{"as_of":"2026-08-09T17:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7efeb08614ce76c669f97071f8d17fc6e43b1781a12e47dddb86dd35c0d25bd6","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":12,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T12:19:07.985835Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-09T16:06:20.088904Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2404.15772","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.15772","snapshot_observed_at":"2026-07-09T16:06:20.088904Z","title":"Bi-mamba4ts: Bidirectional mamba for time series forecasting","venue":"cs.LG","work_id":"d1b8c802-2849-4e1f-b76c-4a129d367ca6","year":2024},"citing_paper":{"arxiv_id":"2408.01129","last_updated":"2026-04-06T02:10:55Z","snapshot_observed_at":"2026-07-30T06:31:55.204130Z","submitted_at":"2024-08-02T09:18:41Z","title":"A Survey of Mamba","version":8},"reference_index":111,"source":"pdf_text","source_observed_at":"2026-05-23T22:09:19.917854Z"},"links":{"cited_paper":"/paper/2404.15772","citing_paper":"/paper/2408.01129"},"observation_digest":"sha256:cef517309e81e253fc2f359e9396caf4c83a691b9817c444c64e59702599666d","observation_id":"939db516-01ec-4e5b-be2e-3b9dc7067939","resolution":{"observed_at":"2026-05-23T22:13:30.597360Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.15772","snapshot_observed_at":"2026-08-08T12:19:07.985835Z","title":"arXiv preprint arXiv:2404.15772","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.07575","last_updated":"2025-02-21T04:10:54Z","snapshot_observed_at":"2026-08-08T12:13:15.265253Z","submitted_at":"2025-02-11T14:17:29Z","title":"Towards Efficient and Multifaceted Computer-assisted Pronunciation Training Leveraging Hierarchical Selective State Space Model and Decoupled Cross-entropy Loss","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T12:19:07.985835Z"},"links":{"cited_paper":"/paper/2404.15772","citing_paper":"/paper/2502.07575"},"observation_digest":"sha256:04a8736fcc3713bbbdeef41db45a29ef9cd5038551f6d1f85cc2461b93ee6900","observation_id":"d03425cd-6d26-4c28-b4e7-88107123665a","resolution":{"observed_at":"2026-08-08T12:19:07.985835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.15772","snapshot_observed_at":"2026-08-07T13:54:29.881362Z","title":"Bi-mamba+: Bidirectional mamba for time series forecasting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20774","last_updated":"2025-05-27T06:24:21Z","snapshot_observed_at":"2026-08-07T13:44:56.412690Z","submitted_at":"2025-05-27T06:24:21Z","title":"TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T13:54:29.881362Z"},"links":{"cited_paper":"/paper/2404.15772","citing_paper":"/paper/2505.20774"},"observation_digest":"sha256:0d5ba6d7c2dc5aba556d451f9782238017b0d22c05636abcdbac3348295abf71","observation_id":"4c70a78f-5b40-47f6-bdde-9c09f33d4f9d","resolution":{"observed_at":"2026-08-07T13:54:29.881362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.15772","snapshot_observed_at":"2026-08-07T12:12:14.863025Z","title":"Bi-mamba+: Bidirectional mamba for time series forecasting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00432","last_updated":"2025-05-31T07:24:24Z","snapshot_observed_at":"2026-08-09T00:10:07.318470Z","submitted_at":"2025-05-31T07:24:24Z","title":"Channel Normalization for Time Series Channel Identification","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T12:12:14.863025Z"},"links":{"cited_paper":"/paper/2404.15772","citing_paper":"/paper/2506.00432"},"observation_digest":"sha256:744cb44cfd32737fe1e359a0a4f18e4d11f64295e40e5290915d65c55e84f483","observation_id":"94357a63-854f-4e9b-8e30-c477ed9e834e","resolution":{"observed_at":"2026-08-07T12:12:14.863025Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.15772","snapshot_observed_at":"2026-08-06T21:27:49.059855Z","title":"Bi-mamba4ts: Bidirectional mamba for time series forecasting","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.00191","last_updated":"2025-06-30T19:01:00Z","snapshot_observed_at":"2026-08-08T10:21:05.038210Z","submitted_at":"2025-06-30T19:01:00Z","title":"Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:27:49.059855Z"},"links":{"cited_paper":"/paper/2404.15772","citing_paper":"/paper/2507.00191"},"observation_digest":"sha256:ce937b36cbe3fad1ed5ccd8b8fa6f6210e9bf3b4e65bfcee33f29fec425449e7","observation_id":"5ad997f3-6ae3-4738-b61a-f591016d5712","resolution":{"observed_at":"2026-08-06T21:27:49.059855Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.15772","snapshot_observed_at":"2026-08-06T19:52:02.673589Z","title":"Bi-mamba+: Bidirectional mamba for time series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.04381","last_updated":"2025-07-06T12:58:52Z","snapshot_observed_at":"2026-08-07T19:24:20.635724Z","submitted_at":"2025-07-06T12:58:52Z","title":"DC-Mamber: A Dual Channel Prediction Model based on Mamba and Linear Transformer for Multivariate Time Series Forecasting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:52:02.673589Z"},"links":{"cited_paper":"/paper/2404.15772","citing_paper":"/paper/2507.04381"},"observation_digest":"sha256:9faa5b8d1a0960878ab8804777aca3db81ab3ab1714d0986850365510abda75b","observation_id":"513112ce-2d6b-4757-86d7-9d4244cd4db1","resolution":{"observed_at":"2026-08-06T19:52:02.673589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.15772","snapshot_observed_at":"2026-08-06T16:42:58.279851Z","title":"Bi-mamba4ts: Bidirectional mamba for time series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12803","last_updated":"2025-07-17T05:39:15Z","snapshot_observed_at":"2026-08-08T14:47:59.438288Z","submitted_at":"2025-07-17T05:39:15Z","title":"FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Prediction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T16:42:58.279851Z"},"links":{"cited_paper":"/paper/2404.15772","citing_paper":"/paper/2507.12803"},"observation_digest":"sha256:bbdc9554cea6715015cbb7119679415006ef2635132a71c0f38e5a98ff6676b8","observation_id":"6fd47a70-ad66-4c3e-817f-dde1a6c70b6b","resolution":{"observed_at":"2026-08-06T16:42:58.279851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2404.15772","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.15772","snapshot_observed_at":"2026-07-09T16:06:20.088904Z","title":"Bi-mamba4ts: Bidirectional mamba for time series forecasting","venue":"cs.LG","work_id":"d1b8c802-2849-4e1f-b76c-4a129d367ca6","year":2024},"citing_paper":{"arxiv_id":"2604.16325","last_updated":"2026-06-27T10:07:07Z","snapshot_observed_at":"2026-08-02T20:30:57.725665Z","submitted_at":"2026-03-06T05:00:28Z","title":"UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-15T14:50:08.468635Z"},"links":{"cited_paper":"/paper/2404.15772","citing_paper":"/paper/2604.16325"},"observation_digest":"sha256:9e2b3b752562a8fd958dbd67384a765ef1823fa53aa20f37cb56aed2fb28c382","observation_id":"77b50083-706f-4b58-ac36-b7bbb0417e1e","resolution":{"observed_at":"2026-05-15T14:51:08.466336Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2404.15772","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.15772","snapshot_observed_at":"2026-07-09T16:06:20.088904Z","title":"Bi-mamba4ts: Bidirectional mamba for time series forecasting","venue":"cs.LG","work_id":"d1b8c802-2849-4e1f-b76c-4a129d367ca6","year":2024},"citing_paper":{"arxiv_id":"2604.23474","last_updated":"2026-04-25T23:54:41Z","snapshot_observed_at":"2026-07-06T23:09:43.659053Z","submitted_at":"2026-04-25T23:54:41Z","title":"GeoCert: Certified Geometric AI for Reliable Forecasting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-08T08:15:10.739078Z"},"links":{"cited_paper":"/paper/2404.15772","citing_paper":"/paper/2604.23474"},"observation_digest":"sha256:0f4fbdd762d871aa39db77b7534af04b4e948ad605d372afdd2b1aca15d8abd3","observation_id":"ffd560d3-7c9e-44ee-8e6f-8c5ef3018614","resolution":{"observed_at":"2026-05-11T20:41:13.838658Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2404.15772","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.15772","snapshot_observed_at":"2026-07-09T16:06:20.088904Z","title":"Bi-mamba4ts: Bidirectional mamba for time series forecasting","venue":"cs.LG","work_id":"d1b8c802-2849-4e1f-b76c-4a129d367ca6","year":2024},"citing_paper":{"arxiv_id":"2606.09917","last_updated":"2026-06-06T17:04:57Z","snapshot_observed_at":"2026-08-08T00:43:56.145518Z","submitted_at":"2026-06-06T17:04:57Z","title":"SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-27T20:01:38.433950Z"},"links":{"cited_paper":"/paper/2404.15772","citing_paper":"/paper/2606.09917"},"observation_digest":"sha256:e828c202576ed07a2412cfacdebe4e78b3aa56fdef5dd9aaed4ab2b1ca3e7535","observation_id":"4568a5a6-e89f-4a3b-a2f6-5272a9cf5a34","resolution":{"observed_at":"2026-07-02T20:57:23.403784Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2404.15772","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.15772","snapshot_observed_at":"2026-07-09T16:06:20.088904Z","title":"Bi-mamba4ts: Bidirectional mamba for time series forecasting","venue":"cs.LG","work_id":"d1b8c802-2849-4e1f-b76c-4a129d367ca6","year":2024},"citing_paper":{"arxiv_id":"2607.07258","last_updated":"2026-07-08T10:46:02Z","snapshot_observed_at":"2026-08-06T11:07:24.943787Z","submitted_at":"2026-07-08T10:46:02Z","title":"FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-09T16:05:59.022664Z"},"links":{"cited_paper":"/paper/2404.15772","citing_paper":"/paper/2607.07258"},"observation_digest":"sha256:b854f2b5961e2227de717b0cc78435a22bbb9d682c4d5364cae4f5421d10ce2b","observation_id":"9bfe8696-c129-47dd-97ff-d65fc71cc25e","resolution":{"observed_at":"2026-07-09T16:06:20.090347Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.15772","snapshot_observed_at":"2026-08-01T19:44:33.238331Z","title":"arXiv preprint arXiv:2404.15772 , year=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16882","last_updated":"2026-07-18T16:51:40Z","snapshot_observed_at":"2026-08-08T03:06:27.280388Z","submitted_at":"2026-07-18T16:51:40Z","title":"HyBDM: Multi-Scale Hybrid Experts for Time Series Forecasting with Bidirectional Dependency Modeling","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-01T19:44:33.238331Z"},"links":{"cited_paper":"/paper/2404.15772","citing_paper":"/paper/2607.16882"},"observation_digest":"sha256:343da638de0b76b818e130dbc16a41e3f49867cd02e157e11304e713e5832e6f","observation_id":"d46eeb55-5631-430a-b681-18a51ab79c0a","resolution":{"observed_at":"2026-08-01T19:44:33.238331Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2404.15772/citation-record","integrity":"/paper/2404.15772/integrity","json":"/paper/2404.15772/citation-record.json","paper":"/paper/2404.15772"},"outbound":[],"paper":{"arxiv_id":"2404.15772","last_updated":"2024-06-27T03:31:25Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T08:19:23.367190Z","submitted_at":"2024-04-24T09:45:48Z","title":"Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2404.15772."}