{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:S7IFVHMCNBAQ3D446MG2DJNJ5V","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":"cffab12334b0cb3433e4fbfd52f7e9350c4d3616065314ea1b5300df2d582c46","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-07T22:28:53Z","title_canon_sha256":"2a89a2a3e23c89487bc62f6711cad28a05fc6a6e37c890249ae9a669debdae9e"},"schema_version":"1.0","source":{"id":"2112.04023","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.04023","created_at":"2026-07-05T03:39:05Z"},{"alias_kind":"arxiv_version","alias_value":"2112.04023v1","created_at":"2026-07-05T03:39:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.04023","created_at":"2026-07-05T03:39:05Z"},{"alias_kind":"pith_short_12","alias_value":"S7IFVHMCNBAQ","created_at":"2026-07-05T03:39:05Z"},{"alias_kind":"pith_short_16","alias_value":"S7IFVHMCNBAQ3D44","created_at":"2026-07-05T03:39:05Z"},{"alias_kind":"pith_short_8","alias_value":"S7IFVHMC","created_at":"2026-07-05T03:39:05Z"}],"graph_snapshots":[{"event_id":"sha256:1f523d3539c1971fc8b411da5ec75e5d34c47a4ef73928e663f04d1308a54460","target":"graph","created_at":"2026-07-05T03:39:05Z","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/2112.04023/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We consider the problem of learning free-form symbolic expressions from raw data, such as that produced by an experiment in any scientific domain. Accurate and interpretable models of scientific phenomena are the cornerstone of scientific research. Simple yet interpretable models, such as linear or logistic regression and decision trees often lack predictive accuracy. Alternatively, accurate blackbox models such as deep neural networks provide high predictive accuracy, but do not readily admit human understanding in a way that would enrich the scientific theory of the phenomenon. Many great br","authors_text":"Francine Chen, Heishiro Toyoda, Kent Lyons, Nikos Arechiga, Rumen Iliev, Yanxia Zhang, Yan-Ying Chen","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-07T22:28:53Z","title":"Accelerating Understanding of Scientific Experiments with End to End Symbolic Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.04023","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:adee819db3e4271f13d3dc728d417381bd4c4b4b92845f6ba63c5f0513e6bcfa","target":"record","created_at":"2026-07-05T03:39:05Z","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":"cffab12334b0cb3433e4fbfd52f7e9350c4d3616065314ea1b5300df2d582c46","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-07T22:28:53Z","title_canon_sha256":"2a89a2a3e23c89487bc62f6711cad28a05fc6a6e37c890249ae9a669debdae9e"},"schema_version":"1.0","source":{"id":"2112.04023","kind":"arxiv","version":1}},"canonical_sha256":"97d05a9d8268410d8f9cf30da1a5a9ed6f1dec624b6173a3648d2d7d0d33095a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"97d05a9d8268410d8f9cf30da1a5a9ed6f1dec624b6173a3648d2d7d0d33095a","first_computed_at":"2026-07-05T03:39:05.242158Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:39:05.242158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"V2/R0QkqGIhz0wcgZ5wS7fJQRsCxBmBgCmy3VzsJrJ0N6JlRtZ4lsSWlGAOipQMEta1TJ1ghauS7t6n65AT9Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:39:05.242649Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.04023","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:adee819db3e4271f13d3dc728d417381bd4c4b4b92845f6ba63c5f0513e6bcfa","sha256:1f523d3539c1971fc8b411da5ec75e5d34c47a4ef73928e663f04d1308a54460"],"state_sha256":"27ce80d8c3515637aeb1a8bdd2d4a0c8fa9f73977f400e55ddbd4c93aeaa35de"}