{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ALFEATVMGQHI7RDZR7P2BSXN6R","short_pith_number":"pith:ALFEATVM","schema_version":"1.0","canonical_sha256":"02ca404eac340e8fc4798fdfa0caedf44c9410086ce709bac7fc64203d365fc0","source":{"kind":"arxiv","id":"2410.07540","version":1},"attestation_state":"computed","paper":{"title":"CoPESD: A Multi-Level Surgical Motion Dataset for Training Large Vision-Language Models to Co-Pilot Endoscopic Submucosal Dissection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guankun Wang, Han Xiao, Hongliang Ren, Hongsheng Li, Huxin Gao, Long Bai, Renrui Zhang, Xiaoxiao Yang, Zhen Li","submitted_at":"2024-10-10T02:22:09Z","abstract_excerpt":"submucosal dissection (ESD) enables rapid resection of large lesions, minimizing recurrence rates and improving long-term overall survival. Despite these advantages, ESD is technically challenging and carries high risks of complications, necessitating skilled surgeons and precise instruments. Recent advancements in Large Visual-Language Models (LVLMs) offer promising decision support and predictive planning capabilities for robotic systems, which can augment the accuracy of ESD and reduce procedural risks. However, existing datasets for multi-level fine-grained ESD surgical motion understandin"},"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":"2410.07540","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-10T02:22:09Z","cross_cats_sorted":[],"title_canon_sha256":"ddd34b40163e7fa65225298577ed8d392605a3ad44dca19ffb6f3d295ad9be69","abstract_canon_sha256":"6bfd6eb59f1d40a45f1b7a0f3a05749de91b19c9aa0eee36dbfad33e98078fdc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:18:39.300405Z","signature_b64":"NxL0CvduWN6ma5xmgsaKW0FTxXRx/6dqyRlE4cmSWd57N/0NDepoBXXxBHC9+VoDg8zzmkSP5ORGcMYHLU+kCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02ca404eac340e8fc4798fdfa0caedf44c9410086ce709bac7fc64203d365fc0","last_reissued_at":"2026-07-05T09:18:39.299752Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:18:39.299752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CoPESD: A Multi-Level Surgical Motion Dataset for Training Large Vision-Language Models to Co-Pilot Endoscopic Submucosal Dissection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guankun Wang, Han Xiao, Hongliang Ren, Hongsheng Li, Huxin Gao, Long Bai, Renrui Zhang, Xiaoxiao Yang, Zhen Li","submitted_at":"2024-10-10T02:22:09Z","abstract_excerpt":"submucosal dissection (ESD) enables rapid resection of large lesions, minimizing recurrence rates and improving long-term overall survival. Despite these advantages, ESD is technically challenging and carries high risks of complications, necessitating skilled surgeons and precise instruments. Recent advancements in Large Visual-Language Models (LVLMs) offer promising decision support and predictive planning capabilities for robotic systems, which can augment the accuracy of ESD and reduce procedural risks. However, existing datasets for multi-level fine-grained ESD surgical motion understandin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.07540","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/2410.07540/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":"2410.07540","created_at":"2026-07-05T09:18:39.299873+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.07540v1","created_at":"2026-07-05T09:18:39.299873+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.07540","created_at":"2026-07-05T09:18:39.299873+00:00"},{"alias_kind":"pith_short_12","alias_value":"ALFEATVMGQHI","created_at":"2026-07-05T09:18:39.299873+00:00"},{"alias_kind":"pith_short_16","alias_value":"ALFEATVMGQHI7RDZ","created_at":"2026-07-05T09:18:39.299873+00:00"},{"alias_kind":"pith_short_8","alias_value":"ALFEATVM","created_at":"2026-07-05T09:18:39.299873+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.06581","citing_title":"MedGRPO: Multi-Task Reinforcement Learning for Heterogeneous Medical Video Understanding","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ALFEATVMGQHI7RDZR7P2BSXN6R","json":"https://pith.science/pith/ALFEATVMGQHI7RDZR7P2BSXN6R.json","graph_json":"https://pith.science/api/pith-number/ALFEATVMGQHI7RDZR7P2BSXN6R/graph.json","events_json":"https://pith.science/api/pith-number/ALFEATVMGQHI7RDZR7P2BSXN6R/events.json","paper":"https://pith.science/paper/ALFEATVM"},"agent_actions":{"view_html":"https://pith.science/pith/ALFEATVMGQHI7RDZR7P2BSXN6R","download_json":"https://pith.science/pith/ALFEATVMGQHI7RDZR7P2BSXN6R.json","view_paper":"https://pith.science/paper/ALFEATVM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.07540&json=true","fetch_graph":"https://pith.science/api/pith-number/ALFEATVMGQHI7RDZR7P2BSXN6R/graph.json","fetch_events":"https://pith.science/api/pith-number/ALFEATVMGQHI7RDZR7P2BSXN6R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ALFEATVMGQHI7RDZR7P2BSXN6R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ALFEATVMGQHI7RDZR7P2BSXN6R/action/storage_attestation","attest_author":"https://pith.science/pith/ALFEATVMGQHI7RDZR7P2BSXN6R/action/author_attestation","sign_citation":"https://pith.science/pith/ALFEATVMGQHI7RDZR7P2BSXN6R/action/citation_signature","submit_replication":"https://pith.science/pith/ALFEATVMGQHI7RDZR7P2BSXN6R/action/replication_record"}},"created_at":"2026-07-05T09:18:39.299873+00:00","updated_at":"2026-07-05T09:18:39.299873+00:00"}