{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:RTTTQCPNVGTXDSL7FZRSLLIVQQ","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":"d8efca64a0174923a9c516dc991dfa81e565e6ae6aae4f3cb59ee804f795b327","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-26T08:18:32Z","title_canon_sha256":"83a2eb96d69250f7ae08f0bc5c2add1576c03d6eb1776d36a0a99d873013991b"},"schema_version":"1.0","source":{"id":"2505.19659","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.19659","created_at":"2026-07-05T11:09:34Z"},{"alias_kind":"arxiv_version","alias_value":"2505.19659v1","created_at":"2026-07-05T11:09:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19659","created_at":"2026-07-05T11:09:34Z"},{"alias_kind":"pith_short_12","alias_value":"RTTTQCPNVGTX","created_at":"2026-07-05T11:09:34Z"},{"alias_kind":"pith_short_16","alias_value":"RTTTQCPNVGTXDSL7","created_at":"2026-07-05T11:09:34Z"},{"alias_kind":"pith_short_8","alias_value":"RTTTQCPN","created_at":"2026-07-05T11:09:34Z"}],"graph_snapshots":[{"event_id":"sha256:a2f5c4f66fb9ef027aa79d935fccbce1c65e4c907d22e18a8b8788c5170de6fd","target":"graph","created_at":"2026-07-05T11:09:34Z","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/2505.19659/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical image segmentation models often struggle to generalize across different domains due to various reasons. Domain Generalization (DG) methods overcome this either through representation learning or data augmentation (DAug). While representation learning methods seek domain-invariant features, they often rely on ad-hoc techniques and lack formal guarantees. DAug methods, which enrich model representations through synthetic samples, have shown comparable or superior performance to representation learning approaches. We propose LangDAug, a novel $\\textbf{Lang}$evin $\\textbf{D}$ata $\\textbf{A","authors_text":"Kinjawl Bhattacharyya, Piyush Tiwary, Prathosh A.P","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-26T08:18:32Z","title":"LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19659","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":"sha256:28950978c31ff814dace2d6df6f3feea02e5575122fd7bacee1474f4c3b09e05","target":"record","created_at":"2026-07-05T11:09:34Z","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":"d8efca64a0174923a9c516dc991dfa81e565e6ae6aae4f3cb59ee804f795b327","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-26T08:18:32Z","title_canon_sha256":"83a2eb96d69250f7ae08f0bc5c2add1576c03d6eb1776d36a0a99d873013991b"},"schema_version":"1.0","source":{"id":"2505.19659","kind":"arxiv","version":1}},"canonical_sha256":"8ce73809eda9a771c97f2e6325ad15840d59755bcc3df54b317c59a75e4766b0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ce73809eda9a771c97f2e6325ad15840d59755bcc3df54b317c59a75e4766b0","first_computed_at":"2026-07-05T11:09:34.160503Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:09:34.160503Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sbZ8tTGH22ASHsf6kuWJOobIP0t/Q4CwZ+uXVm3RD/fL76Pl4729Q+1MzKIUCif8+FTum1MHiLwTPmwuL0YiBg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:09:34.160901Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.19659","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:28950978c31ff814dace2d6df6f3feea02e5575122fd7bacee1474f4c3b09e05","sha256:a2f5c4f66fb9ef027aa79d935fccbce1c65e4c907d22e18a8b8788c5170de6fd"],"state_sha256":"a0e3ce64fcd625bdd20a90433b926758fcbcbd4cc0b1eea94ee3970a405d1a7d"}