{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:P2MY4YODELPJUXGH3PYVVLOOGB","short_pith_number":"pith:P2MY4YOD","schema_version":"1.0","canonical_sha256":"7e998e61c322de9a5cc7dbf15aadce306949616b203de0f44c17707ffa38a7ac","source":{"kind":"arxiv","id":"2606.11605","version":1},"attestation_state":"computed","paper":{"title":"Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Anandkumar Patel, Ge Song, Hongyi Xu, Kiarash Naghavi Khanghah, Rajiv Malhotra","submitted_at":"2026-06-10T03:05:42Z","abstract_excerpt":"Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models. This paper proposes a novel knowledge distillation framework designed to achieve high-accuracy predictions in data-scarce scenarios. The framework integrates analytical physics priors, which are systematically extracted from scientific literature via Large Language Models, into a privileged teacher model. We employ a Graph-Masked Attention layer to capture the complex physical dependencies among input variables showing strict "},"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":"2606.11605","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T03:05:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"87c884f2547a2add9897e95b8813780025e6dae3f7239995c9dbbfabe8953a4e","abstract_canon_sha256":"fefe6467941250d7a14f018c8652858e198cf7dd57033916ab760fc926f73bd3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:09:58.679499Z","signature_b64":"VapCDKmR3PsN+ABkWnTa4Wi4j4K+05gxUUo3nm7luMyYfr3QGKVcnkV5gJW8DhI+A743lv7R+axdz77H3vc6Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e998e61c322de9a5cc7dbf15aadce306949616b203de0f44c17707ffa38a7ac","last_reissued_at":"2026-06-11T01:09:58.678643Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:09:58.678643Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Anandkumar Patel, Ge Song, Hongyi Xu, Kiarash Naghavi Khanghah, Rajiv Malhotra","submitted_at":"2026-06-10T03:05:42Z","abstract_excerpt":"Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models. This paper proposes a novel knowledge distillation framework designed to achieve high-accuracy predictions in data-scarce scenarios. The framework integrates analytical physics priors, which are systematically extracted from scientific literature via Large Language Models, into a privileged teacher model. We employ a Graph-Masked Attention layer to capture the complex physical dependencies among input variables showing strict "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11605","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/2606.11605/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":"2606.11605","created_at":"2026-06-11T01:09:58.678745+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.11605v1","created_at":"2026-06-11T01:09:58.678745+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11605","created_at":"2026-06-11T01:09:58.678745+00:00"},{"alias_kind":"pith_short_12","alias_value":"P2MY4YODELPJ","created_at":"2026-06-11T01:09:58.678745+00:00"},{"alias_kind":"pith_short_16","alias_value":"P2MY4YODELPJUXGH","created_at":"2026-06-11T01:09:58.678745+00:00"},{"alias_kind":"pith_short_8","alias_value":"P2MY4YOD","created_at":"2026-06-11T01:09:58.678745+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/P2MY4YODELPJUXGH3PYVVLOOGB","json":"https://pith.science/pith/P2MY4YODELPJUXGH3PYVVLOOGB.json","graph_json":"https://pith.science/api/pith-number/P2MY4YODELPJUXGH3PYVVLOOGB/graph.json","events_json":"https://pith.science/api/pith-number/P2MY4YODELPJUXGH3PYVVLOOGB/events.json","paper":"https://pith.science/paper/P2MY4YOD"},"agent_actions":{"view_html":"https://pith.science/pith/P2MY4YODELPJUXGH3PYVVLOOGB","download_json":"https://pith.science/pith/P2MY4YODELPJUXGH3PYVVLOOGB.json","view_paper":"https://pith.science/paper/P2MY4YOD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.11605&json=true","fetch_graph":"https://pith.science/api/pith-number/P2MY4YODELPJUXGH3PYVVLOOGB/graph.json","fetch_events":"https://pith.science/api/pith-number/P2MY4YODELPJUXGH3PYVVLOOGB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P2MY4YODELPJUXGH3PYVVLOOGB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P2MY4YODELPJUXGH3PYVVLOOGB/action/storage_attestation","attest_author":"https://pith.science/pith/P2MY4YODELPJUXGH3PYVVLOOGB/action/author_attestation","sign_citation":"https://pith.science/pith/P2MY4YODELPJUXGH3PYVVLOOGB/action/citation_signature","submit_replication":"https://pith.science/pith/P2MY4YODELPJUXGH3PYVVLOOGB/action/replication_record"}},"created_at":"2026-06-11T01:09:58.678745+00:00","updated_at":"2026-06-11T01:09:58.678745+00:00"}