{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GDYH2NOO6HZQ6XPRKJXYFIUJW4","short_pith_number":"pith:GDYH2NOO","schema_version":"1.0","canonical_sha256":"30f07d35cef1f30f5df1526f82a289b736b458f79c126a2936b7a1dba36206bd","source":{"kind":"arxiv","id":"2507.04613","version":1},"attestation_state":"computed","paper":{"title":"HiLa: Hierarchical Vision-Language Collaboration for Cancer Survival Prediction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bo Liu, Dinggang Shen, Jiaqi Cui, Luping Zhou, Lu Wen, Yan Wang, Yuchen Fei","submitted_at":"2025-07-07T02:06:25Z","abstract_excerpt":"Survival prediction using whole-slide images (WSIs) is crucial in cancer re-search. Despite notable success, existing approaches are limited by their reliance on sparse slide-level labels, which hinders the learning of discriminative repre-sentations from gigapixel WSIs. Recently, vision language (VL) models, which incorporate additional language supervision, have emerged as a promising solu-tion. However, VL-based survival prediction remains largely unexplored due to two key challenges. First, current methods often rely on only one simple lan-guage prompt and basic cosine similarity, which fa"},"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":"2507.04613","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-07T02:06:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a0e4625161dfea12c7f007b61849e5b796df615b5efb9177692cba6b10067a03","abstract_canon_sha256":"da61c70ac1b52003b3cddd0a3ef8876668b61e0bd53383e439ce7469e0425bc1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:32:50.664097Z","signature_b64":"wwRTBWtBg8B20OrnI0yjP4dDbobUFpRYFk5WFX6oMD1/hJJaKQ03nMtYbNiU3UtMxRE2g8gu3NaEhKpAMhA4CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"30f07d35cef1f30f5df1526f82a289b736b458f79c126a2936b7a1dba36206bd","last_reissued_at":"2026-07-05T11:32:50.663600Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:32:50.663600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HiLa: Hierarchical Vision-Language Collaboration for Cancer Survival Prediction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bo Liu, Dinggang Shen, Jiaqi Cui, Luping Zhou, Lu Wen, Yan Wang, Yuchen Fei","submitted_at":"2025-07-07T02:06:25Z","abstract_excerpt":"Survival prediction using whole-slide images (WSIs) is crucial in cancer re-search. Despite notable success, existing approaches are limited by their reliance on sparse slide-level labels, which hinders the learning of discriminative repre-sentations from gigapixel WSIs. Recently, vision language (VL) models, which incorporate additional language supervision, have emerged as a promising solu-tion. However, VL-based survival prediction remains largely unexplored due to two key challenges. First, current methods often rely on only one simple lan-guage prompt and basic cosine similarity, which fa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.04613","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/2507.04613/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":"2507.04613","created_at":"2026-07-05T11:32:50.663659+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.04613v1","created_at":"2026-07-05T11:32:50.663659+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.04613","created_at":"2026-07-05T11:32:50.663659+00:00"},{"alias_kind":"pith_short_12","alias_value":"GDYH2NOO6HZQ","created_at":"2026-07-05T11:32:50.663659+00:00"},{"alias_kind":"pith_short_16","alias_value":"GDYH2NOO6HZQ6XPR","created_at":"2026-07-05T11:32:50.663659+00:00"},{"alias_kind":"pith_short_8","alias_value":"GDYH2NOO","created_at":"2026-07-05T11:32:50.663659+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/GDYH2NOO6HZQ6XPRKJXYFIUJW4","json":"https://pith.science/pith/GDYH2NOO6HZQ6XPRKJXYFIUJW4.json","graph_json":"https://pith.science/api/pith-number/GDYH2NOO6HZQ6XPRKJXYFIUJW4/graph.json","events_json":"https://pith.science/api/pith-number/GDYH2NOO6HZQ6XPRKJXYFIUJW4/events.json","paper":"https://pith.science/paper/GDYH2NOO"},"agent_actions":{"view_html":"https://pith.science/pith/GDYH2NOO6HZQ6XPRKJXYFIUJW4","download_json":"https://pith.science/pith/GDYH2NOO6HZQ6XPRKJXYFIUJW4.json","view_paper":"https://pith.science/paper/GDYH2NOO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.04613&json=true","fetch_graph":"https://pith.science/api/pith-number/GDYH2NOO6HZQ6XPRKJXYFIUJW4/graph.json","fetch_events":"https://pith.science/api/pith-number/GDYH2NOO6HZQ6XPRKJXYFIUJW4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GDYH2NOO6HZQ6XPRKJXYFIUJW4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GDYH2NOO6HZQ6XPRKJXYFIUJW4/action/storage_attestation","attest_author":"https://pith.science/pith/GDYH2NOO6HZQ6XPRKJXYFIUJW4/action/author_attestation","sign_citation":"https://pith.science/pith/GDYH2NOO6HZQ6XPRKJXYFIUJW4/action/citation_signature","submit_replication":"https://pith.science/pith/GDYH2NOO6HZQ6XPRKJXYFIUJW4/action/replication_record"}},"created_at":"2026-07-05T11:32:50.663659+00:00","updated_at":"2026-07-05T11:32:50.663659+00:00"}