{"as_of":"2026-08-18T15:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:94e504c90aaaf6402f7c278eff3299279b0b070c69778cc8f5dc385bfa30fe70","coverage":[{"denominator":21,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T16:08:29.962630Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2508.18922/citation-record","integrity":"/paper/2508.18922/integrity","json":"/paper/2508.18922/citation-record.json","paper":"/paper/2508.18922"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:08:32.614964Z","title":"Tactis: Transformer-attentional copulas for time series","venue":null,"work_id":"e2e756bb-52ea-45c3-9db7-9991c2377342","year":2022},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:27.290827Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:2c4a184d00683c1ab14cdaef594bc39f64e148f5bf8d33ca23c226a6d4ef956d","observation_id":"2e22fd59-964a-40fb-a47a-a12b7a27730f","resolution":{"observed_at":"2026-08-05T16:08:32.702535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T16:08:32.494116Z","title":"Weight uncertainty in neural networks","venue":null,"work_id":"88593c8a-cc7c-4011-9dd7-730146de7ad2","year":2015},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:27.435062Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:37d6dc22c2152c260c15695932478a90fce847c029b4cb163ca0dc4d25f9fb1b","observation_id":"4db4598f-8e68-4422-a82d-efc9f535644c","resolution":{"observed_at":"2026-08-05T16:08:32.559260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T16:08:27.619679Z","title":"Optimization methods for large-scale machine learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:27.619679Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:82579f16bbaece3e453c586ae78f8fe0a535f05f588772e4e5cdeba8389da147","observation_id":"2442247c-7766-41e7-aac1-597d8e0eaaa8","resolution":{"observed_at":"2026-08-05T16:08:27.619679Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08095","last_updated":"2021-12-07T09:00:04Z","snapshot_observed_at":"2026-08-16T17:41:38.448202Z","submitted_at":"2021-11-15T21:42:14Z","title":"TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.08095","snapshot_observed_at":"2026-08-05T16:08:27.794898Z","title":"Timevae: A variational auto-encoder for multivariate time series generation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:27.794898Z"},"links":{"cited_paper":"/paper/2111.08095","citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:590156be0d223fa3b320ec9f4009604a5a4e5ff82b4b2e281e034f458e8a4a96","observation_id":"c7ccd4e1-bb5c-4ba2-b58f-485544bb657d","resolution":{"observed_at":"2026-08-05T16:08:27.794898Z","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":"2026-08-05T16:08:32.198454Z","title":"Gp-vae: Deep probabilistic time series imputation","venue":null,"work_id":"0d4e8907-c168-4ccf-89af-2de91d4f0044","year":2020},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:27.945304Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:98701488bc12915b98ed38735fd595faacd677c669ccf7d4a7487c6dfa320d0e","observation_id":"513a50c8-75e1-4cb2-822a-fa6404c63625","resolution":{"observed_at":"2026-08-05T16:08:32.304421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T16:08:31.973187Z","title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","venue":null,"work_id":"0d511b2e-11f9-4c2f-b047-6592921cdefb","year":2016},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:28.056308Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:1fdb66d2f3e36ac5728211a4c92a75fe0445d1931e0130bdfb4b931dc6a2d255","observation_id":"5a515994-5825-42d5-beb8-80c33decdce5","resolution":{"observed_at":"2026-08-05T16:08:32.096179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T16:08:28.200943Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:28.200943Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:e8224dfe9aa99481a5097e00c4c0654f364d2d787180e18fc39cc543553b4dee","observation_id":"ed847540-a1c9-49d4-bc27-536138eb0ea0","resolution":{"observed_at":"2026-08-05T16:08:28.200943Z","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":"2026-08-05T16:08:28.295910Z","title":"Long short-term memory","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:28.295910Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:a53ce148d3cf0621e530a4992b28bf5cb7f482924f634f366a5da34bd673b5a0","observation_id":"e1dd5cde-5451-4fc2-9712-661982ca483c","resolution":{"observed_at":"2026-08-05T16:08:28.295910Z","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":"2026-08-05T16:08:28.405127Z","title":"Auto-encoding variational bayes","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:28.405127Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:8c190c4431a04a16c679b55b5dd7b61ef3d40bbac744b51cd30b8e3b8f23ad5d","observation_id":"662ee59b-a2d0-4bce-8ea1-e2d42e48453e","resolution":{"observed_at":"2026-08-05T16:08:28.405127Z","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":"2026-08-05T16:08:28.518727Z","title":"Simple and scalable predictive uncertainty estimation using deep ensembles","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:28.518727Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:34e52938a5ff014a2401c64f6e243d8f486df7717c09c1a972e1891750475919","observation_id":"7dfa6ead-bcde-481b-acb3-a012b1be207f","resolution":{"observed_at":"2026-08-05T16:08:28.518727Z","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":"2026-08-05T16:08:28.672048Z","title":"A time series is worth 64 words: Long-term forecasting with transformers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:28.672048Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:86d5208b8c2f824709a67e57a8d1001fbcce96c65f0bda0c307296512bdedd9a","observation_id":"bc60eb50-1043-4d23-a1b5-5325c1fa8a4e","resolution":{"observed_at":"2026-08-05T16:08:28.672048Z","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":"2026-08-05T16:08:28.820591Z","title":"Estimating the mean and variance of the target probability distribution","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:28.820591Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:89987b7cda5938c1975f396352222a09273472343693085b9fabec0ea7152e9d","observation_id":"da4f0440-30ba-49f3-8585-0a23fdcb5379","resolution":{"observed_at":"2026-08-05T16:08:28.820591Z","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":"2026-08-05T16:08:31.721341Z","title":"Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting","venue":null,"work_id":"2c2cc559-2936-4c31-8f63-daf05fabcf11","year":2021},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:28.973092Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:a80d6ff026898c5dbabdf0cf83200610ebe8f50211a78545392825d54ea18935","observation_id":"97daa45b-3e45-4c70-8ba1-35c75c7c86bd","resolution":{"observed_at":"2026-08-05T16:08:31.792317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T16:08:31.491994Z","title":"Variational inference with normalizing flows","venue":null,"work_id":"892310a6-4cc7-4661-9c71-d74489010dbf","year":2015},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:29.088451Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:10d510c7b0403817eca1d93bacdece4e9e447169d6aed78483614fae4b8a7855","observation_id":"de1b9007-853a-4c8f-8a77-d664eacc370d","resolution":{"observed_at":"2026-08-05T16:08:31.575312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T16:08:31.242874Z","title":"Learning structured output representation using deep conditional generative models","venue":null,"work_id":"bd116bc0-bb32-4c75-8184-0968df642162","year":2015},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:29.227615Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:bc3684688ac0b4911eca1ad95c4a5c9406a52faeb5384eed61b3d3c43c781afe","observation_id":"6fa4cc36-02b9-4a93-a4da-f8b2115eea25","resolution":{"observed_at":"2026-08-05T16:08:31.421927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T16:08:31.018005Z","title":"Csdi: Conditional score-based diffusion models for probabilistic time series imputation","venue":null,"work_id":"034a34f4-0a33-438a-8a2a-8bf9749cf5ed","year":2021},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:29.333387Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:120d853eae81e52af712bc201b6f6484e30a9c5a4f9c1afa1a9d56c624ff8bf5","observation_id":"2b288fde-21ae-49c0-9f47-e970d488a424","resolution":{"observed_at":"2026-08-05T16:08:31.138067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T16:08:30.812515Z","title":"Attention is all you need","venue":null,"work_id":"560decde-5dc2-4375-a870-ed6fa175c1ab","year":2017},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:29.413384Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:bb9659e709cd0ffccf762f93e9a9daf82d4f58ea4e15fc6149b4c5023003b52b","observation_id":"809a18ee-659f-4bfd-b0a6-9898a4e155d2","resolution":{"observed_at":"2026-08-05T16:08:30.926076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T16:08:30.603654Z","title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","venue":null,"work_id":"074b4d2b-6b66-4123-864e-e8d2d2fd350a","year":2021},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:29.589825Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:03b15180b642615c7627cac9de7d88e535e211e214f7b85378661685afaf7e19","observation_id":"c8553af5-1c2a-4113-a7d1-d491b1b3e358","resolution":{"observed_at":"2026-08-05T16:08:30.736769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T16:08:29.710685Z","title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:29.710685Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:764ce9bd768c6da7543fbb1a09a50328564d8df2133e744b60c022a754701c26","observation_id":"63306e45-2a2e-40dc-a8ae-6381517fe39c","resolution":{"observed_at":"2026-08-05T16:08:29.710685Z","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":"2026-08-05T16:08:30.430843Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","venue":null,"work_id":"d7a84507-bbcd-41ba-84df-ed60ffae18ff","year":2021},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:29.803038Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:92e0a8e9625a6e6474894c81a78e25a3b5b05a6879e0c912d6fde82b546628b3","observation_id":"8c2b7b64-ab9c-4c0a-9ede-b9617daba095","resolution":{"observed_at":"2026-08-05T16:08:30.518515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T16:08:30.261665Z","title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","venue":null,"work_id":"07a6aad8-a786-4d74-a770-98b21a5a9cb7","year":2022},"citing_paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-05T16:08:29.962630Z"},"links":{"citing_paper":"/paper/2508.18922"},"observation_digest":"sha256:41965e0d299b113c5215dd07f2e0331bbb077e07185c1c3ab76b064834fb4941","observation_id":"042ee80c-8031-46a1-adb2-f62788d39f8e","resolution":{"observed_at":"2026-08-05T16:08:30.326436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.18922","last_updated":"2025-08-26T10:55:35Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T11:59:39.374005Z","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling"},"reference_resolution":{"displayed":21,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":21},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2508.18922."}