{"as_of":"2026-08-09T06:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c7815b71d5cd56658b708c1c4e196b38f4b36f521c271d490c2e610c6ee02b37","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T21:54:20.998507Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2602.18885/citation-record","integrity":"/paper/2602.18885/integrity","json":"/paper/2602.18885/citation-record.json","paper":"/paper/2602.18885"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:17.078105Z","title":"K., Gautam, D., Bevilacqua, B., Imran, A., Shah, R., Naghipourfar, M., Teyssier, N., Ilango, R., Nagaraj, S., Dong, M., et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:17.078105Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:80c91fb3e486ec0f4aa14d318046fda588252e6b948daf3b154ff03bc29f6080","observation_id":"db9e0f1b-bd45-43d3-a2e9-b2dda92b1010","resolution":{"observed_at":"2026-08-02T21:54:17.078105Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:17.503022Z","title":"M., Zhou, Y ., Crepaldi, L., Usluer, S., Dunham, A., Braunger, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:17.503022Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:811d19581f8b7f477c69d1dd7fd2126d5ff49385ba5dadfc159fe5f3da96384f","observation_id":"b380bbd8-7251-4074-9b04-e1001f13e83c","resolution":{"observed_at":"2026-08-02T21:54:17.503022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:17.908426Z","title":"M., Torkar, M., Li, D., and Karaletsos, T","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:17.908426Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:8cc6f941d4c133e40e494849da3c3637bb8824e6769dc085a0fcb7823ae9b179","observation_id":"6525cdd9-4033-4fdf-ac45-a4b712f70054","resolution":{"observed_at":"2026-08-02T21:54:17.908426Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:18.571101Z","title":"D., Simmonds, S","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:18.571101Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:198df0d6e25e226923119a0c04f59062331dd33ea593f9b031c19bb87238c1e9","observation_id":"d2500ee2-1471-4495-81cc-19694460d1cc","resolution":{"observed_at":"2026-08-02T21:54:18.571101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:18.807243Z","title":"S., Quake, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:18.807243Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:cc6fb51f1beee84f0c275ee3d6e42b3326046f79df5954bbb27ce1d20babf1d5","observation_id":"089a7214-378e-4e5a-b817-f5055eb3932f","resolution":{"observed_at":"2026-08-02T21:54:18.807243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:19.123493Z","title":"E., Huang, Q., Fang, T., et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:19.123493Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:d9a206fa3c3364d057d86e6f69d482dc6b46cce75d8bd0bd1150667408b00021","observation_id":"b3cd1f4c-d580-42cf-b3e0-10e19355cb8e","resolution":{"observed_at":"2026-08-02T21:54:19.123493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:19.495157Z","title":"P., Ektefaie, Y ., et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:19.495157Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:bcfc1af027a0be36ed00ec613d5b8c6a547793a0226e556a3464f392d355ad66","observation_id":"813bceda-ca1d-41ae-aaf7-9ab93fd93eb9","resolution":{"observed_at":"2026-08-02T21:54:19.495157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:19.892495Z","title":"Data Statistics A.1","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:19.892495Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:d0d68c0f384500644409afdbdf2162ed9f35d0a593db7bcc7d5963519581c733","observation_id":"3b282257-a0ee-4235-b8e5-ef1588c188ca","resolution":{"observed_at":"2026-08-02T21:54:19.892495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:20.121292Z","title":"Gene expression profiles are measured under single-gene perturbations with matched control cells","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:20.121292Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:fb3681ea38a56cac35e8c02a5f4f147c916269056b1b86751347bd9c8334ff45","observation_id":"e13fcd7e-79ab-4287-ac35-f561bfa8bb18","resolution":{"observed_at":"2026-08-02T21:54:20.121292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:20.274750Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:20.274750Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:0d966861bbbf9bdcd5ca7f69473112c6d88412bb001d3be2a845fe3131b18289","observation_id":"fbb905c5-697e-42c0-ae7d-0b0f67b219c1","resolution":{"observed_at":"2026-08-02T21:54:20.274750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:20.547903Z","title":"The embeddings are available in two variants: Ada (1,536-dim) and Model 3 (3,072-dim), covering 93,800 and 133,736 genes respectively","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:20.547903Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:968979249c65f3035d96905f7e2a19d56edca21ba9bf7f9c3d7b35c848fd5c32","observation_id":"c7b469d8-98a4-4cdb-b79f-27f516c21f17","resolution":{"observed_at":"2026-08-02T21:54:20.547903Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:20.836444Z","title":"While effective at capturing global expression shifts, these methods are not explicitly designed to recover sparse gene-level effects","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:20.836444Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:d1995ead0754784f552aee677dc203accb852196112f4e7c0330b7262b407acc","observation_id":"f2c8f8ba-9129-46b9-b78c-b946a2b29367","resolution":{"observed_at":"2026-08-02T21:54:20.836444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:20.998507Z","title":"Recent studies further explore the integration oftextual and semantic biological knowledge","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:20.998507Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:9e3a1dbc3ed3a89423e53b028c79f594a70b879f7d105bf8dc892daba1412495","observation_id":"efb8a6c4-6bb9-4bfd-992b-7294c2e21a9e","resolution":{"observed_at":"2026-08-02T21:54:20.998507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:20.456318Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":228,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:20.456318Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:4d9b5539403fa1a2dcc5cff7a9fc0e07c4a376eae9a4c4ec9c3cfcb47eba59ec","observation_id":"62aea791-efe6-49f5-8df7-3e71748df6ea","resolution":{"observed_at":"2026-08-02T21:54:20.456318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:17.690200Z","title":"scgenept: Is lan- guage all you need for modeling single-cell perturbations? bioRxiv, pp","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":1992,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:17.690200Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:d80f23863d028736f5fcb1d409de86c3bfa3920ee1e3eb26584103b1459895a2","observation_id":"ee9c6172-d5af-4544-9a81-4bf4eaf48bd5","resolution":{"observed_at":"2026-08-02T21:54:17.690200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:19.728929Z","title":"M., Nassar, M., Osi´nski, B., Eksi, R., Yan, Z., Stark, R., Zhang, K., and Grae- pel, T","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:19.728929Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:0f1e804d51e971467a3ecbc507aa80ded53f8e11ed170a7978056baef059b656","observation_id":"c64f80d1-855f-4175-8122-da5b17adfaa8","resolution":{"observed_at":"2026-08-02T21:54:19.728929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:18.945360Z","title":"L., Fang, T., Doncheva, N","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:18.945360Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:5ac87f657c6d71f335e9687624610e22f9cb0d25e153ddde2cf9b68ddc0ca08a","observation_id":"35c7049f-c36b-4ada-85eb-5a4f35ed769e","resolution":{"observed_at":"2026-08-02T21:54:18.945360Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:18.240920Z","title":"A systematic comparison 9 Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction of single-cell perturbation response prediction models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:18.240920Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:e1641f673f3a7edb27281513a9f5c56386874cca82fedd53664de76325540e85","observation_id":"e0c167eb-d1f3-44be-af26-8dfc010d49cb","resolution":{"observed_at":"2026-08-02T21:54:18.240920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14919","last_updated":"2025-05-20T21:13:23Z","snapshot_observed_at":"2026-08-09T04:38:06.007698Z","submitted_at":"2025-05-20T21:13:23Z","title":"TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14919","snapshot_observed_at":"2026-08-02T21:54:19.333431Z","title":"T., Bendidi, I., Russell, C., Hodgson, L., Mesbahi, Y","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:19.333431Z"},"links":{"cited_paper":"/paper/2505.14919","citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:21544229fba42a4e45720a626d5019bb4999eb6ffab651289b90a3393e1dd80b","observation_id":"fe9852c4-9d7e-426f-ac63-40d9848dc454","resolution":{"observed_at":"2026-08-02T21:54:19.333431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:20.698533Z","title":"Subsequent methods, including (Lotfollahi et al., 2023; Adduri et al., 2025), extend this paradigm by conditioning latent variables on perturbation identities and cellular contexts","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:20.698533Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:5458638e7e0a533f21d8c9886771ff21121b3c288a9db18900c45fada6bf153a","observation_id":"183f4cc8-6620-4ae4-b082-61c663d525f5","resolution":{"observed_at":"2026-08-02T21:54:20.698533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.22641","last_updated":"2025-06-27T21:12:46Z","snapshot_observed_at":"2026-08-09T04:37:58.126116Z","submitted_at":"2025-06-27T21:12:46Z","title":"Diversity by Design: Addressing Mode Collapse Improves scRNA-seq Perturbation Modeling on Well-Calibrated Metrics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.22641","snapshot_observed_at":"2026-08-02T21:54:18.379945Z","title":"M., Miller, H","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:18.379945Z"},"links":{"cited_paper":"/paper/2506.22641","citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:f0635e37582807d837e96c7b23fb09290381d429efa19f5f22db91d325990e80","observation_id":"df93421d-cb33-4b0e-a864-05593006b92c","resolution":{"observed_at":"2026-08-02T21:54:18.379945Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T21:54:17.297789Z","title":"and Zou, J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:17.297789Z"},"links":{"citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:5fc0fe6c2aee69942b46da828894719302c905efcfa3793965d9b1790add9b26","observation_id":"04c86f23-5495-4eed-ae8a-4cc2657b2b70","resolution":{"observed_at":"2026-08-02T21:54:17.297789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01144","last_updated":"2017-08-05T22:45:19Z","snapshot_observed_at":"2026-08-01T18:34:23.156273Z","submitted_at":"2016-11-03T19:48:08Z","title":"Categorical Reparameterization with Gumbel-Softmax","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.01144","snapshot_observed_at":"2026-08-02T21:54:18.085167Z","title":"Categorical repa- rameterization with Gumbel-Softmax.arXiv preprint arXiv:1611.01144,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-02T21:54:18.085167Z"},"links":{"cited_paper":"/paper/1611.01144","citing_paper":"/paper/2602.18885"},"observation_digest":"sha256:a8a01c5c50040f7bf83c8449b3570ddfbe05f73e1de8c83feaa7020fcda2a89c","observation_id":"a2250ec6-c850-48b9-af65-4d47315e243e","resolution":{"observed_at":"2026-08-02T21:54:18.085167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2602.18885","last_updated":"2026-07-05T08:45:51Z","latest_version":2,"primary_category":"cs.CE","snapshot_observed_at":"2026-08-06T09:29:38.543597Z","submitted_at":"2026-02-21T16:15:40Z","title":"Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":23},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2602.18885."}