{"as_of":"2026-08-06T01:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b5f57eb3c05ff480e509e6632f66bc659a22a79b19316741eb1a23e40207663c","coverage":[{"denominator":58,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":58,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T13:54:23.735517Z","state":"measured"},{"denominator":60,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":60,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T03:15:42.984242Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-19T14:22:23.976178Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.24762","snapshot_observed_at":"2026-08-03T03:15:42.984242Z","title":"Berghaus, D., Seifner, P., Cvejoski, K., Ojeda, C., and S´anchez, R","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.08733","last_updated":"2026-06-08T13:40:44Z","snapshot_observed_at":"2026-08-03T03:15:40.229658Z","submitted_at":"2026-02-09T14:39:11Z","title":"Foundation Inference Models for Ordinary Differential Equations","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-03T03:15:42.984242Z"},"links":{"cited_paper":"/paper/2509.24762","citing_paper":"/paper/2602.08733"},"observation_digest":"sha256:94803c74a7e06ab7cd5f7323aa37d45017f5bb0ed02e6c18a9e663ba01a24433","observation_id":"b3174438-c801-4bd7-ac9f-3b151690a407","resolution":{"observed_at":"2026-08-03T03:15:42.984242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"cited_work":{"arxiv_id":"2509.24762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.24762","snapshot_observed_at":"2026-06-09T03:08:01.821054Z","title":"In-context learning of temporal point processes with foundation inference models","venue":null,"work_id":"fe883dea-b8f9-4f1b-a8ae-3f30db1558a7","year":2025},"citing_paper":{"arxiv_id":"2605.15488","last_updated":"2026-05-15T00:13:04Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-15T00:13:04Z","title":"SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-19T14:20:00.118059Z"},"links":{"cited_paper":"/paper/2509.24762","citing_paper":"/paper/2605.15488"},"observation_digest":"sha256:62941ad39f594e26d1ace22fe3956546249964610cdc60f4307fcee0be0529f7","observation_id":"69ff1a0d-941e-4908-a3fc-a876e17754df","resolution":{"observed_at":"2026-06-09T03:08:01.821054Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.24762/citation-record","integrity":"/paper/2509.24762/integrity","json":"/paper/2509.24762/citation-record.json","paper":"/paper/2509.24762"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T13:54:12.268251Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:12.268251Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:77d7f3462d3c285729c7544d662f2e14bdc12d0f75aa56faa7c24c29096c8135","observation_id":"ee8b88d7-63df-4327-a4ec-e24c2ba5e0a6","resolution":{"observed_at":"2026-08-04T13:54:12.268251Z","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":"2026-08-04T13:54:12.458901Z","title":"Modeling financial contagion using mutually exciting jump processes","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:12.458901Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:2edb1b200b796b4a2c456c624cf39c1e94e57382d7f9fa8ccc508987d02aa572","observation_id":"f9ce3041-7bea-4955-bdb6-7e83115ee257","resolution":{"observed_at":"2026-08-04T13:54:12.458901Z","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":"2026-08-04T13:54:12.613354Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:12.613354Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:466d07396a995424ab330d2f88239b26b385a8b5ae6c23cf34b6940bb5034746","observation_id":"cc61f404-e118-4039-ae6f-89905fa2ddf0","resolution":{"observed_at":"2026-08-04T13:54:12.613354Z","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":"2026-08-04T13:54:12.832162Z","title":"Neural ordinary differential equations","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:12.832162Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:360ed1af3ec065ab02d2af275ef2f7d1246255d7c3f8d724b27bac0395903d7d","observation_id":"fc9ea536-5b78-413a-a6e3-1e53ca987f6a","resolution":{"observed_at":"2026-08-04T13:54:12.832162Z","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":"2026-08-04T13:54:13.031986Z","title":"Hawkes process modeling of covid-19 with mobility leading indicators and spatial covariates","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:13.031986Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:3ac2d1afd422f2ca478e7ab660157e844c266e111b9153e8aed789272a8c5131","observation_id":"51c3d809-0adb-4949-97f1-61fec925441d","resolution":{"observed_at":"2026-08-04T13:54:13.031986Z","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":"2026-08-04T13:54:13.203752Z","title":"Recurrent point review models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:13.203752Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:b8f2a182b4d07a8a680cd4d60d5ae37002bcc071d68a721b8f37d1698a47526b","observation_id":"a7a56dff-6070-41be-9e34-02ba8953be9c","resolution":{"observed_at":"2026-08-04T13:54:13.203752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14747","last_updated":"2022-03-22T09:14:27Z","snapshot_observed_at":"2026-07-06T12:02:49.515406Z","submitted_at":"2021-10-27T20:17:47Z","title":"Dynamic Review-based Recommenders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14747","snapshot_observed_at":"2026-08-04T13:54:13.362140Z","title":"Sanchez, Christian Bauckhage, and Cesar Ojeda","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:13.362140Z"},"links":{"cited_paper":"/paper/2110.14747","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:3ecbefe83b4f0cd4f0a5cdbd294ad031dd669b716647efdc851879254a1856d8","observation_id":"3d9b0744-4b6b-4b5d-9cbe-165d1b40aee8","resolution":{"observed_at":"2026-08-04T13:54:13.362140Z","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":"10.1007/978-1-4757-2001-3","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Springer series in statistics","work_id":"7cb25ec7-0294-4b02-98d8-f93cca051ee0","year":2007},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:13.536861Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:82f6936f2acd99d38ed97cba54bdc0f964a4165dd03bc1cd90d61a2d723a5398","observation_id":"a9622537-9ee3-4657-9f9d-f6f90b39b97a","resolution":{"observed_at":"2026-08-04T13:58:33.844740Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T13:54:13.703076Z","title":"ODEF ormer: Symbolic regression of dynamical systems with transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:13.703076Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:0c814922e036f31bcc6f653be680fcb742499cccdfc91a74cf56cdaec6b0b92f","observation_id":"cef3c118-c5f8-4a90-a2b1-b018f1bb9b45","resolution":{"observed_at":"2026-08-04T13:54:13.703076Z","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":"2026-08-04T13:54:13.845734Z","title":"Long horizon forecasting with temporal point processes","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:13.845734Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:38eaf02765fb5cad90b715036ce1fa5a8ab78da412ce700bbbdc122566c2049b","observation_id":"29bfb54e-2a8b-4145-9fdc-dee09f44cf61","resolution":{"observed_at":"2026-08-04T13:54:13.845734Z","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":"2026-08-04T13:54:14.027774Z","title":"Recurrent marked temporal point processes: Embedding event history to vector","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:14.027774Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:9b274ebc2dd0048ff0cd220d85ae48fcf8a81096aee30e75e162305d6fcbf659","observation_id":"820938ab-ce88-4636-bad1-e5bc560aa5a5","resolution":{"observed_at":"2026-08-04T13:54:14.027774Z","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":"2026-08-04T13:54:14.160951Z","title":null,"venue":null,"work_id":null,"year":1971},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:14.160951Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:339061b1a7a4fb820404bd3ce66571ef705b6290a47170168245a72961cd6338","observation_id":"377c85ba-b1c7-4546-9280-f34e4f6f1e3f","resolution":{"observed_at":"2026-08-04T13:54:14.160951Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.01848","last_updated":"2023-09-16T09:33:32Z","snapshot_observed_at":"2026-07-06T13:27:49.894090Z","submitted_at":"2022-07-05T07:17:43Z","title":"TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.01848","snapshot_observed_at":"2026-08-04T13:54:14.308532Z","title":"Tabpfn: A transformer that solves small tabular classification problems in a second","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:14.308532Z"},"links":{"cited_paper":"/paper/2207.01848","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:64741ac94789d0e58c0cb9c8f26d2f5891cc2fe87790c38db24be362735eadab","observation_id":"591e43e3-55bc-4e93-bc26-a358150c6bab","resolution":{"observed_at":"2026-08-04T13:54:14.308532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.07430","last_updated":"2026-04-06T11:13:29Z","snapshot_observed_at":"2026-08-05T03:40:09.353331Z","submitted_at":"2024-10-09T20:57:00Z","title":"EventFlow: Forecasting Temporal Point Processes with Flow Matching","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.07430","snapshot_observed_at":"2026-08-04T13:54:14.452162Z","title":"Eventflow: Forecasting temporal point processes with flow matching","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:14.452162Z"},"links":{"cited_paper":"/paper/2410.07430","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:10b997aaee8476098dbd4e268a0d76830bf6b005d20d6d3e223565fae83ce4d3","observation_id":"e7c8fa21-6889-4b7e-82f4-4719f54bd709","resolution":{"observed_at":"2026-08-04T13:54:14.452162Z","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":"2026-08-04T13:54:14.606008Z","title":"Neural controlled differential equations for irregular time series","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:14.606008Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:0d44b9af09ec900ceb60d76f5c53fe3884eaa0797d18ef144d0af162a5fb025e","observation_id":"02b7bc66-9efc-47d2-8504-12c4028b93e3","resolution":{"observed_at":"2026-08-04T13:54:14.606008Z","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":"2026-08-04T13:54:14.787248Z","title":"Poisson processes, volume 3","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:14.787248Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:890de7bce26d4fb21b7bbbfe4913eef9863c00176241b6b5879919e4cad665c3","observation_id":"33ba0021-0100-4f3d-a6c9-d6ad3e73f808","resolution":{"observed_at":"2026-08-04T13:54:14.787248Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1507.02822","last_updated":"2015-07-10T09:31:52Z","snapshot_observed_at":"2026-07-06T04:23:25.001486Z","submitted_at":"2015-07-10T09:31:52Z","title":"Hawkes Processes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1507.02822","snapshot_observed_at":"2026-08-04T13:54:15.041107Z","title":"Laub, Thomas Taimre, and Philip K","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:15.041107Z"},"links":{"cited_paper":"/paper/1507.02822","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:ccea09bee25b6b3b428d58a27df01f1a9e061345ff0ae4e04e3ef7b268c45d0d","observation_id":"22bf25d5-2867-4c98-bfc4-9817aef7c655","resolution":{"observed_at":"2026-08-04T13:54:15.041107Z","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":"2026-08-04T13:54:15.195930Z","title":"SNAP Datasets : Stanford large network dataset collection","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:15.195930Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:65636140f70f45e6a64c366cf70ebffcf17b049570d38b61b693cb08cbf5db43","observation_id":"0be3345f-c85d-4073-8cc1-2f7e9d1ee92a","resolution":{"observed_at":"2026-08-04T13:54:15.195930Z","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":"2026-08-04T13:54:15.336382Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:15.336382Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:fbd9aa369b71834226c08d2cfdab072d3bcce14f97e0466f46323f078dd58397","observation_id":"b0493014-d582-4e68-9bd1-b51c8d530c8c","resolution":{"observed_at":"2026-08-04T13:54:15.336382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.09823","last_updated":"2024-12-24T07:36:33Z","snapshot_observed_at":"2026-07-30T21:10:13.854782Z","submitted_at":"2021-10-19T10:15:00Z","title":"An Empirical Study: Extensive Deep Temporal Point Process","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.09823","snapshot_observed_at":"2026-08-04T13:54:15.575233Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:15.575233Z"},"links":{"cited_paper":"/paper/2110.09823","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:eecc0d01acc558e8a4ce252bb29ac622789e97399f6cf38bd97cf78b79cf93e7","observation_id":"44915e8c-1f40-4e74-b94f-8f1ebf7e90f2","resolution":{"observed_at":"2026-08-04T13:54:15.575233Z","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":"2026-08-04T13:54:15.787093Z","title":"Discovering latent network structure in point process data","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:15.787093Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:9844d86881e5a72069d19dc14808f21014856933f81953592572e208b619d0e7","observation_id":"873f6423-ca96-4e7a-8505-2201f688f541","resolution":{"observed_at":"2026-08-04T13:54:15.787093Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-04T13:54:15.993342Z","title":"Decoupled weight decay regularization, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:15.993342Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:c04bb85ab49674262ee3514eab8be780dc7a66a3be774cf123bf98214898dd83","observation_id":"03bf2b14-c156-4b7c-8368-3a71c8a49921","resolution":{"observed_at":"2026-08-04T13:54:15.993342Z","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":"2026-08-04T13:54:16.169777Z","title":"u dke, Marin Bilo s , Oleksandr Shchur, Marten Lienen, and Stephan G \\","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:16.169777Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:5477fb5383831770b507c5492384ce7c21375c73eb469d7c66607c1fb8d824fa","observation_id":"0a986526-94de-41cc-a08f-9e0964844223","resolution":{"observed_at":"2026-08-04T13:54:16.169777Z","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":"2026-08-04T13:54:16.407405Z","title":"Variational bayesian inference for nonlinear hawkes process with gaussian process self-effects","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:16.407405Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:55912ac121dc765a5f64517f6d8bd2d2e51e11f734e04feb5747a027e0f311e3","observation_id":"e42bda11-0fb1-4e76-8abc-a83de2b9bd9a","resolution":{"observed_at":"2026-08-04T13:54:16.407405Z","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":"2026-08-04T13:54:16.549504Z","title":"Amortized in-context mixed effect transformer models: A zero-shot approach for pharmacokinetics","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:16.549504Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:e5b3dd43373704747d815ea983a215c0f3639c3b609ceb0e71c723bc55d209fb","observation_id":"b9c3c1bc-e67b-490c-95ff-b02437d9352f","resolution":{"observed_at":"2026-08-04T13:54:16.549504Z","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":"2026-08-04T13:54:16.723614Z","title":"The neural hawkes process: A neurally self-modulating multivariate point process","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:16.723614Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:31e3007f84d6d29f99323883c448b70fe7182a2dfd12a9d92dae4ca4d4fa9491","observation_id":"cd026246-6100-48eb-a34f-88d6c32b440d","resolution":{"observed_at":"2026-08-04T13:54:16.723614Z","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":"2026-08-04T13:54:16.894936Z","title":"Imputing missing events in continuous-time event streams","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:16.894936Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:b3e31a078630b65af64c6bb2458c9bd94cca77d8061f67f38b63ba0874b02cbb","observation_id":"0281e695-03ec-47c6-889e-d5b78f7f8efc","resolution":{"observed_at":"2026-08-04T13:54:16.894936Z","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":"2026-08-04T13:54:17.065488Z","title":"Transformer embeddings of irregularly spaced events and their participants","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:17.065488Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:ae932ba310484c9291697a095655a53aada369ac60a80c21d209c714d88a8df4","observation_id":"0fb3944f-b1b3-4637-ac47-3d17473b79b0","resolution":{"observed_at":"2026-08-04T13:54:17.065488Z","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":"2026-08-04T13:54:17.261658Z","title":"Transformers can do bayesian inference","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:17.261658Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:12bca297b153b5f773bf60ad0bcaebe814f7f009b08b6b36921615611646041c","observation_id":"5490c4f4-0dd1-4685-b345-e9dd6cae40cc","resolution":{"observed_at":"2026-08-04T13:54:17.261658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.23947","last_updated":"2025-05-29T18:56:45Z","snapshot_observed_at":"2026-07-06T21:33:17.306281Z","submitted_at":"2025-05-29T18:56:45Z","title":"Position: The Future of Bayesian Prediction Is Prior-Fitted","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.23947","snapshot_observed_at":"2026-08-04T13:54:17.391322Z","title":"u ller, Arik Reuter, Noah Hollmann, David R \\","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:17.391322Z"},"links":{"cited_paper":"/paper/2505.23947","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:a896b94ffcbd49be01c844663e9336252f2ba08bd4610124fbf191c6bc45ebaa","observation_id":"78975e8f-0de7-420a-8fff-1d26254e1ab6","resolution":{"observed_at":"2026-08-04T13:54:17.391322Z","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":"2026-08-04T13:54:17.592956Z","title":"Justifying recommendations using distantly-labeled reviews and fine-grained aspects","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:17.592956Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:cd23f9074f5cfc2c59aee0363cff5d9c63341cd9f0b9ea790fe747928ab8031d","observation_id":"cd88525e-a248-4625-bdb0-53cbc8a33b89","resolution":{"observed_at":"2026-08-04T13:54:17.592956Z","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":"2026-08-04T13:54:17.726846Z","title":null,"venue":null,"work_id":null,"year":1981},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:17.726846Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:df439d657f6b538be91feeb20c11ea8f50065990e64c320700597f8a7dd2396f","observation_id":"db10f8b8-79ce-473d-8cee-d5c8916d8c55","resolution":{"observed_at":"2026-08-04T13:54:17.726846Z","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":"2026-08-04T13:54:17.896381Z","title":"Learning deep generative models for queuing systems","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:17.896381Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:b4331d8d7aa414f81cec6e2c63eca328d83a08e6cb99779fe6c6eca81df9e2de","observation_id":"b2bf15f0-8243-49e7-a430-54f90fb61a77","resolution":{"observed_at":"2026-08-04T13:54:17.896381Z","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":"2026-08-04T13:54:18.445940Z","title":"Decomposable transformer point processes","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:18.445940Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:854d2ec83ba93de898d3d6510d06345548200bde2cb5bd7d83f6e53c5d77d8d8","observation_id":"ed68eec4-1ffd-43be-9c27-85dc1d57445c","resolution":{"observed_at":"2026-08-04T13:54:18.445940Z","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":"2026-08-04T13:54:18.667867Z","title":"Bayesian inference for hawkes processes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:18.667867Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:cf8c70c651fea8da53f440696c9d9c80fe5d4e4ea67ea528740878856c71e912","observation_id":"3c85acf9-21ac-4101-b6e5-a971eb5edc7d","resolution":{"observed_at":"2026-08-04T13:54:18.667867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00221","last_updated":"2018-06-01T07:35:20Z","snapshot_observed_at":"2026-07-06T06:42:25.746932Z","submitted_at":"2018-06-01T07:35:20Z","title":"Lecture Notes: Temporal Point Processes and the Conditional Intensity Function","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.00221","snapshot_observed_at":"2026-08-04T13:54:18.811578Z","title":"Temporal point processes and the conditional intensity function","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:18.811578Z"},"links":{"cited_paper":"/paper/1806.00221","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:6f19c00be352b06e7fd620e8c86663c9ac72e7a510ee40e63b19d9918f930c34","observation_id":"8f65fa41-5b88-4f7c-a734-353317f505be","resolution":{"observed_at":"2026-08-04T13:54:18.811578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.19049","last_updated":"2026-06-08T13:19:57Z","snapshot_observed_at":"2026-07-06T20:43:00.304358Z","submitted_at":"2025-02-26T11:04:02Z","title":"In-Context Learning of Stochastic Differential Equations with Foundation Inference Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.19049","snapshot_observed_at":"2026-08-04T13:54:18.931369Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:18.931369Z"},"links":{"cited_paper":"/paper/2502.19049","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:ed24525b16688c0e04008e411f6c651fc65fde8105ddf7d4d68a688a5ed4f03c","observation_id":"b806bbf5-86f3-41c3-be8c-1fcdb1cb1701","resolution":{"observed_at":"2026-08-04T13:54:18.931369Z","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":"2026-08-04T13:54:19.069423Z","title":"Zero-shot imputation with foundation inference models for dynamical systems","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:19.069423Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:9938ef614577d6e33c6f4f629aa64dc8a613ad5b31b8ce78d56a64a27465a5bc","observation_id":"2093e4b2-2ee6-4897-930d-15a0704d00f0","resolution":{"observed_at":"2026-08-04T13:54:19.069423Z","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":"2026-08-04T13:54:19.205314Z","title":"Intensity-free learning of temporal point processes","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:19.205314Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:0d87336f3f042f145928ece83244b60e54c931798d344a3625c734d47eb55d95","observation_id":"f17cc156-ef89-4f27-9413-73c50b26488b","resolution":{"observed_at":"2026-08-04T13:54:19.205314Z","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":"2026-08-04T13:54:19.444798Z","title":"Multi-time attention networks for irregularly sampled time series","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:19.444798Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:9899afb403cf0e3b1bfad6cc179d3f9336e3e6e382a61461c409cd3fa2425224","observation_id":"afa75b1b-570f-474f-845a-6fd746ccc2ce","resolution":{"observed_at":"2026-08-04T13:54:19.444798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06149","last_updated":"2024-06-10T10:15:32Z","snapshot_observed_at":"2026-07-06T18:28:03.461510Z","submitted_at":"2024-06-10T10:15:32Z","title":"Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06149","snapshot_observed_at":"2026-08-04T13:54:19.552749Z","title":"Decoupled marked temporal point process using neural ordinary differential equations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:19.552749Z"},"links":{"cited_paper":"/paper/2406.06149","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:31a75f634ce8dcd90b468aa2411b388ab60a0a6e4453144eb0d378856be47cd6","observation_id":"8b123b98-3280-4282-9ea2-d1373b01e8ef","resolution":{"observed_at":"2026-08-04T13:54:19.552749Z","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":"2026-08-04T13:54:19.612499Z","title":"A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:19.612499Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:d250832ff3ddbaf179152d6aec8a22833bbc3f730d5da19a4d3867e214526b8f","observation_id":"b55bc961-c1e6-4416-86d6-10003fc8ea4b","resolution":{"observed_at":"2026-08-04T13:54:19.612499Z","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":"2026-08-04T13:54:19.691190Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:19.691190Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:35f5bd7eb14e5574d3f75694b2629cb9416a9dfb85c84a9da3daca44d57fac29","observation_id":"c72716c9-b3c5-4284-a139-3650869eba9e","resolution":{"observed_at":"2026-08-04T13:54:19.691190Z","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":"2026-08-04T13:54:19.797416Z","title":"Learning granger causality from instance-wise self-attentive hawkes processes","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:19.797416Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:cce6116141f62cfde6dd3114035a4b23b86fc54187f485b6ec1d76075f7e9241","observation_id":"fd3c52c1-2828-4a1c-b31f-2f7fd30f8eb9","resolution":{"observed_at":"2026-08-04T13:54:19.797416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.08051","last_updated":"2017-05-23T01:08:38Z","snapshot_observed_at":"2026-07-06T05:43:47.056226Z","submitted_at":"2017-05-23T01:08:38Z","title":"Wasserstein Learning of Deep Generative Point Process Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.08051","snapshot_observed_at":"2026-08-04T13:54:19.864848Z","title":"Wasserstein learning of deep generative point process models","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:19.864848Z"},"links":{"cited_paper":"/paper/1705.08051","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:903e13b7d517e6a80931024d5964d2735ddb536372f45b16f7ab1d8f0718e011","observation_id":"2000322e-86ad-4330-8992-3c199b78c08a","resolution":{"observed_at":"2026-08-04T13:54:19.864848Z","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":"2026-08-04T13:54:20.264811Z","title":"Learning granger causality for hawkes processes","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:20.264811Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:a4763d4915e0fce9cd80396684f773e35a05d7dcd7eaa3df3582b95938610f01","observation_id":"b79e3305-7313-49ed-969a-3431d26e40ed","resolution":{"observed_at":"2026-08-04T13:54:20.264811Z","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":"2026-08-04T13:54:20.464758Z","title":"Zhang, and Hongyuan Mei","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:20.464758Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:afea8d994faa6f2283d57253a5ad1fde1c54fcd76a9ae01b2e47b07224eabc5f","observation_id":"6c021e65-de48-4af9-82ed-c7a726cddde2","resolution":{"observed_at":"2026-08-04T13:54:20.464758Z","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":"2026-08-04T13:54:20.705764Z","title":"Zhang, Qingsong Wen, JUN ZHOU, and Hongyuan Mei","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:20.705764Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:3f1c9a6104c01e692581e4534dbe924d2a6226e146e9ff8137660d71ac37bf55","observation_id":"26f5a32e-e766-421a-a2a1-58ca9c9ee14d","resolution":{"observed_at":"2026-08-04T13:54:20.705764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.00044","last_updated":"2022-05-06T06:03:14Z","snapshot_observed_at":"2026-07-06T12:23:56.415717Z","submitted_at":"2021-12-31T20:00:29Z","title":"Transformer Embeddings of Irregularly Spaced Events and Their Participants","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.00044","snapshot_observed_at":"2026-08-04T13:54:20.825264Z","title":"Transformer embeddings of irregularly spaced events and their participants","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:20.825264Z"},"links":{"cited_paper":"/paper/2201.00044","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:ba0bc4d52cfaec51b92ac32b92ce5ab726fd952183b1d46b680189c7947f6e92","observation_id":"f22e59d8-cc36-45cd-9fc5-2042cc04a009","resolution":{"observed_at":"2026-08-04T13:54:20.825264Z","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":"2026-08-04T13:54:21.215558Z","title":"Interacting diffusion processes for event sequence forecasting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:21.215558Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:b50e9b71e4b1203da5b5b7653a3c49b0dfcb9781de0c7a5fded020887fe42e60","observation_id":"95768f55-194d-42d2-af14-b2f04d40c310","resolution":{"observed_at":"2026-08-04T13:54:21.215558Z","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":"2026-08-04T13:54:21.704743Z","title":"Self-attentive H awkes process","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:21.704743Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:6073a98b6654e5ca546bbb3a7d89878337dce492fd14369c93994456d7d19f14","observation_id":"f3eb3279-88dc-44a4-b702-e5c650d6d458","resolution":{"observed_at":"2026-08-04T13:54:21.704743Z","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":"2026-08-04T13:54:21.934818Z","title":"Seismic: A self-exciting point process model for predicting tweet popularity","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:21.934818Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:8f4e7bc69531429dffc71460c98ba10e00b13a6332735acf8273a8d7891eafb2","observation_id":"a3582367-e25f-4182-8365-379f209bf973","resolution":{"observed_at":"2026-08-04T13:54:21.934818Z","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":"2026-08-04T13:54:22.224826Z","title":"Learning triggering kernels for multi-dimensional hawkes processes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:22.224826Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:ba88609e959fb835eadd1fdbf398f3f5ae290a19a66cad727b0c1d3e8e8a3c90","observation_id":"91349b2c-b776-4bbf-af11-392747972329","resolution":{"observed_at":"2026-08-04T13:54:22.224826Z","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":"2026-08-04T13:54:22.806760Z","title":"Learning tree-based deep model for recommender systems","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:22.806760Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:51d2dede8861e2abce93dd2ab5eb611d3f5012fbbbcb02c548fefc9b8ce4ea7d","observation_id":"c0ee9ab9-1744-4052-852c-282bf8fa1640","resolution":{"observed_at":"2026-08-04T13:54:22.806760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.09291","last_updated":"2021-02-21T01:59:26Z","snapshot_observed_at":"2026-08-05T04:21:14.985565Z","submitted_at":"2020-02-21T13:48:13Z","title":"Transformer Hawkes Process","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.09291","snapshot_observed_at":"2026-08-04T13:54:23.274745Z","title":"Transformer hawkes process","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:23.274745Z"},"links":{"cited_paper":"/paper/2002.09291","citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:5f072ce14ab0cfa4b2f246da5d2917e8ff0f6c314633ab82af17f49606cd82ba","observation_id":"d37090fb-76bb-40b6-9c29-8d48c078ced5","resolution":{"observed_at":"2026-08-04T13:54:23.274745Z","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":"2026-08-04T13:54:23.364744Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:23.364744Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:778be7915326072239e29583a2859da9f128f9ac5ea8ce3111ef2676904e76ef","observation_id":"92ccc74f-fe1a-4d78-b5fa-7df026a38693","resolution":{"observed_at":"2026-08-04T13:54:23.364744Z","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":"2026-08-04T13:54:23.494792Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:23.494792Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:1426f7fb0de58e7f73ab1b2a48e7e32a3f89da1bdfa0a6b1e0819ae17055bbe0","observation_id":"46b494c5-a903-4911-92ca-2fe7de471eb0","resolution":{"observed_at":"2026-08-04T13:54:23.494792Z","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":"2026-08-04T13:54:23.735517Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","version":3},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:23.735517Z"},"links":{"citing_paper":"/paper/2509.24762"},"observation_digest":"sha256:b19877cbc75aea204d7c051697726d460328c572c3116379e3fc19074738883f","observation_id":"6ec96ebb-fff6-4fec-8e14-acc9f9f30aee","resolution":{"observed_at":"2026-08-04T13:54:23.735517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.24762","last_updated":"2026-06-08T13:33:54Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T13:52:14.127012Z","submitted_at":"2025-09-29T13:28:06Z","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models"},"reference_resolution":{"displayed":58,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":57,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":58},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 2 inbound Pith citation observations for arXiv:2509.24762."}