{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:GEWCVYRCBADWTMA3ELBDH4NO4O","short_pith_number":"pith:GEWCVYRC","canonical_record":{"source":{"id":"2605.16208","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-15T17:25:17Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"857b71cb9d2a3715725ddc2ec41499c3295a8a5afe5305c51c9ec99a2b484604","abstract_canon_sha256":"efe91c570509b25f6a23c92bd2266a303a6c54e04ddb2532ee54f5feee132d42"},"schema_version":"1.0"},"canonical_sha256":"312c2ae222080769b01b22c233f1aee394324dd2873b0f5bc41e468b14a2114d","source":{"kind":"arxiv","id":"2605.16208","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16208","created_at":"2026-05-20T00:01:58Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16208v1","created_at":"2026-05-20T00:01:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16208","created_at":"2026-05-20T00:01:58Z"},{"alias_kind":"pith_short_12","alias_value":"GEWCVYRCBADW","created_at":"2026-05-20T00:01:58Z"},{"alias_kind":"pith_short_16","alias_value":"GEWCVYRCBADWTMA3","created_at":"2026-05-20T00:01:58Z"},{"alias_kind":"pith_short_8","alias_value":"GEWCVYRC","created_at":"2026-05-20T00:01:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:GEWCVYRCBADWTMA3ELBDH4NO4O","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16208","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-15T17:25:17Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"857b71cb9d2a3715725ddc2ec41499c3295a8a5afe5305c51c9ec99a2b484604","abstract_canon_sha256":"efe91c570509b25f6a23c92bd2266a303a6c54e04ddb2532ee54f5feee132d42"},"schema_version":"1.0"},"canonical_sha256":"312c2ae222080769b01b22c233f1aee394324dd2873b0f5bc41e468b14a2114d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:58.115228Z","signature_b64":"uB6TWmvNq2KcHwAjUUrH5eEXU8JoTlXn+QLkd4q6qWTwgwJG05F9ZvDTLlI5UNQSjQpLN7iWncV/KsqBrqTFCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"312c2ae222080769b01b22c233f1aee394324dd2873b0f5bc41e468b14a2114d","last_reissued_at":"2026-05-20T00:01:58.114450Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:58.114450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16208","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:01:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ekOklUpEwJaMONSoCS67h8iGu4xSbtzBCuIEoiARCX4dczTafXaolIgirgJDVeT9ATHpm+mvC10PTS4OniL+CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T16:23:12.311030Z"},"content_sha256":"04a4662f85a962c0d4be31c7a074aa45bd42afaa9061ed736db62538a095b166","schema_version":"1.0","event_id":"sha256:04a4662f85a962c0d4be31c7a074aa45bd42afaa9061ed736db62538a095b166"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:GEWCVYRCBADWTMA3ELBDH4NO4O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"QSurv approximates cumulative hazards via Gauss-Legendre quadrature to enable scalable nonparametric continuous-time survival modeling in deep networks.","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Chaeyeon Lee, Hyungrok Do, Sehwan Kim","submitted_at":"2026-05-15T17:25:17Z","abstract_excerpt":"Flexible continuous-time survival modeling is critical for capturing complex time-varying hazard dynamics in high-dimensional data; however, training such models remains challenging due to the intractable integral required for likelihood estimation. We introduce QSurv, a scalable deep learning framework that enables nonparametric continuous-time modeling without relying on time discretization or restrictive distributional assumptions. We propose a training objective based on Gauss-Legendre numerical quadrature, which approximates the cumulative hazard with high-order accuracy while facilitatin"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We introduce QSurv, a scalable deep learning framework that enables nonparametric continuous-time modeling without relying on time discretization or restrictive distributional assumptions. We propose a training objective based on Gauss-Legendre numerical quadrature, which approximates the cumulative hazard with high-order accuracy while facilitating efficient end-to-end training via standard backpropagation.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Gauss-Legendre numerical quadrature approximates the cumulative hazard with high-order accuracy while facilitating efficient end-to-end training via standard backpropagation, without introducing bias that would affect model learning or predictions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"QSurv uses Gauss-Legendre numerical quadrature and time-conditioned low-rank adaptation to enable scalable nonparametric continuous-time survival modeling with theoretical error bounds.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"QSurv approximates cumulative hazards via Gauss-Legendre quadrature to enable scalable nonparametric continuous-time survival modeling in deep networks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d1f0052f30f7a6b9aec6bf93378d38956ea8560fc6257aa38a9a789ed23f299c"},"source":{"id":"2605.16208","kind":"arxiv","version":1},"verdict":{"id":"0c287a98-1272-481a-9082-20a90cf895c5","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T18:32:24.818777Z","strongest_claim":"We introduce QSurv, a scalable deep learning framework that enables nonparametric continuous-time modeling without relying on time discretization or restrictive distributional assumptions. We propose a training objective based on Gauss-Legendre numerical quadrature, which approximates the cumulative hazard with high-order accuracy while facilitating efficient end-to-end training via standard backpropagation.","one_line_summary":"QSurv uses Gauss-Legendre numerical quadrature and time-conditioned low-rank adaptation to enable scalable nonparametric continuous-time survival modeling with theoretical error bounds.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Gauss-Legendre numerical quadrature approximates the cumulative hazard with high-order accuracy while facilitating efficient end-to-end training via standard backpropagation, without introducing bias that would affect model learning or predictions.","pith_extraction_headline":"QSurv approximates cumulative hazards via Gauss-Legendre quadrature to enable scalable nonparametric continuous-time survival modeling in deep networks."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16208/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T19:01:18.882842Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T18:40:54.186056Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"shingle_duplication","ran_at":"2026-05-19T17:49:44.677479Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T17:49:44.167410Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:24.836290Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"external_links","ran_at":"2026-05-19T17:31:34.822203Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T17:22:07.046037Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T16:41:55.396728Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"1de87a0724f6311998c1aa539e7c6fad34c5a949f7ae60d77fd4012c60bfe049"},"references":{"count":48,"sample":[{"doi":"","year":2020,"title":"A. Avati, T. Duan, S. Zhou, K. Jung, N. H. Shah, and A. Y . Ng. Countdown regression: Sharp and calibrated survival predictions. In R. P. Adams and V . Gogate, editors,Proceedings of The 35th Uncertai","work_id":"e12a8b64-849b-4f9e-9b08-fcb5115939b7","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kirby, J. B. Freymann, K. Farahani, and C. Davatzikos. Advancing the cancer genome atlas glioma mri collections with expert segmentation ","work_id":"9b77188c-7557-41ec-ab9a-37488abeb662","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"arXiv preprint arXiv:1811.02629 (2018)","work_id":"1605e84f-e9eb-47fa-8083-67dd5daedf5c","ref_index":3,"cited_arxiv_id":"1811.02629","is_internal_anchor":true},{"doi":"10.1007/978-3-030-47426-3_53","year":2020,"title":"A. Bennis, S. Mouysset, and M. Serrurier. Estimation of conditional mixture weibull distribution with right censored data using neural network for time-to-event analysis. InAdvances in Knowledge Disco","work_id":"c917e70e-871a-4e83-9c08-6ec2300250ab","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1999,"title":"N. E. Breslow and N. Chatterjee. Design and analysis of two-phase studies with binary outcome applied to wilms tumour prognosis.Journal of the Royal Statistical Society: Series C (Applied Statistics),","work_id":"ca9eb33f-a2a0-49f5-83f7-d7bcbaa79737","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":48,"snapshot_sha256":"c1b74b07cb59aeb30d675348c6e1f5b328ad7691e4b7c05fa8f10d35f07527d2","internal_anchors":4},"formal_canon":{"evidence_count":2,"snapshot_sha256":"2b735800ac29e2d1e8ad082daa29e64665a4d874c9c25f76c6ad53627cd99c6d"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"0c287a98-1272-481a-9082-20a90cf895c5"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:01:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dxyl8TcWsp9iCDfH8a5xm9KKCKG1Rb+3Pv83CtR4gGNG0Bj7QUgbzrjfoJ2PGAlGneRqB/OhenlN04IUHeC2BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T16:23:12.312208Z"},"content_sha256":"36b4d1cc5ecf9b8818e8760d32b9a17f26f43398aee77aedf3f6a126a10f0b28","schema_version":"1.0","event_id":"sha256:36b4d1cc5ecf9b8818e8760d32b9a17f26f43398aee77aedf3f6a126a10f0b28"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GEWCVYRCBADWTMA3ELBDH4NO4O/bundle.json","state_url":"https://pith.science/pith/GEWCVYRCBADWTMA3ELBDH4NO4O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GEWCVYRCBADWTMA3ELBDH4NO4O/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-20T16:23:12Z","links":{"resolver":"https://pith.science/pith/GEWCVYRCBADWTMA3ELBDH4NO4O","bundle":"https://pith.science/pith/GEWCVYRCBADWTMA3ELBDH4NO4O/bundle.json","state":"https://pith.science/pith/GEWCVYRCBADWTMA3ELBDH4NO4O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GEWCVYRCBADWTMA3ELBDH4NO4O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:GEWCVYRCBADWTMA3ELBDH4NO4O","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"efe91c570509b25f6a23c92bd2266a303a6c54e04ddb2532ee54f5feee132d42","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-15T17:25:17Z","title_canon_sha256":"857b71cb9d2a3715725ddc2ec41499c3295a8a5afe5305c51c9ec99a2b484604"},"schema_version":"1.0","source":{"id":"2605.16208","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16208","created_at":"2026-05-20T00:01:58Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16208v1","created_at":"2026-05-20T00:01:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16208","created_at":"2026-05-20T00:01:58Z"},{"alias_kind":"pith_short_12","alias_value":"GEWCVYRCBADW","created_at":"2026-05-20T00:01:58Z"},{"alias_kind":"pith_short_16","alias_value":"GEWCVYRCBADWTMA3","created_at":"2026-05-20T00:01:58Z"},{"alias_kind":"pith_short_8","alias_value":"GEWCVYRC","created_at":"2026-05-20T00:01:58Z"}],"graph_snapshots":[{"event_id":"sha256:36b4d1cc5ecf9b8818e8760d32b9a17f26f43398aee77aedf3f6a126a10f0b28","target":"graph","created_at":"2026-05-20T00:01:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We introduce QSurv, a scalable deep learning framework that enables nonparametric continuous-time modeling without relying on time discretization or restrictive distributional assumptions. We propose a training objective based on Gauss-Legendre numerical quadrature, which approximates the cumulative hazard with high-order accuracy while facilitating efficient end-to-end training via standard backpropagation."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"Gauss-Legendre numerical quadrature approximates the cumulative hazard with high-order accuracy while facilitating efficient end-to-end training via standard backpropagation, without introducing bias that would affect model learning or predictions."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"QSurv uses Gauss-Legendre numerical quadrature and time-conditioned low-rank adaptation to enable scalable nonparametric continuous-time survival modeling with theoretical error bounds."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"QSurv approximates cumulative hazards via Gauss-Legendre quadrature to enable scalable nonparametric continuous-time survival modeling in deep networks."}],"snapshot_sha256":"d1f0052f30f7a6b9aec6bf93378d38956ea8560fc6257aa38a9a789ed23f299c"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"2b735800ac29e2d1e8ad082daa29e64665a4d874c9c25f76c6ad53627cd99c6d"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T19:01:18.882842Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T18:40:54.186056Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"shingle_duplication","ran_at":"2026-05-19T17:49:44.677479Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"citation_quote_validity","ran_at":"2026-05-19T17:49:44.167410Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:24.836290Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"external_links","ran_at":"2026-05-19T17:31:34.822203Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T17:22:07.046037Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T16:41:55.396728Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.16208/integrity.json","findings":[],"snapshot_sha256":"1de87a0724f6311998c1aa539e7c6fad34c5a949f7ae60d77fd4012c60bfe049","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Flexible continuous-time survival modeling is critical for capturing complex time-varying hazard dynamics in high-dimensional data; however, training such models remains challenging due to the intractable integral required for likelihood estimation. We introduce QSurv, a scalable deep learning framework that enables nonparametric continuous-time modeling without relying on time discretization or restrictive distributional assumptions. We propose a training objective based on Gauss-Legendre numerical quadrature, which approximates the cumulative hazard with high-order accuracy while facilitatin","authors_text":"Chaeyeon Lee, Hyungrok Do, Sehwan Kim","cross_cats":["cs.LG"],"headline":"QSurv approximates cumulative hazards via Gauss-Legendre quadrature to enable scalable nonparametric continuous-time survival modeling in deep networks.","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-15T17:25:17Z","title":"A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature"},"references":{"count":48,"internal_anchors":4,"resolved_work":48,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"A. Avati, T. Duan, S. Zhou, K. Jung, N. H. Shah, and A. Y . Ng. Countdown regression: Sharp and calibrated survival predictions. In R. P. Adams and V . Gogate, editors,Proceedings of The 35th Uncertai","work_id":"e12a8b64-849b-4f9e-9b08-fcb5115939b7","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kirby, J. B. Freymann, K. Farahani, and C. Davatzikos. Advancing the cancer genome atlas glioma mri collections with expert segmentation ","work_id":"9b77188c-7557-41ec-ab9a-37488abeb662","year":2017},{"cited_arxiv_id":"1811.02629","doi":"","is_internal_anchor":true,"ref_index":3,"title":"arXiv preprint arXiv:1811.02629 (2018)","work_id":"1605e84f-e9eb-47fa-8083-67dd5daedf5c","year":2018},{"cited_arxiv_id":"","doi":"10.1007/978-3-030-47426-3_53","is_internal_anchor":false,"ref_index":4,"title":"A. Bennis, S. Mouysset, and M. Serrurier. Estimation of conditional mixture weibull distribution with right censored data using neural network for time-to-event analysis. InAdvances in Knowledge Disco","work_id":"c917e70e-871a-4e83-9c08-6ec2300250ab","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"N. E. Breslow and N. Chatterjee. Design and analysis of two-phase studies with binary outcome applied to wilms tumour prognosis.Journal of the Royal Statistical Society: Series C (Applied Statistics),","work_id":"ca9eb33f-a2a0-49f5-83f7-d7bcbaa79737","year":1999}],"snapshot_sha256":"c1b74b07cb59aeb30d675348c6e1f5b328ad7691e4b7c05fa8f10d35f07527d2"},"source":{"id":"2605.16208","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T18:32:24.818777Z","id":"0c287a98-1272-481a-9082-20a90cf895c5","model_set":{"reader":"grok-4.3"},"one_line_summary":"QSurv uses Gauss-Legendre numerical quadrature and time-conditioned low-rank adaptation to enable scalable nonparametric continuous-time survival modeling with theoretical error bounds.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"QSurv approximates cumulative hazards via Gauss-Legendre quadrature to enable scalable nonparametric continuous-time survival modeling in deep networks.","strongest_claim":"We introduce QSurv, a scalable deep learning framework that enables nonparametric continuous-time modeling without relying on time discretization or restrictive distributional assumptions. We propose a training objective based on Gauss-Legendre numerical quadrature, which approximates the cumulative hazard with high-order accuracy while facilitating efficient end-to-end training via standard backpropagation.","weakest_assumption":"Gauss-Legendre numerical quadrature approximates the cumulative hazard with high-order accuracy while facilitating efficient end-to-end training via standard backpropagation, without introducing bias that would affect model learning or predictions."}},"verdict_id":"0c287a98-1272-481a-9082-20a90cf895c5"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:04a4662f85a962c0d4be31c7a074aa45bd42afaa9061ed736db62538a095b166","target":"record","created_at":"2026-05-20T00:01:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"efe91c570509b25f6a23c92bd2266a303a6c54e04ddb2532ee54f5feee132d42","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-15T17:25:17Z","title_canon_sha256":"857b71cb9d2a3715725ddc2ec41499c3295a8a5afe5305c51c9ec99a2b484604"},"schema_version":"1.0","source":{"id":"2605.16208","kind":"arxiv","version":1}},"canonical_sha256":"312c2ae222080769b01b22c233f1aee394324dd2873b0f5bc41e468b14a2114d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"312c2ae222080769b01b22c233f1aee394324dd2873b0f5bc41e468b14a2114d","first_computed_at":"2026-05-20T00:01:58.114450Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:58.114450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uB6TWmvNq2KcHwAjUUrH5eEXU8JoTlXn+QLkd4q6qWTwgwJG05F9ZvDTLlI5UNQSjQpLN7iWncV/KsqBrqTFCg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:58.115228Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16208","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:04a4662f85a962c0d4be31c7a074aa45bd42afaa9061ed736db62538a095b166","sha256:36b4d1cc5ecf9b8818e8760d32b9a17f26f43398aee77aedf3f6a126a10f0b28"],"state_sha256":"2b21571e460f47db8882e175ba44a6ee47767461b2fcdb539f203bd04a025126"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BOk+/chhPHqLMvIO5bsQIny7CglAMtRaURMoL+cj8mD+iiVJDlv4/6wBzMhDCYpABvbDwmOI/Mdr/W8Sso7UCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T16:23:12.317287Z","bundle_sha256":"31247c1881d310b9e48ccd22a1786b6dc54bae71e736be564e2d523552537cfa"}}