{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:NYWCVHTSAY2IUSBS7PHWWE3BJG","short_pith_number":"pith:NYWCVHTS","schema_version":"1.0","canonical_sha256":"6e2c2a9e7206348a4832fbcf6b1361499b4237c44adb8441e0ccd9de246aeb4a","source":{"kind":"arxiv","id":"2607.10546","version":1},"attestation_state":"computed","paper":{"title":"LLM-PDESR: Robust PDE Discovery via Subdomain Weighted Residuals and LLM-Guided Symbolic Hypothesis Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bo Yang, Chunguo Wu, Hao Ma, Heow Pueh Lee, Jinyang Du, Xiaohu Shi, Yanchun Liang","submitted_at":"2026-07-12T03:16:49Z","abstract_excerpt":"Discovering governing partial differential equations (PDEs) from noisy observational data is a fundamental challenge in scientific machine learning. Traditional symbolic regression (SR) methods often struggle to identify accurate equations within vast combinatorial search spaces, largely due to their inability to incorporate essential domain-specific prior knowledge. Furthermore, reliance on pointwise evaluations and discrete finite differences inherently amplifies high-frequency noise, creating deceptive fitness landscapes that derail the optimization process. To resolve these bottlenecks, we"},"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":"2607.10546","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-12T03:16:49Z","cross_cats_sorted":[],"title_canon_sha256":"585934436815dedc897a89d8e454e35ae8f79273604550279519963eb63b150e","abstract_canon_sha256":"760b36e5a139cd5c4afd5d331aff74504b2cbf00aa2afde41489c096fa649ae6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:21:20.646564Z","signature_b64":"69RQQb3f4ib003gHHxeWBlAt4PhgCLr04ahBh+jOLRSJkspHUa2oG6M/+r7JQKmbJQ8TYm7J+TB2MPs72hJTAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e2c2a9e7206348a4832fbcf6b1361499b4237c44adb8441e0ccd9de246aeb4a","last_reissued_at":"2026-07-14T01:21:20.645686Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:21:20.645686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLM-PDESR: Robust PDE Discovery via Subdomain Weighted Residuals and LLM-Guided Symbolic Hypothesis Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bo Yang, Chunguo Wu, Hao Ma, Heow Pueh Lee, Jinyang Du, Xiaohu Shi, Yanchun Liang","submitted_at":"2026-07-12T03:16:49Z","abstract_excerpt":"Discovering governing partial differential equations (PDEs) from noisy observational data is a fundamental challenge in scientific machine learning. Traditional symbolic regression (SR) methods often struggle to identify accurate equations within vast combinatorial search spaces, largely due to their inability to incorporate essential domain-specific prior knowledge. Furthermore, reliance on pointwise evaluations and discrete finite differences inherently amplifies high-frequency noise, creating deceptive fitness landscapes that derail the optimization process. To resolve these bottlenecks, we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.10546","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/2607.10546/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":"2607.10546","created_at":"2026-07-14T01:21:20.646129+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.10546v1","created_at":"2026-07-14T01:21:20.646129+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.10546","created_at":"2026-07-14T01:21:20.646129+00:00"},{"alias_kind":"pith_short_12","alias_value":"NYWCVHTSAY2I","created_at":"2026-07-14T01:21:20.646129+00:00"},{"alias_kind":"pith_short_16","alias_value":"NYWCVHTSAY2IUSBS","created_at":"2026-07-14T01:21:20.646129+00:00"},{"alias_kind":"pith_short_8","alias_value":"NYWCVHTS","created_at":"2026-07-14T01:21:20.646129+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/NYWCVHTSAY2IUSBS7PHWWE3BJG","json":"https://pith.science/pith/NYWCVHTSAY2IUSBS7PHWWE3BJG.json","graph_json":"https://pith.science/api/pith-number/NYWCVHTSAY2IUSBS7PHWWE3BJG/graph.json","events_json":"https://pith.science/api/pith-number/NYWCVHTSAY2IUSBS7PHWWE3BJG/events.json","paper":"https://pith.science/paper/NYWCVHTS"},"agent_actions":{"view_html":"https://pith.science/pith/NYWCVHTSAY2IUSBS7PHWWE3BJG","download_json":"https://pith.science/pith/NYWCVHTSAY2IUSBS7PHWWE3BJG.json","view_paper":"https://pith.science/paper/NYWCVHTS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.10546&json=true","fetch_graph":"https://pith.science/api/pith-number/NYWCVHTSAY2IUSBS7PHWWE3BJG/graph.json","fetch_events":"https://pith.science/api/pith-number/NYWCVHTSAY2IUSBS7PHWWE3BJG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NYWCVHTSAY2IUSBS7PHWWE3BJG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NYWCVHTSAY2IUSBS7PHWWE3BJG/action/storage_attestation","attest_author":"https://pith.science/pith/NYWCVHTSAY2IUSBS7PHWWE3BJG/action/author_attestation","sign_citation":"https://pith.science/pith/NYWCVHTSAY2IUSBS7PHWWE3BJG/action/citation_signature","submit_replication":"https://pith.science/pith/NYWCVHTSAY2IUSBS7PHWWE3BJG/action/replication_record"}},"created_at":"2026-07-14T01:21:20.646129+00:00","updated_at":"2026-07-14T01:21:20.646129+00:00"}