:JEQA333OOIT5OHTSOKO7AS7CLO","merge_version":"pith-open-graph-merge-v1","event_count":5,"valid_event_count":5,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9bf62040693f82d266206769bcdeafe101994640bedaa0138f2912248389c5f8","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2026-07-02T20:30:40Z","title_canon_sha256":"67feb1f2750dd4c3f66bbee69d19f463f402657a4a962790c774d055c200d281"},"schema_version":"1.0","source":{"id":"2607.02746","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.02746","created_at":"2026-07-07T00:16:11Z"},{"alias_kind":"arxiv_version","alias_value":"2607.02746v1","created_at":"2026-07-07T00:16:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.02746","created_at":"2026-07-07T00:16:11Z"},{"alias_kind":"pith_short_12","alias_value":"JEQA333OOIT5","created_at":"2026-07-07T00:16:11Z"},{"alias_kind":"pith_short_16","alias_value":"JEQA333OOIT5OHTS","created_at":"2026-07-07T00:16:11Z"},{"alias_kind":"pith_short_8","alias_value":"JEQA333O","created_at":"2026-07-07T00:16:11Z"}],"graph_snapshots":[{"event_id":"sha256:f7e5d3102d3e09615d0158cc8445b8ccf4941dff9a5e7605d3806847926e976b","target":"graph","created_at":"2026-07-07T00:16:11Z","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/2607.02746/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning has shown speedups for numerical methods in physics applications, but integrating Python-based libraries into high-performance C++ solvers creates performance bottlenecks. We present CodeJeNN, which bridges this gap by auto-generating self-contained C++ code from trained Keras models for inference. This eliminates external dependencies through minimal inlined functions, allowing seamless integration into existing frameworks. We describe the Keras-to-C++ workflow, supported architectures, and limitations. CodeJeNN is demonstrated through inference benchmarks against Keras in ea","authors_text":"Eric J Ching, Jay Arcities, Kamal Viswanath, Pavel Popov, Ryan F Johnson","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2026-07-02T20:30:40Z","title":"CodeJeNN: A simple C++ neural network generator for physics applications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.02746","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":"sha256:937b41d817a32b92bd23ef58bfd71f30ddda594f37660c1cb20ccf6735d1530f","target":"record","created_at":"2026-07-07T00:16:11Z","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":"9bf62040693f82d266206769bcdeafe101994640bedaa0138f2912248389c5f8","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2026-07-02T20:30:40Z","title_canon_sha256":"67feb1f2750dd4c3f66bbee69d19f463f402657a4a962790c774d055c200d281"},"schema_version":"1.0","source":{"id":"2607.02746","kind":"arxiv","version":1}},"canonical_sha256":"49200def6e7227d71e72729df04be25bb500465627c92bf9757b28797c29c473","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"49200def6e7227d71e72729df04be25bb500465627c92bf9757b28797c29c473","first_computed_at":"2026-07-07T00:16:11.727756Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-07T00:16:11.727756Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Zaw+gNZI0Itct3QWxW8Tzaeok8GppC57V61OusSg6ZblmDTwUc/avzQRy7lwBWUZWZjkrg07t3d//l8NjZ3/BQ==","signature_status":"signed_v1","signed_at":"2026-07-07T00:16:11.728488Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.02746","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:8a9bff145831758857d473ab1666f0b59cc2557b3c0d1ef08cb6a691e82c8312","sha256:961c79cda125033ef1bbd9747ca8ee9247e99e875d8f11bf36753f9d44eeab01","sha256:fddd66196bcc2936c86878d4c25a0ea274197fe1825ad1744f581ee55e643741"]}],"invalid_events":[],"applied_event_ids":["sha256:937b41d817a32b92bd23ef58bfd71f30ddda594f37660c1cb20ccf6735d1530f","sha256:f7e5d3102d3e09615d0158cc8445b8ccf4941dff9a5e7605d3806847926e976b"],"state_sha256":"980e1dd33d1bcbd46571f09f141c7562c87edeb3f3923a9d8adfd736636bb95c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9mKNo9p8CxyhVpIeo2rloslXNpX+H+7KvKK9ddsGIFXh4D4/cWWn7nj439iNkP8JfdfassDKQR2uj2y/UHojAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T00:20:04.410046Z","bundle_sha256":"aac9810ea25cdd91f28b37aabc658bf768814b6c1b3fe8f42bf6ce39eca25ca8"}}