{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LS22GU6B6YMRZX3HSUTFQM4VMR","short_pith_number":"pith:LS22GU6B","schema_version":"1.0","canonical_sha256":"5cb5a353c1f6191cdf679526583395645d9ec3574a0e75f868bb9a97c2ae98eb","source":{"kind":"arxiv","id":"2601.10457","version":3},"attestation_state":"computed","paper":{"title":"NSR-Boost: A Neuro-Symbolic Residual Boosting Framework for Industrial Legacy Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dabiao Ma, Haojun Fei, Hongtao Liu, Jian Yang, Jinle Tong, Mengyuan Han, Qing Yang, Ziming Dai","submitted_at":"2026-01-15T14:48:52Z","abstract_excerpt":"Although the Gradient Boosted Decision Trees (GBDTs) dominate industrial tabular applications, upgrading legacy models in high-concurrency production environments still faces prohibitive retraining costs and systemic risks. To address this problem, we present NSR-Boost, a neuro-symbolic residual boosting framework designed specifically for industrial scenarios. Its core advantage lies in being ``non-intrusive''. It treats the legacy model as a frozen model and performs targeted repairs on \"hard regions\" where predictions fail. The framework comprises three key stages: First, finding hard regio"},"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":"2601.10457","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-01-15T14:48:52Z","cross_cats_sorted":[],"title_canon_sha256":"be2d892adfebb9a80421bc6073b465b54f75193e51f5c4cbdb02ec0e8bb61941","abstract_canon_sha256":"8514d72a87cdd8923684dee9fc1a3d9881dd16d49a62da51606756af2a1e7637"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:02:30.822216Z","signature_b64":"o+jukp8apUmtwu4WKt+284IOxQgnfbHt4x7AOLwdoxagB0JGr9obaklnyS5E6XU/bpTKMU9dHmp+/ACX6GZ/Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cb5a353c1f6191cdf679526583395645d9ec3574a0e75f868bb9a97c2ae98eb","last_reissued_at":"2026-05-26T01:02:30.821341Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:02:30.821341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NSR-Boost: A Neuro-Symbolic Residual Boosting Framework for Industrial Legacy Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dabiao Ma, Haojun Fei, Hongtao Liu, Jian Yang, Jinle Tong, Mengyuan Han, Qing Yang, Ziming Dai","submitted_at":"2026-01-15T14:48:52Z","abstract_excerpt":"Although the Gradient Boosted Decision Trees (GBDTs) dominate industrial tabular applications, upgrading legacy models in high-concurrency production environments still faces prohibitive retraining costs and systemic risks. To address this problem, we present NSR-Boost, a neuro-symbolic residual boosting framework designed specifically for industrial scenarios. Its core advantage lies in being ``non-intrusive''. It treats the legacy model as a frozen model and performs targeted repairs on \"hard regions\" where predictions fail. The framework comprises three key stages: First, finding hard regio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.10457","kind":"arxiv","version":3},"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/2601.10457/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":"2601.10457","created_at":"2026-05-26T01:02:30.821473+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.10457v3","created_at":"2026-05-26T01:02:30.821473+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.10457","created_at":"2026-05-26T01:02:30.821473+00:00"},{"alias_kind":"pith_short_12","alias_value":"LS22GU6B6YMR","created_at":"2026-05-26T01:02:30.821473+00:00"},{"alias_kind":"pith_short_16","alias_value":"LS22GU6B6YMRZX3H","created_at":"2026-05-26T01:02:30.821473+00:00"},{"alias_kind":"pith_short_8","alias_value":"LS22GU6B","created_at":"2026-05-26T01:02:30.821473+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/LS22GU6B6YMRZX3HSUTFQM4VMR","json":"https://pith.science/pith/LS22GU6B6YMRZX3HSUTFQM4VMR.json","graph_json":"https://pith.science/api/pith-number/LS22GU6B6YMRZX3HSUTFQM4VMR/graph.json","events_json":"https://pith.science/api/pith-number/LS22GU6B6YMRZX3HSUTFQM4VMR/events.json","paper":"https://pith.science/paper/LS22GU6B"},"agent_actions":{"view_html":"https://pith.science/pith/LS22GU6B6YMRZX3HSUTFQM4VMR","download_json":"https://pith.science/pith/LS22GU6B6YMRZX3HSUTFQM4VMR.json","view_paper":"https://pith.science/paper/LS22GU6B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.10457&json=true","fetch_graph":"https://pith.science/api/pith-number/LS22GU6B6YMRZX3HSUTFQM4VMR/graph.json","fetch_events":"https://pith.science/api/pith-number/LS22GU6B6YMRZX3HSUTFQM4VMR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LS22GU6B6YMRZX3HSUTFQM4VMR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LS22GU6B6YMRZX3HSUTFQM4VMR/action/storage_attestation","attest_author":"https://pith.science/pith/LS22GU6B6YMRZX3HSUTFQM4VMR/action/author_attestation","sign_citation":"https://pith.science/pith/LS22GU6B6YMRZX3HSUTFQM4VMR/action/citation_signature","submit_replication":"https://pith.science/pith/LS22GU6B6YMRZX3HSUTFQM4VMR/action/replication_record"}},"created_at":"2026-05-26T01:02:30.821473+00:00","updated_at":"2026-05-26T01:02:30.821473+00:00"}