{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JEQA333OOIT5OHTSOKO7AS7CLO","short_pith_number":"pith:JEQA333O","schema_version":"1.0","canonical_sha256":"49200def6e7227d71e72729df04be25bb500465627c92bf9757b28797c29c473","source":{"kind":"arxiv","id":"2607.02746","version":1},"attestation_state":"computed","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"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"},"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"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.02746","created_at":"2026-07-07T00:16:11.727876+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.02746v1","created_at":"2026-07-07T00:16:11.727876+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.02746","created_at":"2026-07-07T00:16:11.727876+00:00"},{"alias_kind":"pith_short_12","alias_value":"JEQA333OOIT5","created_at":"2026-07-07T00:16:11.727876+00:00"},{"alias_kind":"pith_short_16","alias_value":"JEQA333OOIT5OHTS","created_at":"2026-07-07T00:16:11.727876+00:00"},{"alias_kind":"pith_short_8","alias_value":"JEQA333O","created_at":"2026-07-07T00:16:11.727876+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO","json":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO.json","graph_json":"https://pith.science/api/pith-number/JEQA333OOIT5OHTSOKO7AS7CLO/graph.json","events_json":"https://pith.science/api/pith-number/JEQA333OOIT5OHTSOKO7AS7CLO/events.json","paper":"https://pith.science/paper/JEQA333O"},"agent_actions":{"view_html":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO","download_json":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO.json","view_paper":"https://pith.science/paper/JEQA333O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.02746&json=true","fetch_graph":"https://pith.science/api/pith-number/JEQA333OOIT5OHTSOKO7AS7CLO/graph.json","fetch_events":"https://pith.science/api/pith-number/JEQA333OOIT5OHTSOKO7AS7CLO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO/action/storage_attestation","attest_author":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO/action/author_attestation","sign_citation":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO/action/citation_signature","submit_replication":"https://pith.science/pith/JEQA333OOIT5OHTSOKO7AS7CLO/action/replication_record"}},"created_at":"2026-07-07T00:16:11.727876+00:00","updated_at":"2026-07-07T00:16:11.727876+00:00"}