{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JOJKJIILLYW6OB6HXOJZM74Y5K","short_pith_number":"pith:JOJKJIIL","schema_version":"1.0","canonical_sha256":"4b92a4a10b5e2de707c7bb93967f98eab406573d977656f93ac3cdfc350a0f71","source":{"kind":"arxiv","id":"2607.14287","version":1},"attestation_state":"computed","paper":{"title":"XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alan Pachkovskiy, Imtiaz Ahmed, Jeremy Dawson, Md Mahedi Hasan, Md Mushfiqur Rahaman, Srinjoy Das","submitted_at":"2026-07-15T18:49:09Z","abstract_excerpt":"Defect segmentation in additive manufacturing (AM) X-ray computed tomography (XCT) images remains challenging due to severe class imbalance and large distribution shifts across scan conditions. Although recent foundation models such as the Segment Anything Model (SAM) provide strong general-purpose segmentation priors, their natural-image pre-training transfers poorly to the AM XCT domain, where defects appear as subtle non-semantic microstructural anomalies. Moreover, adapting SAM to the AM domain is further limited by the large domain gap and scarcity of labeled real XCT data. We present XCT"},"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":"2607.14287","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-15T18:49:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"37f06a9dcd9e94267c41ef2f1cebd641c6a005b2f90b8785089a0cdc3cf58de4","abstract_canon_sha256":"86caa28be47f57ea2019ff58ecbfdf8c6b1f3b175e82a5b649e315643283f403"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-17T00:21:03.383118Z","signature_b64":"3uoHH6SYZjWMj3RMztbmkRbAvh3PHWV9IlUVJYZnx+OsrGcySO7ChOJ0qU8wt3f4I/7ANq+JE40VddHJTdYwCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b92a4a10b5e2de707c7bb93967f98eab406573d977656f93ac3cdfc350a0f71","last_reissued_at":"2026-07-17T00:21:03.381850Z","signature_status":"signed_v1","first_computed_at":"2026-07-17T00:21:03.381850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alan Pachkovskiy, Imtiaz Ahmed, Jeremy Dawson, Md Mahedi Hasan, Md Mushfiqur Rahaman, Srinjoy Das","submitted_at":"2026-07-15T18:49:09Z","abstract_excerpt":"Defect segmentation in additive manufacturing (AM) X-ray computed tomography (XCT) images remains challenging due to severe class imbalance and large distribution shifts across scan conditions. Although recent foundation models such as the Segment Anything Model (SAM) provide strong general-purpose segmentation priors, their natural-image pre-training transfers poorly to the AM XCT domain, where defects appear as subtle non-semantic microstructural anomalies. Moreover, adapting SAM to the AM domain is further limited by the large domain gap and scarcity of labeled real XCT data. We present XCT"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.14287","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/2607.14287/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":"2607.14287","created_at":"2026-07-17T00:21:03.382678+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.14287v1","created_at":"2026-07-17T00:21:03.382678+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.14287","created_at":"2026-07-17T00:21:03.382678+00:00"},{"alias_kind":"pith_short_12","alias_value":"JOJKJIILLYW6","created_at":"2026-07-17T00:21:03.382678+00:00"},{"alias_kind":"pith_short_16","alias_value":"JOJKJIILLYW6OB6H","created_at":"2026-07-17T00:21:03.382678+00:00"},{"alias_kind":"pith_short_8","alias_value":"JOJKJIIL","created_at":"2026-07-17T00:21:03.382678+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/JOJKJIILLYW6OB6HXOJZM74Y5K","json":"https://pith.science/pith/JOJKJIILLYW6OB6HXOJZM74Y5K.json","graph_json":"https://pith.science/api/pith-number/JOJKJIILLYW6OB6HXOJZM74Y5K/graph.json","events_json":"https://pith.science/api/pith-number/JOJKJIILLYW6OB6HXOJZM74Y5K/events.json","paper":"https://pith.science/paper/JOJKJIIL"},"agent_actions":{"view_html":"https://pith.science/pith/JOJKJIILLYW6OB6HXOJZM74Y5K","download_json":"https://pith.science/pith/JOJKJIILLYW6OB6HXOJZM74Y5K.json","view_paper":"https://pith.science/paper/JOJKJIIL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.14287&json=true","fetch_graph":"https://pith.science/api/pith-number/JOJKJIILLYW6OB6HXOJZM74Y5K/graph.json","fetch_events":"https://pith.science/api/pith-number/JOJKJIILLYW6OB6HXOJZM74Y5K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JOJKJIILLYW6OB6HXOJZM74Y5K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JOJKJIILLYW6OB6HXOJZM74Y5K/action/storage_attestation","attest_author":"https://pith.science/pith/JOJKJIILLYW6OB6HXOJZM74Y5K/action/author_attestation","sign_citation":"https://pith.science/pith/JOJKJIILLYW6OB6HXOJZM74Y5K/action/citation_signature","submit_replication":"https://pith.science/pith/JOJKJIILLYW6OB6HXOJZM74Y5K/action/replication_record"}},"created_at":"2026-07-17T00:21:03.382678+00:00","updated_at":"2026-07-17T00:21:03.382678+00:00"}