{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DSYQ426WPSZKYUIBRDS5VLCHZI","short_pith_number":"pith:DSYQ426W","schema_version":"1.0","canonical_sha256":"1cb10e6bd67cb2ac510188e5daac47ca03feef19561ef42411ee46b87e5d01f8","source":{"kind":"arxiv","id":"2504.13820","version":1},"attestation_state":"computed","paper":{"title":"CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenxin Tao, Gao Huang, Pan Liu, Shiji Song, Yang Yue, Yulin Wang","submitted_at":"2025-04-18T17:50:43Z","abstract_excerpt":"Humans can develop internal world models that encode common sense knowledge, telling them how the world works and predicting the consequences of their actions. This concept has emerged as a promising direction for establishing general-purpose machine-learning models in recent preliminary works, e.g., for visual representation learning. In this paper, we present CheXWorld, the first effort towards a self-supervised world model for radiographic images. Specifically, our work develops a unified framework that simultaneously models three aspects of medical knowledge essential for qualified radiolo"},"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":"2504.13820","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-18T17:50:43Z","cross_cats_sorted":[],"title_canon_sha256":"be584d5612a2f53f23314432c73970b8cd5e8f8abcb92f37b3efdc15e09ddc10","abstract_canon_sha256":"984a25604178b3456146a063ec4f33c31587c531a034305fce5f1803c41dc550"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:02.550367Z","signature_b64":"wDBh33EWYxRFQ3MIHJ7AMd0uU1xGYccq6+0tr2w38VGqcncStYuIE9rlcamizOmlx2u8GHD4Ca7M9tEE3uo2Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1cb10e6bd67cb2ac510188e5daac47ca03feef19561ef42411ee46b87e5d01f8","last_reissued_at":"2026-07-05T10:51:02.549898Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:02.549898Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenxin Tao, Gao Huang, Pan Liu, Shiji Song, Yang Yue, Yulin Wang","submitted_at":"2025-04-18T17:50:43Z","abstract_excerpt":"Humans can develop internal world models that encode common sense knowledge, telling them how the world works and predicting the consequences of their actions. This concept has emerged as a promising direction for establishing general-purpose machine-learning models in recent preliminary works, e.g., for visual representation learning. In this paper, we present CheXWorld, the first effort towards a self-supervised world model for radiographic images. Specifically, our work develops a unified framework that simultaneously models three aspects of medical knowledge essential for qualified radiolo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.13820","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/2504.13820/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":"2504.13820","created_at":"2026-07-05T10:51:02.549955+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.13820v1","created_at":"2026-07-05T10:51:02.549955+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.13820","created_at":"2026-07-05T10:51:02.549955+00:00"},{"alias_kind":"pith_short_12","alias_value":"DSYQ426WPSZK","created_at":"2026-07-05T10:51:02.549955+00:00"},{"alias_kind":"pith_short_16","alias_value":"DSYQ426WPSZKYUIB","created_at":"2026-07-05T10:51:02.549955+00:00"},{"alias_kind":"pith_short_8","alias_value":"DSYQ426W","created_at":"2026-07-05T10:51:02.549955+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/DSYQ426WPSZKYUIBRDS5VLCHZI","json":"https://pith.science/pith/DSYQ426WPSZKYUIBRDS5VLCHZI.json","graph_json":"https://pith.science/api/pith-number/DSYQ426WPSZKYUIBRDS5VLCHZI/graph.json","events_json":"https://pith.science/api/pith-number/DSYQ426WPSZKYUIBRDS5VLCHZI/events.json","paper":"https://pith.science/paper/DSYQ426W"},"agent_actions":{"view_html":"https://pith.science/pith/DSYQ426WPSZKYUIBRDS5VLCHZI","download_json":"https://pith.science/pith/DSYQ426WPSZKYUIBRDS5VLCHZI.json","view_paper":"https://pith.science/paper/DSYQ426W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.13820&json=true","fetch_graph":"https://pith.science/api/pith-number/DSYQ426WPSZKYUIBRDS5VLCHZI/graph.json","fetch_events":"https://pith.science/api/pith-number/DSYQ426WPSZKYUIBRDS5VLCHZI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DSYQ426WPSZKYUIBRDS5VLCHZI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DSYQ426WPSZKYUIBRDS5VLCHZI/action/storage_attestation","attest_author":"https://pith.science/pith/DSYQ426WPSZKYUIBRDS5VLCHZI/action/author_attestation","sign_citation":"https://pith.science/pith/DSYQ426WPSZKYUIBRDS5VLCHZI/action/citation_signature","submit_replication":"https://pith.science/pith/DSYQ426WPSZKYUIBRDS5VLCHZI/action/replication_record"}},"created_at":"2026-07-05T10:51:02.549955+00:00","updated_at":"2026-07-05T10:51:02.549955+00:00"}