{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:64J6QDUNYNOP5773HG4ERPYM3G","short_pith_number":"pith:64J6QDUN","canonical_record":{"source":{"id":"2509.03036","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-03T05:53:40Z","cross_cats_sorted":["cs.AI","cs.IR","cs.SC"],"title_canon_sha256":"5c00a9af88adc9e2915f16def78c7c70b7733c28f262a545ada75d96b223efe6","abstract_canon_sha256":"31bd04832f246e7ea2427b574f546a84b7a94b92fb021c13385a8503167a82e2"},"schema_version":"1.0"},"canonical_sha256":"f713e80e8dc35cfefffb39b848bf0cd98d73770cd46c8c2bff7d468bdb009d74","source":{"kind":"arxiv","id":"2509.03036","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.03036","created_at":"2026-07-05T12:04:02Z"},{"alias_kind":"arxiv_version","alias_value":"2509.03036v1","created_at":"2026-07-05T12:04:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.03036","created_at":"2026-07-05T12:04:02Z"},{"alias_kind":"pith_short_12","alias_value":"64J6QDUNYNOP","created_at":"2026-07-05T12:04:02Z"},{"alias_kind":"pith_short_16","alias_value":"64J6QDUNYNOP5773","created_at":"2026-07-05T12:04:02Z"},{"alias_kind":"pith_short_8","alias_value":"64J6QDUN","created_at":"2026-07-05T12:04:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:64J6QDUNYNOP5773HG4ERPYM3G","target":"record","payload":{"canonical_record":{"source":{"id":"2509.03036","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-03T05:53:40Z","cross_cats_sorted":["cs.AI","cs.IR","cs.SC"],"title_canon_sha256":"5c00a9af88adc9e2915f16def78c7c70b7733c28f262a545ada75d96b223efe6","abstract_canon_sha256":"31bd04832f246e7ea2427b574f546a84b7a94b92fb021c13385a8503167a82e2"},"schema_version":"1.0"},"canonical_sha256":"f713e80e8dc35cfefffb39b848bf0cd98d73770cd46c8c2bff7d468bdb009d74","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:04:02.558480Z","signature_b64":"n92qy05M+9RB2q7L5IybzURnR1BisFO6hvoBsSdZo4Mh6wu9T4w/662f86BiEBJ5N0+zuwGNP86XOD6I45RSCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f713e80e8dc35cfefffb39b848bf0cd98d73770cd46c8c2bff7d468bdb009d74","last_reissued_at":"2026-07-05T12:04:02.557995Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:04:02.557995Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.03036","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":"2026-07-05T12:04:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o5LksIMHu7H0MqWPcJczZUMCl1D0x2cyAWw7IfVZDPeiBXHBhD8IQMe4UvOWCD4e4saSXRnX4Z4ZVoRQVxkABQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T12:48:39.355447Z"},"content_sha256":"84c6ed496c3be3e8d7a178295f4047b122fe37eacf8cc44eaa331359c1f392b4","schema_version":"1.0","event_id":"sha256:84c6ed496c3be3e8d7a178295f4047b122fe37eacf8cc44eaa331359c1f392b4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:64J6QDUNYNOP5773HG4ERPYM3G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.SC"],"primary_cat":"cs.LG","authors_text":"Bilge Taskin, Teddy Lazebnik, Wenxiong Xie","submitted_at":"2025-09-03T05:53:40Z","abstract_excerpt":"Symbolic regression (SR) has emerged as a powerful tool for automated scientific discovery, enabling the derivation of governing equations from experimental data. A growing body of work illustrates the promise of integrating domain knowledge into the SR to improve the discovered equation's generality and usefulness. Physics-informed SR (PiSR) addresses this by incorporating domain knowledge, but current methods often require specialized formulations and manual feature engineering, limiting their adaptability only to domain experts. In this study, we leverage pre-trained Large Language Models ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.03036","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/2509.03036/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":"2026-07-05T12:04:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f7AO8I7IIItJAr/MAV9e7q+wIMfWbescZIARerFiZ0uXmGEH9lIsFgaTZji/MgO0tXs9ZhX2j3qbODKdSWW9Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T12:48:39.356344Z"},"content_sha256":"10facf6d1b0c02682531b8a6482ec2af87d40949dd84534751f71a6c1fcc3104","schema_version":"1.0","event_id":"sha256:10facf6d1b0c02682531b8a6482ec2af87d40949dd84534751f71a6c1fcc3104"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/64J6QDUNYNOP5773HG4ERPYM3G/bundle.json","state_url":"https://pith.science/pith/64J6QDUNYNOP5773HG4ERPYM3G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/64J6QDUNYNOP5773HG4ERPYM3G/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":"2026-08-17T12:48:39Z","links":{"resolver":"https://pith.science/pith/64J6QDUNYNOP5773HG4ERPYM3G","bundle":"https://pith.science/pith/64J6QDUNYNOP5773HG4ERPYM3G/bundle.json","state":"https://pith.science/pith/64J6QDUNYNOP5773HG4ERPYM3G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/64J6QDUNYNOP5773HG4ERPYM3G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:64J6QDUNYNOP5773HG4ERPYM3G","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":"31bd04832f246e7ea2427b574f546a84b7a94b92fb021c13385a8503167a82e2","cross_cats_sorted":["cs.AI","cs.IR","cs.SC"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-03T05:53:40Z","title_canon_sha256":"5c00a9af88adc9e2915f16def78c7c70b7733c28f262a545ada75d96b223efe6"},"schema_version":"1.0","source":{"id":"2509.03036","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.03036","created_at":"2026-07-05T12:04:02Z"},{"alias_kind":"arxiv_version","alias_value":"2509.03036v1","created_at":"2026-07-05T12:04:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.03036","created_at":"2026-07-05T12:04:02Z"},{"alias_kind":"pith_short_12","alias_value":"64J6QDUNYNOP","created_at":"2026-07-05T12:04:02Z"},{"alias_kind":"pith_short_16","alias_value":"64J6QDUNYNOP5773","created_at":"2026-07-05T12:04:02Z"},{"alias_kind":"pith_short_8","alias_value":"64J6QDUN","created_at":"2026-07-05T12:04:02Z"}],"graph_snapshots":[{"event_id":"sha256:10facf6d1b0c02682531b8a6482ec2af87d40949dd84534751f71a6c1fcc3104","target":"graph","created_at":"2026-07-05T12:04:02Z","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/2509.03036/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Symbolic regression (SR) has emerged as a powerful tool for automated scientific discovery, enabling the derivation of governing equations from experimental data. A growing body of work illustrates the promise of integrating domain knowledge into the SR to improve the discovered equation's generality and usefulness. Physics-informed SR (PiSR) addresses this by incorporating domain knowledge, but current methods often require specialized formulations and manual feature engineering, limiting their adaptability only to domain experts. In this study, we leverage pre-trained Large Language Models (","authors_text":"Bilge Taskin, Teddy Lazebnik, Wenxiong Xie","cross_cats":["cs.AI","cs.IR","cs.SC"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-03T05:53:40Z","title":"Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.03036","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":"sha256:84c6ed496c3be3e8d7a178295f4047b122fe37eacf8cc44eaa331359c1f392b4","target":"record","created_at":"2026-07-05T12:04:02Z","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":"31bd04832f246e7ea2427b574f546a84b7a94b92fb021c13385a8503167a82e2","cross_cats_sorted":["cs.AI","cs.IR","cs.SC"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-03T05:53:40Z","title_canon_sha256":"5c00a9af88adc9e2915f16def78c7c70b7733c28f262a545ada75d96b223efe6"},"schema_version":"1.0","source":{"id":"2509.03036","kind":"arxiv","version":1}},"canonical_sha256":"f713e80e8dc35cfefffb39b848bf0cd98d73770cd46c8c2bff7d468bdb009d74","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f713e80e8dc35cfefffb39b848bf0cd98d73770cd46c8c2bff7d468bdb009d74","first_computed_at":"2026-07-05T12:04:02.557995Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:04:02.557995Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"n92qy05M+9RB2q7L5IybzURnR1BisFO6hvoBsSdZo4Mh6wu9T4w/662f86BiEBJ5N0+zuwGNP86XOD6I45RSCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T12:04:02.558480Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.03036","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:84c6ed496c3be3e8d7a178295f4047b122fe37eacf8cc44eaa331359c1f392b4","sha256:10facf6d1b0c02682531b8a6482ec2af87d40949dd84534751f71a6c1fcc3104"],"state_sha256":"f0ce7fb2baafb3e24e9f22e2db1dc90a851886a13755c801071a6a82bbe0e090"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Eict9OruCv7ap3jyl/+2sc9GZGP8bXEULAKJnw2w44SplB/PptLzQT56K6VD5PdLpC22bT+Yzs+a+dSHgacnDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T12:48:39.363635Z","bundle_sha256":"b360651ed6af5f9f290ab591d47d15ebb729734e3332f0510fadf64a40ab9b97"}}