{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2QLFMM6SVQBQI7UZCSLSP6QX7G","short_pith_number":"pith:2QLFMM6S","schema_version":"1.0","canonical_sha256":"d4165633d2ac03047e99149727fa17f9adf3969f7aea5f977dc5e940b3dda489","source":{"kind":"arxiv","id":"2506.22446","version":1},"attestation_state":"computed","paper":{"title":"EAGLE: Efficient Alignment of Generalized Latent Embeddings for Multimodal Survival Prediction with Interpretable Attribution Analysis","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Aakash Tripathi, Asim Waqas, Ghulam Rasool, Matthew B. Schabath, Yasin Yilmaz","submitted_at":"2025-06-12T03:56:13Z","abstract_excerpt":"Accurate cancer survival prediction requires integration of diverse data modalities that reflect the complex interplay between imaging, clinical parameters, and textual reports. However, existing multimodal approaches suffer from simplistic fusion strategies, massive computational requirements, and lack of interpretability-critical barriers to clinical adoption. We present EAGLE (Efficient Alignment of Generalized Latent Embeddings), a novel deep learning framework that addresses these limitations through attention-based multimodal fusion with comprehensive attribution analysis. EAGLE introduc"},"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":"2506.22446","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-12T03:56:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"27bcbdcfa165dd1fda627495898749e466997021f97232aec12204db988aa636","abstract_canon_sha256":"d238f5cdf9f5f12fb66d0f7e1405d6edc5c999fc0f0456204c7cc5af5b2a9e04"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:28:38.647269Z","signature_b64":"hbWkl2f+bvTIGgupjtTpnCc621m9WFY3gtOWNI69VnlkpOSLKQ35mUtJyBgx8syFzUdWKBnYdOT+1pg+r4emCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4165633d2ac03047e99149727fa17f9adf3969f7aea5f977dc5e940b3dda489","last_reissued_at":"2026-07-05T11:28:38.646816Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:28:38.646816Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EAGLE: Efficient Alignment of Generalized Latent Embeddings for Multimodal Survival Prediction with Interpretable Attribution Analysis","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Aakash Tripathi, Asim Waqas, Ghulam Rasool, Matthew B. Schabath, Yasin Yilmaz","submitted_at":"2025-06-12T03:56:13Z","abstract_excerpt":"Accurate cancer survival prediction requires integration of diverse data modalities that reflect the complex interplay between imaging, clinical parameters, and textual reports. However, existing multimodal approaches suffer from simplistic fusion strategies, massive computational requirements, and lack of interpretability-critical barriers to clinical adoption. We present EAGLE (Efficient Alignment of Generalized Latent Embeddings), a novel deep learning framework that addresses these limitations through attention-based multimodal fusion with comprehensive attribution analysis. EAGLE introduc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.22446","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/2506.22446/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":"2506.22446","created_at":"2026-07-05T11:28:38.646876+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.22446v1","created_at":"2026-07-05T11:28:38.646876+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.22446","created_at":"2026-07-05T11:28:38.646876+00:00"},{"alias_kind":"pith_short_12","alias_value":"2QLFMM6SVQBQ","created_at":"2026-07-05T11:28:38.646876+00:00"},{"alias_kind":"pith_short_16","alias_value":"2QLFMM6SVQBQI7UZ","created_at":"2026-07-05T11:28:38.646876+00:00"},{"alias_kind":"pith_short_8","alias_value":"2QLFMM6S","created_at":"2026-07-05T11:28:38.646876+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/2QLFMM6SVQBQI7UZCSLSP6QX7G","json":"https://pith.science/pith/2QLFMM6SVQBQI7UZCSLSP6QX7G.json","graph_json":"https://pith.science/api/pith-number/2QLFMM6SVQBQI7UZCSLSP6QX7G/graph.json","events_json":"https://pith.science/api/pith-number/2QLFMM6SVQBQI7UZCSLSP6QX7G/events.json","paper":"https://pith.science/paper/2QLFMM6S"},"agent_actions":{"view_html":"https://pith.science/pith/2QLFMM6SVQBQI7UZCSLSP6QX7G","download_json":"https://pith.science/pith/2QLFMM6SVQBQI7UZCSLSP6QX7G.json","view_paper":"https://pith.science/paper/2QLFMM6S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.22446&json=true","fetch_graph":"https://pith.science/api/pith-number/2QLFMM6SVQBQI7UZCSLSP6QX7G/graph.json","fetch_events":"https://pith.science/api/pith-number/2QLFMM6SVQBQI7UZCSLSP6QX7G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2QLFMM6SVQBQI7UZCSLSP6QX7G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2QLFMM6SVQBQI7UZCSLSP6QX7G/action/storage_attestation","attest_author":"https://pith.science/pith/2QLFMM6SVQBQI7UZCSLSP6QX7G/action/author_attestation","sign_citation":"https://pith.science/pith/2QLFMM6SVQBQI7UZCSLSP6QX7G/action/citation_signature","submit_replication":"https://pith.science/pith/2QLFMM6SVQBQI7UZCSLSP6QX7G/action/replication_record"}},"created_at":"2026-07-05T11:28:38.646876+00:00","updated_at":"2026-07-05T11:28:38.646876+00:00"}