{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:Y5KKNTDZ46K65AASFL7KTMBDP3","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":"6f73507c0549ee40597c7481ff9b89685c0aa404e4cfc93df714df78fe1d8987","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-08T03:02:57Z","title_canon_sha256":"a0f5c5d3fac1109c4739d1a57217e91c6a9c731270087ebb5bdc1626dbcea8fa"},"schema_version":"1.0","source":{"id":"2503.06030","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.06030","created_at":"2026-07-05T10:26:52Z"},{"alias_kind":"arxiv_version","alias_value":"2503.06030v1","created_at":"2026-07-05T10:26:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.06030","created_at":"2026-07-05T10:26:52Z"},{"alias_kind":"pith_short_12","alias_value":"Y5KKNTDZ46K6","created_at":"2026-07-05T10:26:52Z"},{"alias_kind":"pith_short_16","alias_value":"Y5KKNTDZ46K65AAS","created_at":"2026-07-05T10:26:52Z"},{"alias_kind":"pith_short_8","alias_value":"Y5KKNTDZ","created_at":"2026-07-05T10:26:52Z"}],"graph_snapshots":[{"event_id":"sha256:a7db54e8667d2cf73495dd770849388f22912d7cab823ab2b78cf60f9b976f4c","target":"graph","created_at":"2026-07-05T10:26:52Z","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/2503.06030/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Computed tomography (CT) is extensively used for accurate visualization and segmentation of organs and lesions. While deep learning models such as convolutional neural networks (CNNs) and vision transformers (ViTs) have significantly improved CT image analysis, their performance often declines when applied to diverse, real-world clinical data. Although foundation models offer a broader and more adaptable solution, their potential is limited due to the challenge of obtaining large-scale, voxel-level annotations for medical images. In response to these challenges, prompting-based models using vi","authors_text":"David S. Yu, Deborah Marshall, Maria Thor, Xiaofeng Yang, Yuheng Li, Yuxiang Lai, Zachary Buchwald","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-08T03:02:57Z","title":"Towards Universal Text-driven CT Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.06030","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:f47586f09f257d4a026fe000c3ccfe8f4d844eebcf8721e109dcd4afca488cab","target":"record","created_at":"2026-07-05T10:26:52Z","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":"6f73507c0549ee40597c7481ff9b89685c0aa404e4cfc93df714df78fe1d8987","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-08T03:02:57Z","title_canon_sha256":"a0f5c5d3fac1109c4739d1a57217e91c6a9c731270087ebb5bdc1626dbcea8fa"},"schema_version":"1.0","source":{"id":"2503.06030","kind":"arxiv","version":1}},"canonical_sha256":"c754a6cc79e795ee80122afea9b0237ee11cedf4c236c05cc6a868421236538d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c754a6cc79e795ee80122afea9b0237ee11cedf4c236c05cc6a868421236538d","first_computed_at":"2026-07-05T10:26:52.186987Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:26:52.186987Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MbMFcQ9NzXdfEe4NOvRgwPvBOJDvObyax2qd2oeGLaEp1c9IDauqNLXEiOsB3CHeL3+BBmWC5mTP+jVLjTouDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:26:52.187678Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.06030","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f47586f09f257d4a026fe000c3ccfe8f4d844eebcf8721e109dcd4afca488cab","sha256:a7db54e8667d2cf73495dd770849388f22912d7cab823ab2b78cf60f9b976f4c"],"state_sha256":"877da9773a6ec841183be33fb3775a23c48a778a374425e68449d1d3d52eb847"}