{"as_of":"2026-08-12T16:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c0cb1a432d72924151c4d7d45848aae330ca63d3ee75dba032bead2be12d2818","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:39:57.059435Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2608.09692/citation-record","integrity":"/paper/2608.09692/integrity","json":"/paper/2608.09692/citation-record.json","paper":"/paper/2608.09692"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.537923Z","title":"Flow matching with gaussian process priors for probabilistic time series forecasting,","venue":null,"work_id":"e0c55455-6201-4884-9298-7fdd495c9518","year":2025},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.944739Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:a01a7f0cee587fbe49c3f9392bfc2489dae397703797e18ce90ffe0ac5a9bc35","observation_id":"2f6e79e5-fe49-47a8-bbc1-c00ddf42a797","resolution":{"observed_at":"2026-08-11T12:39:57.542192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.28867","last_updated":"2026-05-22T07:10:20Z","snapshot_observed_at":"2026-08-04T16:22:45.174745Z","submitted_at":"2026-05-22T07:10:20Z","title":"PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation","version":1},"cited_work":{"arxiv_id":"2605.28867","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.28867","snapshot_observed_at":"2026-08-11T12:39:57.219517Z","title":"PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation","venue":"cs.LG","work_id":"250b232b-a182-4362-9a80-63ade87c2f24","year":2026},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.949159Z"},"links":{"cited_paper":"/paper/2605.28867","citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:95cf84fdc83d2c2d5d472e1043b99d6eeeedddffc3a8fc121cf472e10a853770","observation_id":"cba1a935-15ae-4692-bddc-dbe7ac16b558","resolution":{"observed_at":"2026-08-11T12:39:57.223748Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2511.07968","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.200665Z","title":"Timeflow: Towards stochastic-aware and efficient time series generation via flow matching modeling,","venue":null,"work_id":"d05c03ac-e7ab-4e7b-8480-b30e5403857e","year":2025},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.953433Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:b5b1fe8bc68cfd46092fc19af9b9eafb744cbef195076a2b67954ba581a22fe3","observation_id":"e4ba16f4-c434-4e09-aa27-88ad0a9c04fc","resolution":{"observed_at":"2026-08-11T12:39:57.206600Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.05736","last_updated":"2026-05-11T06:04:49Z","snapshot_observed_at":"2026-08-02T07:26:05.030564Z","submitted_at":"2026-05-07T06:28:18Z","title":"SDFlow: Similarity-Driven Flow Matching for Time Series Generation","version":2},"cited_work":{"arxiv_id":"2605.05736","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.05736","snapshot_observed_at":"2026-08-11T12:39:57.101684Z","title":"SDFlow: Similarity-Driven Flow Matching for Time Series Generation","venue":"cs.AI","work_id":"ee7b843f-e244-4297-baab-a5eb014b09c8","year":2026},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.957453Z"},"links":{"cited_paper":"/paper/2605.05736","citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:2759e1b2fda5291e0cb97ef27961afd5232c2c57f3c958bb48a5d83f86db3719","observation_id":"26ca3af6-ccca-49fc-ba57-ffdb4c1d912a","resolution":{"observed_at":"2026-08-11T12:39:57.107932Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.526089Z","title":"A non-isotropic time series diffusion model with moving average transitions,","venue":null,"work_id":"1cd88db0-ad03-4fec-ab79-7db84feb0125","year":2025},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.961943Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:6602befe1b7bbff3657e739728f81e27f85825215473f2ea405b9ac0ec6ce687","observation_id":"d46a19d0-89f1-4428-94d5-86a8080686d9","resolution":{"observed_at":"2026-08-11T12:39:57.530257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.513574Z","title":"Non-stationary diffusion for probabilistic time series forecasting,","venue":null,"work_id":"37f67d35-37ec-451d-9e4e-e3f0c6ad0fe3","year":2025},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.966045Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:dd95332510fa993da8311662552491c4b505db48cb7756a3089ccee01cb65e55","observation_id":"01d32bfe-f268-4060-8dec-0fe8ea2eae74","resolution":{"observed_at":"2026-08-11T12:39:57.518103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T12:39:56.970251Z","title":"Time-series generative ad- versarial networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.970251Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:b148540bb60da8cf945f025aeb148d677fb3b9207285e901d6a778e6b9dae6b7","observation_id":"d20b195c-f00d-4b4a-b4b6-0aceb7e807dd","resolution":{"observed_at":"2026-08-11T12:39:56.970251Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.495105Z","title":"PSA-GAN: Progressive self attention GANs for synthetic time series,","venue":null,"work_id":"84d6ae39-ff6e-4a56-9341-7a5b408c0034","year":2022},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.974197Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:ab93ad6f8e9b514ee401eee8529588875ae36e2717a065c861330df8edc9d0e5","observation_id":"179be448-c63b-4302-89f5-41fd4c032884","resolution":{"observed_at":"2026-08-11T12:39:57.499152Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.482826Z","title":"Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting,","venue":null,"work_id":"52cb83cd-5454-4cbf-997f-a2cee289fa9c","year":2021},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.977963Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:4c5152721e0f6993acc7a15d0114029a5b6408876e6e08efe17f0ed48639d031","observation_id":"5cf2d845-45a9-4e6e-901e-6fcc822d951c","resolution":{"observed_at":"2026-08-11T12:39:57.487262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.471035Z","title":"Csdi: Conditional score- based diffusion models for probabilistic time series imputation,","venue":null,"work_id":"25c70672-3f4b-4727-813f-6f85d1e59247","year":2021},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.981777Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:eede3f199a56d8b45c8ddee27938ffa9c9dec8697726e1f4cc4b9e65d1035ed0","observation_id":"e50c9929-c683-4d5b-aa02-9752350a75da","resolution":{"observed_at":"2026-08-11T12:39:57.475094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.458664Z","title":"Diffusion-ts: Interpretable diffusion for general time series generation,","venue":null,"work_id":"5aaca6c8-38e6-4937-afd7-6849a3847ec3","year":2024},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.985332Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:4e81680dc605de7ed056e5f0017c2ee947a132b6e7003ad386d1634c55873882","observation_id":"81574bc5-0da1-4a7b-8650-ea9d6c7a1b07","resolution":{"observed_at":"2026-08-11T12:39:57.463124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T12:39:56.989096Z","title":"Flow matching for generative modeling,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.989096Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:fbc4d03e78b3c2bc6a8c24fdc7b5ac9b55887f04d3b950717d16167889fd56cb","observation_id":"d0c55ae5-2cdc-4d03-b261-7a7d1a4ccaa1","resolution":{"observed_at":"2026-08-11T12:39:56.989096Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.433132Z","title":"Forecasting and stock control for intermittent demands,","venue":null,"work_id":"45c6eebc-775f-4b09-8c2f-1c27253a0e76","year":1972},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.992714Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:1f5f3fadb5c813eddd0559002511bec4c973a1fac9e2c369741d117e4356d2b8","observation_id":"cf48ae30-8b10-47b1-852b-df0ff9a4d29e","resolution":{"observed_at":"2026-08-11T12:39:57.437256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.417216Z","title":"The accuracy of intermittent demand estimates,","venue":null,"work_id":"3a9764b4-49e8-4e83-9a51-088adbf3d4f9","year":2005},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:56.997298Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:e90249faa71dc036d77d029a1106696bd77a5b9817df168924bec9f8211c4850","observation_id":"dde37177-6623-4359-a345-4ea9e54225a0","resolution":{"observed_at":"2026-08-11T12:39:57.424295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.403415Z","title":"Intermittent demand forecasts with neural networks,","venue":null,"work_id":"bfaf845a-4567-4071-ae4d-63c67bf5ca83","year":2013},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.001037Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:1468808122c949dd0f314f550d43f7d3f5e1278bd38575b90d56ce05d15e06b5","observation_id":"2b1397d4-9605-475f-8351-bb363c2eda26","resolution":{"observed_at":"2026-08-11T12:39:57.407406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.391831Z","title":"Forecast- ing intermittent and sparse time series: A unified probabilistic framework via deep renewal processes,","venue":null,"work_id":"d21def44-2eae-4b3c-ba3f-c1ff8102d94f","year":2021},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.004811Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:c7d0f0384ab51b6702230b52d884be7b5505ce325eca01c15b2a2229f50a695d","observation_id":"1a26e972-6dbb-42f0-8c97-eb339962dea8","resolution":{"observed_at":"2026-08-11T12:39:57.396020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.380752Z","title":"Another look at measures of forecast accuracy,","venue":null,"work_id":"6f400ec8-d1fd-49f1-aac9-84f3c151ff16","year":2006},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.009007Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:8c971e42627382e0a967fac897445b58d12818de687b0279f72fd09e1d65a814","observation_id":"e061771b-4a61-4568-99d4-b781893b3d0e","resolution":{"observed_at":"2026-08-11T12:39:57.384898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.368997Z","title":"Precipitation as a chain-dependent process,","venue":null,"work_id":"a2253910-e2f6-4ea5-a904-7096f1c69548","year":1962},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.013048Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:14c5bb24e7f269bdc549d977cfee56f9c5b2336dbf3f247b6ae257270697f580","observation_id":"55f80679-a55a-4199-8853-ae54c3383fa8","resolution":{"observed_at":"2026-08-11T12:39:57.372890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.357059Z","title":"Stochastic simulation of daily precipitation, temper- ature, and solar radiation,","venue":null,"work_id":"1117a164-8b7a-457d-a7ab-027cc6407d28","year":1981},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.016691Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:fb19989b8d93f41c86e1ae6cc74861fef0ec4dc0f966999c0c3722f49e1aa6ef","observation_id":"087a5398-cc14-4866-aab2-7aab52a41ed8","resolution":{"observed_at":"2026-08-11T12:39:57.361331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.345593Z","title":"A model fitting analysis of daily rainfall data,","venue":null,"work_id":"6a99fd1d-c56d-4c7e-b20b-0a8969c31f8e","year":1984},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.020447Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:b0f2e3ceb026fa64fd0b6590ab977e2a115591d3b94034eb80a6d02ec355312d","observation_id":"a3c283f5-f1bd-4abd-8bb6-112b8294c903","resolution":{"observed_at":"2026-08-11T12:39:57.349926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.333779Z","title":"Simultaneous stochastic simulation of daily precipitation, temperature and solar radiation at multiple sites in complex terrain,","venue":null,"work_id":"b244bcb5-fcdc-48d3-9c27-d7b4a6141216","year":1999},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.024392Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:50ed43e2f4ade40dab1187f1d7ad803bc0b99e85bb7be4e7048b393d176ee740","observation_id":"d90da96a-5b6a-4913-a948-9cff3be8054e","resolution":{"observed_at":"2026-08-11T12:39:57.337889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.321984Z","title":"Estimation of relationships for limited dependent variables,","venue":null,"work_id":"cde8160f-a3c2-4071-80fe-314cc6ab8146","year":1958},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.028674Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:4751346e636ab2a838433b3e77b09b9b1d9041039e621013a1b7f2d06b97692d","observation_id":"b5961828-f508-4392-a2ea-59ade544fead","resolution":{"observed_at":"2026-08-11T12:39:57.325891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.308859Z","title":"Tobit models: A survey,","venue":null,"work_id":"212e0f70-b89c-4044-b9be-d932351d7d65","year":1984},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.032587Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:1c838dd788fb61050910e3ca03d4abad4c45c3d833e54d27dfa93b911787610e","observation_id":"a741aecf-7840-43fd-b272-3ccfa5e9926b","resolution":{"observed_at":"2026-08-11T12:39:57.314037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.292822Z","title":"Strictly proper scoring rules, prediction, and estimation,","venue":null,"work_id":"f324ff73-0c0e-4f3b-ae27-cfe5e4f6c853","year":2007},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.036337Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:ab87bf4a3cf6c3f94a11939dc5655acc987504013a36ab9319b561ddae669191","observation_id":"47cb85c2-278a-4840-8401-fff05bbb6a74","resolution":{"observed_at":"2026-08-11T12:39:57.297634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.280858Z","title":"Ts2vec: Towards universal representation of time series,","venue":null,"work_id":"0184910b-49bb-4b99-9ee9-dbac6a5eb55f","year":2022},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.040004Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:1dde9b3dea39e51949ef430b4aa4c292605f8f1b97e3cf48ecada236d8f988c2","observation_id":"8d4d6a3b-b03c-4f78-ad63-51026becf4a6","resolution":{"observed_at":"2026-08-11T12:39:57.284829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.268147Z","title":"Variogram-based proper scoring rules for probabilistic forecasts of multivariate quantities,","venue":null,"work_id":"0adc7fee-f4f0-4ef3-8fdd-efe5b4c1526b","year":2015},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.044080Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:22a6383abbd90a1d8be8b9ee858a229321172b19e48e84c069031ee1dd9cd92f","observation_id":"11b17a18-34d0-40ab-b80c-4233aa926791","resolution":{"observed_at":"2026-08-11T12:39:57.272889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.255926Z","title":"The schaake shuffle: A method for reconstructing space–time variability in forecasted precipitation and temperature fields,","venue":null,"work_id":"00df7f39-9400-4bc0-aed4-8f29b214804a","year":2004},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.047935Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:12373fd331a57fdae010147b7553cba000bcdf861de5f86bac9b1b46b5a1545e","observation_id":"68bc2139-9ce5-46f2-a921-792fc1701558","resolution":{"observed_at":"2026-08-11T12:39:57.260293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.244091Z","title":"Uncertainty quantifi- cation in complex simulation models using ensemble copula coupling,","venue":null,"work_id":"88529a1d-88ce-437f-a99c-fc0c5b8c74f8","year":2013},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.051477Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:ef325b4e2f5cd3b643309f74b0d269c417e258d3da1631ed7e3e97e1af5840e9","observation_id":"bc873d57-3cd1-48f2-a7dd-92491e7d5f16","resolution":{"observed_at":"2026-08-11T12:39:57.248426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:39:57.231920Z","title":"A similarity-based implementation of the schaake shuffle,","venue":null,"work_id":"a3c5e41c-402e-4683-8a1d-854b1fb9a693","year":1909},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.055234Z"},"links":{"citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:3e18598ddf77c4dd075a8022c52cde45803037b9b36407b4e9a6fb438dc9ca65","observation_id":"cf11dbbf-1987-4520-ae49-3d07b867b641","resolution":{"observed_at":"2026-08-11T12:39:57.235962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06643","last_updated":"2021-05-14T04:49:58Z","snapshot_observed_at":"2026-08-08T19:22:20.606680Z","submitted_at":"2021-05-14T04:49:58Z","title":"Monash Time Series Forecasting Archive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06643","snapshot_observed_at":"2026-08-11T12:39:57.059435Z","title":"Monash time series forecasting archive,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T12:39:57.059435Z"},"links":{"cited_paper":"/paper/2105.06643","citing_paper":"/paper/2608.09692"},"observation_digest":"sha256:f372b17a2fe08f9aca9ce3b83b4d1ade4fd4b87b6f4def8dc40bb76c5aa2f339","observation_id":"8798a95b-5241-4789-9509-06eb97dc16de","resolution":{"observed_at":"2026-08-11T12:39:57.059435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.09692","last_updated":"2026-08-10T14:57:36Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T15:21:15.904810Z","submitted_at":"2026-08-10T14:57:36Z","title":"Evaluating Generative Time-Series Models on Data with Point Masses"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":3,"verified_fuzzy":24},"total_outbound_references":30},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2608.09692."}