{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RAQLMDDXTSL4OUZGUEDCEBOS6P","short_pith_number":"pith:RAQLMDDX","schema_version":"1.0","canonical_sha256":"8820b60c779c97c75326a1062205d2f3f87b4a307f941afb89e5e9dfc190c362","source":{"kind":"arxiv","id":"2607.09526","version":1},"attestation_state":"computed","paper":{"title":"ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Anjia Han, Chao He, Huijuan Shi, Jiawen Li, Mingxi Fu, Tian Guan, Xitong Ling, Yonghong He","submitted_at":"2026-07-10T15:35:06Z","abstract_excerpt":"Foundation models are reshaping computational pathology, yet their capabilities remain shaped by pretraining objectives, data sources, and spatial scales, fragmenting complementary expertise across separate backbones. Here we present ALICE, a unified foundation model trained through multi-stage agglomerative distillation that sequentially distills eight vision-only, vision-language, and slide-level teacher models into dedicated modules of a single backbone. ALICE is pretrained on 24,985,184 tile-level pathology images and 155,604 high-resolution images, and evaluated across 21 task scenarios, "},"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":"2607.09526","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-10T15:35:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"dbc289e9c2cba825882175820795833cec3973c3b3db363e1a4cbf58c111719c","abstract_canon_sha256":"bcf9175383645b1a84c9524aa96b39321c0424d056a739dd8319edd55228b318"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-13T01:20:16.381261Z","signature_b64":"R/zVmVZjNYz4kOHvdcqOqVBa6YRsB5C3ALOsy49fRKJIoykR1z9Qk9MUHb3mLbZVjAQRpk3tRvO/RJzv0VSaCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8820b60c779c97c75326a1062205d2f3f87b4a307f941afb89e5e9dfc190c362","last_reissued_at":"2026-07-13T01:20:16.380128Z","signature_status":"signed_v1","first_computed_at":"2026-07-13T01:20:16.380128Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Anjia Han, Chao He, Huijuan Shi, Jiawen Li, Mingxi Fu, Tian Guan, Xitong Ling, Yonghong He","submitted_at":"2026-07-10T15:35:06Z","abstract_excerpt":"Foundation models are reshaping computational pathology, yet their capabilities remain shaped by pretraining objectives, data sources, and spatial scales, fragmenting complementary expertise across separate backbones. Here we present ALICE, a unified foundation model trained through multi-stage agglomerative distillation that sequentially distills eight vision-only, vision-language, and slide-level teacher models into dedicated modules of a single backbone. ALICE is pretrained on 24,985,184 tile-level pathology images and 155,604 high-resolution images, and evaluated across 21 task scenarios, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.09526","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/2607.09526/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":"2607.09526","created_at":"2026-07-13T01:20:16.380722+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.09526v1","created_at":"2026-07-13T01:20:16.380722+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.09526","created_at":"2026-07-13T01:20:16.380722+00:00"},{"alias_kind":"pith_short_12","alias_value":"RAQLMDDXTSL4","created_at":"2026-07-13T01:20:16.380722+00:00"},{"alias_kind":"pith_short_16","alias_value":"RAQLMDDXTSL4OUZG","created_at":"2026-07-13T01:20:16.380722+00:00"},{"alias_kind":"pith_short_8","alias_value":"RAQLMDDX","created_at":"2026-07-13T01:20:16.380722+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/RAQLMDDXTSL4OUZGUEDCEBOS6P","json":"https://pith.science/pith/RAQLMDDXTSL4OUZGUEDCEBOS6P.json","graph_json":"https://pith.science/api/pith-number/RAQLMDDXTSL4OUZGUEDCEBOS6P/graph.json","events_json":"https://pith.science/api/pith-number/RAQLMDDXTSL4OUZGUEDCEBOS6P/events.json","paper":"https://pith.science/paper/RAQLMDDX"},"agent_actions":{"view_html":"https://pith.science/pith/RAQLMDDXTSL4OUZGUEDCEBOS6P","download_json":"https://pith.science/pith/RAQLMDDXTSL4OUZGUEDCEBOS6P.json","view_paper":"https://pith.science/paper/RAQLMDDX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.09526&json=true","fetch_graph":"https://pith.science/api/pith-number/RAQLMDDXTSL4OUZGUEDCEBOS6P/graph.json","fetch_events":"https://pith.science/api/pith-number/RAQLMDDXTSL4OUZGUEDCEBOS6P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RAQLMDDXTSL4OUZGUEDCEBOS6P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RAQLMDDXTSL4OUZGUEDCEBOS6P/action/storage_attestation","attest_author":"https://pith.science/pith/RAQLMDDXTSL4OUZGUEDCEBOS6P/action/author_attestation","sign_citation":"https://pith.science/pith/RAQLMDDXTSL4OUZGUEDCEBOS6P/action/citation_signature","submit_replication":"https://pith.science/pith/RAQLMDDXTSL4OUZGUEDCEBOS6P/action/replication_record"}},"created_at":"2026-07-13T01:20:16.380722+00:00","updated_at":"2026-07-13T01:20:16.380722+00:00"}