{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2AVGWXCDJGUJKGRGVACGTDUI3K","short_pith_number":"pith:2AVGWXCD","schema_version":"1.0","canonical_sha256":"d02a6b5c4349a8951a26a804698e88dabbdc3adbf7743f343c85fc010ea31539","source":{"kind":"arxiv","id":"2506.20254","version":2},"attestation_state":"computed","paper":{"title":"Recognizing Surgical Phases Anywhere: Few-Shot Test-time Adaptation and Task-graph Guided Refinement","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Christian Heiliger, Ege \\\"Ozsoy, Hongliang Ren, Joel L. Lavanchy, Kun Yuan, Long Bai, Nassir Navab, Nicolas Padoy, Shi Li, Tingxuan Chen, Vinkle Srivastav, Yiming Huang","submitted_at":"2025-06-25T08:56:13Z","abstract_excerpt":"The complexity and diversity of surgical workflows, driven by heterogeneous operating room settings, institutional protocols, and anatomical variability, present a significant challenge in developing generalizable models for cross-institutional and cross-procedural surgical understanding. While recent surgical foundation models pretrained on large-scale vision-language data offer promising transferability, their zero-shot performance remains constrained by domain shifts, limiting their utility in unseen surgical environments. To address this, we introduce Surgical Phase Anywhere (SPA), a light"},"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":"2506.20254","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-25T08:56:13Z","cross_cats_sorted":[],"title_canon_sha256":"30b3d352a5efc4925c925dc0f5d77427d24e5d0d57533643baf74b91807362f2","abstract_canon_sha256":"c7c10e72b2a5a59501dcc1a179eec05e47efee922e9ad9bbce593389a905df6b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:36:56.145503Z","signature_b64":"UpHnCTnZyP6Ux6Aj130ifSyJWyXYLByfiplKouq49yOr0SOHn3huIkCuUtpp+moIupXg3Q7YGhKLz1If7UH/BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d02a6b5c4349a8951a26a804698e88dabbdc3adbf7743f343c85fc010ea31539","last_reissued_at":"2026-07-05T11:36:56.145020Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:36:56.145020Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recognizing Surgical Phases Anywhere: Few-Shot Test-time Adaptation and Task-graph Guided Refinement","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Christian Heiliger, Ege \\\"Ozsoy, Hongliang Ren, Joel L. Lavanchy, Kun Yuan, Long Bai, Nassir Navab, Nicolas Padoy, Shi Li, Tingxuan Chen, Vinkle Srivastav, Yiming Huang","submitted_at":"2025-06-25T08:56:13Z","abstract_excerpt":"The complexity and diversity of surgical workflows, driven by heterogeneous operating room settings, institutional protocols, and anatomical variability, present a significant challenge in developing generalizable models for cross-institutional and cross-procedural surgical understanding. While recent surgical foundation models pretrained on large-scale vision-language data offer promising transferability, their zero-shot performance remains constrained by domain shifts, limiting their utility in unseen surgical environments. To address this, we introduce Surgical Phase Anywhere (SPA), a light"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.20254","kind":"arxiv","version":2},"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/2506.20254/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":"2506.20254","created_at":"2026-07-05T11:36:56.145080+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.20254v2","created_at":"2026-07-05T11:36:56.145080+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.20254","created_at":"2026-07-05T11:36:56.145080+00:00"},{"alias_kind":"pith_short_12","alias_value":"2AVGWXCDJGUJ","created_at":"2026-07-05T11:36:56.145080+00:00"},{"alias_kind":"pith_short_16","alias_value":"2AVGWXCDJGUJKGRG","created_at":"2026-07-05T11:36:56.145080+00:00"},{"alias_kind":"pith_short_8","alias_value":"2AVGWXCD","created_at":"2026-07-05T11:36:56.145080+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.09294","citing_title":"Geo-RepNet: Geometry-Aware Representation Learning for Surgical Phase Recognition in Endoscopic Submucosal Dissection","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2AVGWXCDJGUJKGRGVACGTDUI3K","json":"https://pith.science/pith/2AVGWXCDJGUJKGRGVACGTDUI3K.json","graph_json":"https://pith.science/api/pith-number/2AVGWXCDJGUJKGRGVACGTDUI3K/graph.json","events_json":"https://pith.science/api/pith-number/2AVGWXCDJGUJKGRGVACGTDUI3K/events.json","paper":"https://pith.science/paper/2AVGWXCD"},"agent_actions":{"view_html":"https://pith.science/pith/2AVGWXCDJGUJKGRGVACGTDUI3K","download_json":"https://pith.science/pith/2AVGWXCDJGUJKGRGVACGTDUI3K.json","view_paper":"https://pith.science/paper/2AVGWXCD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.20254&json=true","fetch_graph":"https://pith.science/api/pith-number/2AVGWXCDJGUJKGRGVACGTDUI3K/graph.json","fetch_events":"https://pith.science/api/pith-number/2AVGWXCDJGUJKGRGVACGTDUI3K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2AVGWXCDJGUJKGRGVACGTDUI3K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2AVGWXCDJGUJKGRGVACGTDUI3K/action/storage_attestation","attest_author":"https://pith.science/pith/2AVGWXCDJGUJKGRGVACGTDUI3K/action/author_attestation","sign_citation":"https://pith.science/pith/2AVGWXCDJGUJKGRGVACGTDUI3K/action/citation_signature","submit_replication":"https://pith.science/pith/2AVGWXCDJGUJKGRGVACGTDUI3K/action/replication_record"}},"created_at":"2026-07-05T11:36:56.145080+00:00","updated_at":"2026-07-05T11:36:56.145080+00:00"}