{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:57RPXIOZAEO2456UTDQGWFMT24","short_pith_number":"pith:57RPXIOZ","schema_version":"1.0","canonical_sha256":"efe2fba1d9011dae77d498e06b1593d70c16780d645ccf473a8b6b01a426a4e9","source":{"kind":"arxiv","id":"2506.02327","version":1},"attestation_state":"computed","paper":{"title":"Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alan Yuille, Jieneng Chen, Kang Wang, Lei Zhu, Qiuping Liu, Rama Chellappa, Shuwen Sun, Yijun Yang, Yu-Dong Zhang, Zhao-Yang Wang, Zongwei Zhou","submitted_at":"2025-06-02T23:50:40Z","abstract_excerpt":"Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in simulating disease dynamics for clinical decision-making, leveraging recent advances in large generative models. To this end, we introduce the Medical World Model (MeWM), the first world model in medicine that visually predicts future disease states based on clinical decisions. MeWM comprises (i) vision-language models to serve as policy models, and (ii) tumor generative models as dynamics models. The policy model generates action plans, such as cl"},"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":"2506.02327","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-02T23:50:40Z","cross_cats_sorted":[],"title_canon_sha256":"78bd876f5499955a3c1b58d5db41aa6030cb7aaea41fe676f4402a9423d2a1ab","abstract_canon_sha256":"8ba19d04080dca69701901447c79ff3673cf32e3e4dabddd1983222d364cf89f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:36.480339Z","signature_b64":"rYru8n8sDx3ooj8nDH5Lx28MhIk85xSSf8m7ZAgoP1Kd92UyjWc+TIZBW5yCfdWsrh75+56gUdBLxe+XvFduDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efe2fba1d9011dae77d498e06b1593d70c16780d645ccf473a8b6b01a426a4e9","last_reissued_at":"2026-07-05T11:14:36.479916Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:36.479916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alan Yuille, Jieneng Chen, Kang Wang, Lei Zhu, Qiuping Liu, Rama Chellappa, Shuwen Sun, Yijun Yang, Yu-Dong Zhang, Zhao-Yang Wang, Zongwei Zhou","submitted_at":"2025-06-02T23:50:40Z","abstract_excerpt":"Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in simulating disease dynamics for clinical decision-making, leveraging recent advances in large generative models. To this end, we introduce the Medical World Model (MeWM), the first world model in medicine that visually predicts future disease states based on clinical decisions. MeWM comprises (i) vision-language models to serve as policy models, and (ii) tumor generative models as dynamics models. The policy model generates action plans, such as cl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.02327","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/2506.02327/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":"2506.02327","created_at":"2026-07-05T11:14:36.479969+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.02327v1","created_at":"2026-07-05T11:14:36.479969+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.02327","created_at":"2026-07-05T11:14:36.479969+00:00"},{"alias_kind":"pith_short_12","alias_value":"57RPXIOZAEO2","created_at":"2026-07-05T11:14:36.479969+00:00"},{"alias_kind":"pith_short_16","alias_value":"57RPXIOZAEO2456U","created_at":"2026-07-05T11:14:36.479969+00:00"},{"alias_kind":"pith_short_8","alias_value":"57RPXIOZ","created_at":"2026-07-05T11:14:36.479969+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06401","citing_title":"A Definition and Roadmap for World Models","ref_index":223,"is_internal_anchor":true},{"citing_arxiv_id":"2606.00133","citing_title":"World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications","ref_index":135,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12191","citing_title":"Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application","ref_index":195,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17580","citing_title":"ECG-WM: A Physiology-Informed ECG World Model for Clinical Intervention Simulation","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16927","citing_title":"From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction","ref_index":104,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11438","citing_title":"Beyond Masks: The Case for Medical Image Parsing","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10761","citing_title":"RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology","ref_index":113,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22618","citing_title":"Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07329","citing_title":"Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling","ref_index":37,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/57RPXIOZAEO2456UTDQGWFMT24","json":"https://pith.science/pith/57RPXIOZAEO2456UTDQGWFMT24.json","graph_json":"https://pith.science/api/pith-number/57RPXIOZAEO2456UTDQGWFMT24/graph.json","events_json":"https://pith.science/api/pith-number/57RPXIOZAEO2456UTDQGWFMT24/events.json","paper":"https://pith.science/paper/57RPXIOZ"},"agent_actions":{"view_html":"https://pith.science/pith/57RPXIOZAEO2456UTDQGWFMT24","download_json":"https://pith.science/pith/57RPXIOZAEO2456UTDQGWFMT24.json","view_paper":"https://pith.science/paper/57RPXIOZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.02327&json=true","fetch_graph":"https://pith.science/api/pith-number/57RPXIOZAEO2456UTDQGWFMT24/graph.json","fetch_events":"https://pith.science/api/pith-number/57RPXIOZAEO2456UTDQGWFMT24/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/57RPXIOZAEO2456UTDQGWFMT24/action/timestamp_anchor","attest_storage":"https://pith.science/pith/57RPXIOZAEO2456UTDQGWFMT24/action/storage_attestation","attest_author":"https://pith.science/pith/57RPXIOZAEO2456UTDQGWFMT24/action/author_attestation","sign_citation":"https://pith.science/pith/57RPXIOZAEO2456UTDQGWFMT24/action/citation_signature","submit_replication":"https://pith.science/pith/57RPXIOZAEO2456UTDQGWFMT24/action/replication_record"}},"created_at":"2026-07-05T11:14:36.479969+00:00","updated_at":"2026-07-05T11:14:36.479969+00:00"}