{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:A6Y3CJVX5SRAIIO5CHYJJ7EXA3","short_pith_number":"pith:A6Y3CJVX","canonical_record":{"source":{"id":"2409.07369","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-09-11T15:58:09Z","cross_cats_sorted":["math-ph","math.MP"],"title_canon_sha256":"6381840128f6ee662d41dc1a18c3e34e9155601db46a33996efa1a7541993782","abstract_canon_sha256":"7c598bdddd9f24078e16e06269ee23b8d654b48c663c607d711b620687c18b8a"},"schema_version":"1.0"},"canonical_sha256":"07b1b126b7eca20421dd11f094fc9706d5cf0faba52fb93fd88613f607cc1aac","source":{"kind":"arxiv","id":"2409.07369","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.07369","created_at":"2026-07-05T09:36:14Z"},{"alias_kind":"arxiv_version","alias_value":"2409.07369v2","created_at":"2026-07-05T09:36:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.07369","created_at":"2026-07-05T09:36:14Z"},{"alias_kind":"pith_short_12","alias_value":"A6Y3CJVX5SRA","created_at":"2026-07-05T09:36:14Z"},{"alias_kind":"pith_short_16","alias_value":"A6Y3CJVX5SRAIIO5","created_at":"2026-07-05T09:36:14Z"},{"alias_kind":"pith_short_8","alias_value":"A6Y3CJVX","created_at":"2026-07-05T09:36:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:A6Y3CJVX5SRAIIO5CHYJJ7EXA3","target":"record","payload":{"canonical_record":{"source":{"id":"2409.07369","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-09-11T15:58:09Z","cross_cats_sorted":["math-ph","math.MP"],"title_canon_sha256":"6381840128f6ee662d41dc1a18c3e34e9155601db46a33996efa1a7541993782","abstract_canon_sha256":"7c598bdddd9f24078e16e06269ee23b8d654b48c663c607d711b620687c18b8a"},"schema_version":"1.0"},"canonical_sha256":"07b1b126b7eca20421dd11f094fc9706d5cf0faba52fb93fd88613f607cc1aac","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:14.570057Z","signature_b64":"shelxhsgebg5wLdcY+fuNh7zVr2v4Rt4WnFB7oAIvImvRWIyXs3PWLX2oRinhoiDN5kh9BqUl8T0Pa0Iggz2BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07b1b126b7eca20421dd11f094fc9706d5cf0faba52fb93fd88613f607cc1aac","last_reissued_at":"2026-07-05T09:36:14.569468Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:14.569468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.07369","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:36:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N4YsfJynJU+b2IB6GTgKSNJiLBLHNHmSdYz0zWcQfaCgi2gPi/tpeCzi6N/HS+wWxC5fvFzC5WLcMpt78YqACw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T21:51:42.823176Z"},"content_sha256":"d74e178ecac1209e584318687f122f4efd1d963f06554ebf8ae00a23769a29c0","schema_version":"1.0","event_id":"sha256:d74e178ecac1209e584318687f122f4efd1d963f06554ebf8ae00a23769a29c0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:A6Y3CJVX5SRAIIO5CHYJJ7EXA3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Constraining Genetic Symbolic Regression via Semantic Backpropagation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math-ph","math.MP"],"primary_cat":"math.OC","authors_text":"Andrew Ooi, Maximilian Reissmann, Richard Sandberg, Yuan Fang","submitted_at":"2024-09-11T15:58:09Z","abstract_excerpt":"Evolutionary symbolic regression approaches are powerful tools that can approximate an explicit mapping between input features and observation for various problems. However, ensuring that explored expressions maintain consistency with domain-specific constraints remains a crucial challenge. While neural networks are able to employ additional information like conservation laws to achieve more appropriate and robust approximations, the potential remains unrealized within genetic algorithms. This disparity is rooted in the inherent discrete randomness of recombining and mutating to generate new m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.07369","kind":"arxiv","version":2},"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/2409.07369/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":"2026-07-05T09:36:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a6kBk6g/UMz2olt8oBdr4CvEKi+4ZuvqdyiqyBzyC9rfVSiSheeTjBq/pLSmUrDcj0vGoQVbS+xbgIs7aXAJCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T21:51:42.824070Z"},"content_sha256":"7fb68cb554e0a1988bb3e4cac6004836969d14624993362ca9ab975215892416","schema_version":"1.0","event_id":"sha256:7fb68cb554e0a1988bb3e4cac6004836969d14624993362ca9ab975215892416"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A6Y3CJVX5SRAIIO5CHYJJ7EXA3/bundle.json","state_url":"https://pith.science/pith/A6Y3CJVX5SRAIIO5CHYJJ7EXA3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A6Y3CJVX5SRAIIO5CHYJJ7EXA3/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":"2026-08-21T21:51:42Z","links":{"resolver":"https://pith.science/pith/A6Y3CJVX5SRAIIO5CHYJJ7EXA3","bundle":"https://pith.science/pith/A6Y3CJVX5SRAIIO5CHYJJ7EXA3/bundle.json","state":"https://pith.science/pith/A6Y3CJVX5SRAIIO5CHYJJ7EXA3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A6Y3CJVX5SRAIIO5CHYJJ7EXA3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:A6Y3CJVX5SRAIIO5CHYJJ7EXA3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7c598bdddd9f24078e16e06269ee23b8d654b48c663c607d711b620687c18b8a","cross_cats_sorted":["math-ph","math.MP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-09-11T15:58:09Z","title_canon_sha256":"6381840128f6ee662d41dc1a18c3e34e9155601db46a33996efa1a7541993782"},"schema_version":"1.0","source":{"id":"2409.07369","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.07369","created_at":"2026-07-05T09:36:14Z"},{"alias_kind":"arxiv_version","alias_value":"2409.07369v2","created_at":"2026-07-05T09:36:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.07369","created_at":"2026-07-05T09:36:14Z"},{"alias_kind":"pith_short_12","alias_value":"A6Y3CJVX5SRA","created_at":"2026-07-05T09:36:14Z"},{"alias_kind":"pith_short_16","alias_value":"A6Y3CJVX5SRAIIO5","created_at":"2026-07-05T09:36:14Z"},{"alias_kind":"pith_short_8","alias_value":"A6Y3CJVX","created_at":"2026-07-05T09:36:14Z"}],"graph_snapshots":[{"event_id":"sha256:7fb68cb554e0a1988bb3e4cac6004836969d14624993362ca9ab975215892416","target":"graph","created_at":"2026-07-05T09:36:14Z","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/2409.07369/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Evolutionary symbolic regression approaches are powerful tools that can approximate an explicit mapping between input features and observation for various problems. However, ensuring that explored expressions maintain consistency with domain-specific constraints remains a crucial challenge. While neural networks are able to employ additional information like conservation laws to achieve more appropriate and robust approximations, the potential remains unrealized within genetic algorithms. This disparity is rooted in the inherent discrete randomness of recombining and mutating to generate new m","authors_text":"Andrew Ooi, Maximilian Reissmann, Richard Sandberg, Yuan Fang","cross_cats":["math-ph","math.MP"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-09-11T15:58:09Z","title":"Constraining Genetic Symbolic Regression via Semantic Backpropagation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.07369","kind":"arxiv","version":2},"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:d74e178ecac1209e584318687f122f4efd1d963f06554ebf8ae00a23769a29c0","target":"record","created_at":"2026-07-05T09:36:14Z","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":"7c598bdddd9f24078e16e06269ee23b8d654b48c663c607d711b620687c18b8a","cross_cats_sorted":["math-ph","math.MP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-09-11T15:58:09Z","title_canon_sha256":"6381840128f6ee662d41dc1a18c3e34e9155601db46a33996efa1a7541993782"},"schema_version":"1.0","source":{"id":"2409.07369","kind":"arxiv","version":2}},"canonical_sha256":"07b1b126b7eca20421dd11f094fc9706d5cf0faba52fb93fd88613f607cc1aac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"07b1b126b7eca20421dd11f094fc9706d5cf0faba52fb93fd88613f607cc1aac","first_computed_at":"2026-07-05T09:36:14.569468Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:36:14.569468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"shelxhsgebg5wLdcY+fuNh7zVr2v4Rt4WnFB7oAIvImvRWIyXs3PWLX2oRinhoiDN5kh9BqUl8T0Pa0Iggz2BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:36:14.570057Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.07369","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d74e178ecac1209e584318687f122f4efd1d963f06554ebf8ae00a23769a29c0","sha256:7fb68cb554e0a1988bb3e4cac6004836969d14624993362ca9ab975215892416"],"state_sha256":"6dc74a8ee57e2e902bf54c81bb084692b92d6c723e9f7c129035110c93bfd1dd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NZOjENKwotUDwSJhBSKX00cGZTh9v8o1j4PhE4o/Xm0oN/5L0MslHW8OSBYbcNJXGEZDUHra2erpx3d7lLt6AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T21:51:42.829558Z","bundle_sha256":"1e9291963ed7dbd73e148526e7ec880595cf339608133a2f2919449e73fe2568"}}